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Coredump with DumpLayerConnections
by Xavier Otazu 13 Mar '23

13 Mar '23

Hello, For the following code, I receive a coredump (it throws and assertion from grid_layer.h) (output at the end). When not connecting 'input' to 'layer_1', everything works fine. It looks like it does not like to connect a layer to more than one target layer. Thanks a lot in advance! import nest pos = nest.spatial.grid(shape = [3,3] ) input = nest.Create('iaf_psc_alpha', positions=pos) layer_0 = nest.Create('iaf_psc_alpha', positions=pos) layer_1 = nest.Create('iaf_psc_alpha', positions=pos) conn_neur = {'rule':'pairwise_bernoulli', 'use_on_source': True, 'mask': {'grid':{'shape':[3,3]}} } nest.Connect(input, layer_0, conn_neur) nest.Connect(input, layer_1, conn_neur) nest.DumpLayerConnections(input, layer_0, 'static_synapse', 'conn.txt') The output message is: python3: /home/neurobit/src/nest_3.3_src/nestkernel/grid_layer.h:223: nest::Position<D> nest::GridLayer<D>::lid_to_position(nest::index) const [with int D = 2; nest::index = long unsigned int]: Assertion `lid < dims_[ 0 ]' failed. [dcccluster:326996] *** Process received signal *** [dcccluster:326996] Signal: Aborted (6) [dcccluster:326996] Signal code: (-6) [dcccluster:326996] [ 0] /lib64/libpthread.so.0(+0x12c20)[0x7fb317ec0c20] [dcccluster:326996] [ 1] /lib64/libc.so.6(gsignal+0x10f)[0x7fb31739637f] [dcccluster:326996] [ 2] /lib64/libc.so.6(abort+0x127)[0x7fb317380db5] [dcccluster:326996] [ 3] /lib64/libc.so.6(+0x21c89)[0x7fb317380c89] [dcccluster:326996] [ 4] /lib64/libc.so.6(+0x2fa76)[0x7fb31738ea76] [dcccluster:326996] [ 5] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(+0x1d7783)[0x7fb2fcbf7783] [dcccluster:326996] [ 6] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(_ZNK4nest9GridLayerILi2EE12get_positionEm+0xa)[0x7fb2fcbf779a] [dcccluster:326996] [ 7] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(_ZN4nest5LayerILi2EE16dump_connectionsERSoSt10shared_ptrINS_14NodeCollectionEES3_INS_13AbstractLayerEERK5Token+0x525)[0x7fb2fcbfca75] [dcccluster:326996] [ 8] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(_ZN4nest22dump_layer_connectionsERK5TokenSt10shared_ptrINS_14NodeCollectionEES5_R12lockPTRDatumISoXadL_ZN14SLIInterpreter11OstreamtypeEEEE+0x35c)[0x7fb2fcc1530c] [dcccluster:326996] [ 9] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libnestkernel.so(_ZNK4nest10NestModule37DumpLayerConnections_os_g_g_lFunction7executeEP14SLIInterpreter+0xfc)[0x7fb2fcb491dc] [dcccluster:326996] [10] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libsli.so(_ZN13FunctionDatum7executeEP14SLIInterpreter+0x2b)[0x7fb2f7c0975b] [dcccluster:326996] [11] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libsli.so(_ZN14SLIInterpreter8execute_Em+0x1b2)[0x7fb2f7c06b32] [dcccluster:326996] [12] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/../../../nest/libsli.so(_ZN14SLIInterpreter7executeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x1ea)[0x7fb2f7c06e7a] [dcccluster:326996] [13] /home/neurobit/local/nest_3.3/lib64/python3.8/site-packages/nest/pynestkernel.so(+0x38669)[0x7fb2ff200669] [dcccluster:326996] [14] /lib64/libpython3.8.so.1.0(+0x173db8)[0x7fb31846adb8] [dcccluster:326996] [15] /lib64/libpython3.8.so.1.0(_PyEval_EvalFrameDefault+0x976)[0x7fb3184d8406] [dcccluster:326996] [16] /lib64/libpython3.8.so.1.0(_PyFunction_Vectorcall+0x1bf)[0x7fb3184967af] [dcccluster:326996] [17] /lib64/libpython3.8.so.1.0(_PyEval_EvalFrameDefault+0x5da)[0x7fb3184d806a] [dcccluster:326996] [18] /lib64/libpython3.8.so.1.0(_PyEval_EvalCodeWithName+0x3e7)[0x7fb3184953c7] [dcccluster:326996] [19] /lib64/libpython3.8.so.1.0(_PyFunction_Vectorcall+0x422)[0x7fb318496a12] [dcccluster:326996] [20] /lib64/libpython3.8.so.1.0(_PyEval_EvalFrameDefault+0x5da)[0x7fb3184d806a] [dcccluster:326996] [21] /lib64/libpython3.8.so.1.0(_PyFunction_Vectorcall+0x1bf)[0x7fb3184967af] [dcccluster:326996] [22] /lib64/libpython3.8.so.1.0(_PyEval_EvalFrameDefault+0x54d9)[0x7fb3184dcf69] [dcccluster:326996] [23] /lib64/libpython3.8.so.1.0(_PyEval_EvalCodeWithName+0x3e7)[0x7fb3184953c7] [dcccluster:326996] [24] /lib64/libpython3.8.so.1.0(PyEval_EvalCode+0x23)[0x7fb3184965a3] [dcccluster:326996] [25] /lib64/libpython3.8.so.1.0(+0x22b09a)[0x7fb31852209a] [dcccluster:326996] [26] /lib64/libpython3.8.so.1.0(+0x250fa2)[0x7fb318547fa2] [dcccluster:326996] [27] /lib64/libpython3.8.so.1.0(+0x10312c)[0x7fb3183fa12c] [dcccluster:326996] [28] /lib64/libpython3.8.so.1.0(PyRun_SimpleFileExFlags+0x369)[0x7fb318400fe3] [dcccluster:326996] [29] /lib64/libpython3.8.so.1.0(Py_RunMain+0x2ef)[0x7fb318549b6f] [dcccluster:326996] *** End of error message ***

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Re: how to customize Ion channels and receptor types of "cm_default" neuron model through nestml
by 王梓瑞 09 Mar '23

09 Mar '23

Hi, Charl Thanks for your reply. And sorry about taking such a long time in this question. My main research interest is the biophysical model of glutamatergic cortico-striatal projections. I previous constructed a striatal circuit (contain two neuron populations MSN and FSI) using simple Integrate-and-fire model, and it successfully reproduced the firing state of striatum MSN and FSI. In that case I simplified the glutamatergic cortical input from multi-synapse to single synapse. Since glutamatergic cortico-striatal projections consist of AMPA and NMDA receptors, I then changed the simple Integrate-and-fire model to multi-synapse Integrate-and-fire model using NESTML. But the results didn't seem good, and got even worse when I handly tuned some parameters. I have been confused for a long time and I thought my multi-synapse definition might account for this. I read about "cm_default" neuron model on NEST model directory, which you described containing AMPA_NMDA receptors. And the difference between your definition on this receptor and mine is the term of NMDA ratio (ratio of NMDA versus AMPA channels). So I thought that might help me resolve this issue and that's why I wrote to ask how to customize receptor types of "cm_default" neuron model through NESTML. So briefly: I wonder how I can apply the NMDA ratio in my own multi-synapse Integrate-and-fire model or is it possible to extract the AMPA_NMDA receptors from "cm_default". And I read about the "cm_default" neuron model on NEST model directory, and you described like "For receptors, the choice is AMPA, GABA or NMDA or AMPA_NMDA. Ion channels and receptor types can be customized with NESTML." ------------------------------------------------------------------ Sender:Charl Linssen <nest-users(a)turingbirds.com> Sent At:2022 Dec. 8 (Thu.) 16:56 Recipient:users <users(a)nest-simulator.org> Subject:[NEST Users] Re: how to customize Ion channels and receptor types of "cm_default" neuron model through nestml Dear Zirui, Thank you for writing in. Just to double-check, the compartmental ("cm_default") model in NEST is intended for use in morphologically detailed models, containing dozens or hundreds of compartments. If your model can be described by only a few compartments, it could be easier to manually define these (define say a separate membrane potential and dendritic potential and the coupling between them; see https://nestml.readthedocs.io/en/v5.1.0/tutorials/active_dendrite/nestml_ac… <https://nestml.readthedocs.io/en/v5.1.0/tutorials/active_dendrite/nestml_ac… > for an example). If you do indeed need the full morpological complexity in your simulations, then the cm_default model is indeed the way to go. We are currently working on extending NESTML to support defining the biophysics in NESTML (ion channels, membrane dynamics etc.) and then combining this with a morphology in NEST. For a prototype of this functionality, please see the branch in https://github.com/nest/nestml/pull/772 <https://github.com/nest/nestml/pull/772 >. The current effort there is focused on adding the ability to specify different ion channels in a more flexible manner. If you could describe a little bit more about your use case and what exactly you are trying to achieve, we could probably give you some more specific advice. Cheers, Charl On Mon, Dec 5, 2022, at 05:54, 王梓瑞 wrote: Dear nest community, I wonder if any of you know how to customize Ion channels and receptor types of "cm_default" neuron model through nestml. I noticed some information on Extending NESTML- Running NESTML with custom templates, but I still found myself confused due to my poor understanding. Is there any way to acquire the .nestml file of the cm_default neuron model? Thank you. Best, Zirui _______________________________________________ NEST Users mailing list -- users(a)nest-simulator.org <mailto:users@nest-simulator.org > To unsubscribe send an email to users-leave(a)nest-simulator.org <mailto:users-leave@nest-simulator.org >

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Ubuntu issue with version 3.4 and roll back to version 3.3
by Ing Jyh Tsang 07 Mar '23

07 Mar '23

Hi, I am using Ubuntu and just updated to Nest 3.4 however I get the following error: Feb 26 15:53:59 SLIStartup [Fatal]: SLI initialisation file not found at /build/nest-xF9H0B/nest-3.4/debian/nest/usr/share/nest/sli/sli-init.sli. Please check your NEST installation. Moreover, is there a simple way to roll back and install Nest 3.3 , such as: sudo apt install nest=3.3? Thanks, Inton

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Deepcopy problem with connections
by Xavier Otazu 01 Mar '23

01 Mar '23

Hello, For the below code I get an error related to __cinit__ (Cython) when performing a deepcopy that includes a nest.spatial specification. I am using NEST 3.3 and Python 3.8.12 Best, Xavier ---- import nest import copy my_dict = {'rule': 'pairwise_bernoulli', 'p': nest.spatial_distributions.gaussian(nest.spatial.distance, std=1.0), 'mask': {'circular': {'radius': 4.}}} new_dict = copy.deepcopy(my_dict) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/lib64/python3.8/copy.py", line 146, in deepcopy y = copier(x, memo) File "/usr/lib64/python3.8/copy.py", line 230, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib64/python3.8/copy.py", line 172, in deepcopy y = _reconstruct(x, memo, *rv) File "/usr/lib64/python3.8/copy.py", line 270, in _reconstruct state = deepcopy(state, memo) File "/usr/lib64/python3.8/copy.py", line 146, in deepcopy y = copier(x, memo) File "/usr/lib64/python3.8/copy.py", line 230, in _deepcopy_dict y[deepcopy(key, memo)] = deepcopy(value, memo) File "/usr/lib64/python3.8/copy.py", line 161, in deepcopy rv = reductor(4) File "stringsource", line 2, in pynestkernel.SLIDatum.__reduce_cython__ TypeError: no default __reduce__ due to non-trivial __cinit__

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Free NESTML and NEST Desktop Workshop (27.03.2023)
by Jens Bruchertseifer 27 Feb '23

27 Feb '23

Hello everyone, we are pleased to announce that NESTML and NEST Desktop will be represented at the satellite training sessions (https://flagship.kip.uni-heidelberg.de/jss/HBPm?mI=252&m=showAgenda&showAbs…) as part of the HBP Summit 2023 in Marseille! These sessions will take place on *27 March 2023*. Participation is free of charge, but a registration is mandatory. Please note that only a limited number of participants can register - at the moment there are still places available! The registration page is https://flagship.kip.uni-heidelberg.de/jss/HBPm?meetingID=252. You might need to create a free EBRAINS account for that, if you do not have one yet. The abstract of the workshop can be found below, as well as on the conference page. We also have a dedicated page for this tutorial with detailed information on the topics covered (https://clinssen.github.io/HBP-summit-2023-workshop/). Catering will be provided on site. We are looking forward to meeting you at the workshop! On behalf of the tutorial organizers, Jens Bruchertseifer PS: Please have also a look at the other exciting topics at the training session! ;) ----- License to Spike - A NEST Desktop and NESTML Workshop NEST is an established, open-source simulator for spiking neuronal networks, which can capture a high degree of detail of biological network structures while retaining high performance and scalability from laptops to HPC [1]. This tutorial provides hands-on experience in building and simulating neuron, synapse, and network models. It introduces several tools and front-ends to implement modeling ideas most efficiently. Participants do not have to install software as all tools can be accessed via the cloud. First, we look at NEST Desktop [2], a web-based graphical user interface (GUI), which allows the exploration of essential concepts in computational neuroscience without the need to learn a programming language. This advances both the quality and speed of teaching in computational neuroscience. To get acquainted with the GUI, we will create and analyze a balanced two-population network. In the second half of the session, we will create a new, custom neuron model that extends the capabilities of NEST Simulator by introducing new mechanisms, such as an active spiking dendritic compartment. NESTML [3] makes it quick and easy it is to implement and simulate model variants. A neuronal plasticity rule is then introduced, which allows a network to be trained by means of reinforcement learning. This is accomplished by combinating a typical spike-timing dependent plasticity learning rule with a global neuromodulatory dopamine signal. We will use the new learning rule to train a stimulus preference in the balanced network. Citations [1] https://nest-simulator.readthedocs.org/ [2] https://nest-desktop.readthedocs.org/ [3] https://nestml.readthedocs.org/

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NEST Conference 2023 – Save the date & registration
by Hans Ekkehard Plesser 27 Feb '23

27 Feb '23

Dear Colleagues, The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the NEST Conference 2023. The NEST Conference provides an opportunity for the NEST Community to meet, exchange success stories, swap advice, learn about current developments in and around NEST spiking network simulation and its application. We particularly encourage young scientists to participate in the conference! This year's conference will take place as a virtual event on 15-16 June 2023. Register now! For more information please visit the conference website https://nest-simulator.org/conference We are looking forward to seeing you all in June! Hans Ekkehard Plesser and colleagues [Image] -- Prof. Dr. Hans Ekkehard Plesser Head, Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway Phone +47 6723 1560 Email hans.ekkehard.plesser(a)nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no> Home http://arken.nmbu.no/~plesser

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Open NEST Developer Video Conference Monday Feb 27 2023 11:30 CET (UTC+1)
by Dennis Terhorst 27 Feb '23

27 Feb '23

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference, today Monday February 27, 11.30-12.30 CET (UTC+1). * Special topic for the in-depth today will be the introduction to "NEAT" a Neural Analysis Toolkit by @WillemWybo. * Additionally, @heplesser will shed more light on performance optimizations described in #2617: Integrating "deliver events first". Feel free to join the meeting also if it's just to bring your own quick questions for direct discussion in the in-depth section. As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing work in the team and cross-cutting points that need discussion among the teams. The remainder of the meeting we would go into a more in-depth discussion of topics that came up on the mailing list or that are suggested by the teams. Agenda * Welcome * Review of NEST User Mailing List * Project team round * In-depth discussion The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2023-02-27-Open-NEST-Developer-… Looking forward to seeing you! Cheers, Dennis Terhorst ------------------ Log-in information ------------------ We use a virtual conference room provided by DFN (Deutsches Forschungsnetz). You can use the web client to connect. We however encourage everyone to use a headset for better audio quality or even a proper video conferencing system (see below) or software when available. Web client * Visit https://conf.dfn.de/webapp/conference/97938800 * Enter your name and allow your browser to use camera and microphone * The conference does not need a PIN to join, just click join and you're in. In case you see a dfnconf logo and the phrase "Auf den Meetingveranstalter warten", just be patient, the meeting host needs to join first (a voice will tell you). VC system/software How to log in with a video conferencing system, depends on you VC system or software. - Using the H.323 protocol (eg Polycom): vc.dfn.net##97938800 or 194.95.240.2##97938800 - Using the SIP protocol:97938800@vc.dfn.de - By telephone: +49-30-200-97938800 For those who do not have a video conference system or suitable software, Polycom provides a pretty good free app for iOS and Android, so you can join from your tablet (Polycom RealPresence Mobile, available from AppStore/PlayStore). Note that firewalls may interfere with videoconferencing in various and sometimes confusing ways. For more technical information on logging in from various VC systems, please see http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4

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RunManager and STDP connections
by Xavier Otazu 27 Feb '23

27 Feb '23

Hello, In my model I am using STDP synapse connections. When running simulations with nest.Simulate(), everything seems to work fine. When using nest.Run() inside a "with RunManager():" execution is *MUCH* faster, but STDP connections are not updated. Is it a correct behaviour? Thanks a lot in advance, Xavier

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how to record local field potential from the network
by 王梓瑞 21 Feb '23

21 Feb '23

Dear nest community, Is there anyone know how to record local field potential from the network since I only found spike/weight recorder in NEST. Thank you. Best, Zirui

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Collect the calcium concentration of the neuron without structural plasticity
by Maryada Maryada 18 Feb '23

18 Feb '23

Dear NEST Users, I would like to "record" or read calcium concentration of the neurons (let's say aeif_cond_exp). For now Ca variable is 0 before and after I simulate, however the neuron spikes. Is there any neuron model that supports this, or do I need to edit the nestml of the desired model to achieve this. If so, a sample code snippet would be helpful. -- Thanks and Regards *Maryada*

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Open NEST Developer Video Conference Monday Feb 13 2023 11:30 CET (UTC+1)
by Dennis Terhorst 13 Feb '23

13 Feb '23

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference, today Monday February 13, 11.30-12.30 CET (UTC+1). Feel free to join the meeting also if it's just to bring your own quick questions for direct discussion in the in-depth section. As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing work in the team and cross-cutting points that need discussion among the teams. The remainder of the meeting we would go into a more in-depth discussion of topics that came up on the mailing list or that are suggested by the teams. Agenda * Welcome * Review of NEST User Mailing List * Project team round * In-depth discussion The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2023-02-13-Open-NEST-Developer-… Looking forward to seeing you! Cheers, Dennis Terhorst ------------------ Log-in information ------------------ We use a virtual conference room provided by DFN (Deutsches Forschungsnetz). You can use the web client to connect. We however encourage everyone to use a headset for better audio quality or even a proper video conferencing system (see below) or software when available. Web client * Visit https://conf.dfn.de/webapp/conference/97938800 * Enter your name and allow your browser to use camera and microphone * The conference does not need a PIN to join, just click join and you're in. In case you see a dfnconf logo and the phrase "Auf den Meetingveranstalter warten", just be patient, the meeting host needs to join first (a voice will tell you). VC system/software How to log in with a video conferencing system, depends on you VC system or software. - Using the H.323 protocol (eg Polycom): vc.dfn.net##97938800 or 194.95.240.2##97938800 - Using the SIP protocol:97938800@vc.dfn.de - By telephone: +49-30-200-97938800 For those who do not have a video conference system or suitable software, Polycom provides a pretty good free app for iOS and Android, so you can join from your tablet (Polycom RealPresence Mobile, available from AppStore/PlayStore). Note that firewalls may interfere with videoconferencing in various and sometimes confusing ways. For more technical information on logging in from various VC systems, please see http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4

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Fastest way to transfer dense connectivity data in a parallel simulation?
by Robin Gilbert De Schepper 01 Feb '23

01 Feb '23

Hi all! In the world of biophysical detail, it's commonplace that the connectome is generated with algorithms that specify connections as dense tabular data, with each row specifying a synaptic location on a cell pair (SONATA for example). A) In NEST I can't really find the opportunity to fit this data into any of the connection rules: I want to specify pairwise connections from the multiset A to multiset B. - Is this possible with `pairwise bernoulli`, or do the inputs have to be strict sets? - The probability step is superfluous, can it be skipped? B) Then there's the fact that NEST parallelizes transparently, but since this data was generated in parallel by tiling the biological volume, I have neatly fragmented data already available on each node in the distributed cluster. It would be such a waste to communicate all the data to each node, for NEST to communicate and distribute them back another way. The data is too big to allgather and fit into memory of any single node. Not only is this a lot of overhead to implement, but NEST will throw away all but `1 / Nnodes` of the data on each node again, leaving me with a reshuffled version of my starting data. Is there a way to bypass the transparency and to imperatively declare the cells and connections on each machine? -- Robin De Schepper, MSc (they/them) Department of Brain and Behavioral Sciences Unit of Neurophysiology University of Pavia, Italy Via Forlanini 6, 27100 Pavia - Italy Tel: (+39) 038298-7607 http://www-5.unipv.it/dangelo/ Interested in large scale network modelling? Discover our framework <https://bsb.readthedocs.io/en/latest/>: <https://github.com/dbbs-lab/bsb>

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Re: Update: Ablating and executing a small subset of the Allen V1 model (layer23) using cpp intermediate objects where possible.
by Nicolai Haug 31 Jan '23

31 Jan '23

Hi Russell, Being able to easily build and simulate SONATA networks with NEST is hopefully imminent. We currently have a pull request under review which will add native NEST support for networks described by the SONATA format, see https://github.com/nest/nest-simulator/pull/2595. With our implementation, 64 GB of memory is more than sufficient for building and simulating the entire point neuron version of the Allen V1 model. Regarding a downscaled version of the V1 model, this cannot be achieved solely through the JSON configuration file for this particular model. This is due to all layers (V1 nodes) and associated edges being contained in single HDF5 files. In order to use a downscaled V1 model in the context of the upcoming NEST functionality, you would actually need to construct a new set of SONATA files that contain just the nodes and associated edges you are interested in. That is, you have to parse each nodes and edges .h5 and .csv file, extract the relevant entries, create new files and store the relevant entries in correctly named datasets and in the correct order. Unfortunately, I do not know of any examples that can guide you in this endeavour. You could try to reach out to the SONATA developers here https://github.com/AllenInstitute/sonata. They might have some ideas or even readily available examples. Hope this helps! Best regards, Nicolai

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Need help Compiling NEST to use inside Julia
by Russell Jarvis 31 Jan '23

31 Jan '23

Dear Nest mailing list. I am trying to compile NEST to use inside Julia. Surprisingly or unsuprisingly Julia has great tools to support building external modules in cpp and c source code. I am using the tool BinaryBuilder.jl On a linux system with julia installed\, if I type: julia <ENTER> using Pkg Pkg.add("BinaryBuilder.jl") using BinaryBuilder BinaryBuilder.run_wizard() By following the wizard options, I can download the source code https://github.com/nest/nest-simulator.git and build the source tree in an alpine linux sand box. The building of the source tree in Alpine linux is related to how Julia does cross platform builds, an Alpine micro linux build is included with the Julia binary. From there I can optionally include the Binary file: Libtool_jll.jl Then the binary builder drops me into Alpine Linux environment, in order to test out (monkey patch a workable build script). When I navigate to: /workspace/srcdir/nest-simulator-3.3 I can run: sandbox:${WORKSPACE}/srcdir/nest-simulator-3.3 # cmake -DCMAKE_INSTALL_PREFIX=$prefix -DCMAKE_TOOLCHAIN_FILE=${CMAKE_TARGET_TOOLCHAIN} -DCMAKE_BUILD_TYPE=Release -Dwith-python=OFF -Dwith-warning=ON -Dwith-ltdl=ON and then run I get this output: Re-run cmake no build system arguments -- Could NOT find Doxygen (missing: DOXYGEN_EXECUTABLE) -- Host triple: x86_64-pc-linux -- Target triple: x86_64-pc-linux -- Could NOT find Readline (missing: READLINE_LIBRARY NCURSES_LIBRARY READLINE_INCLUDE_DIR) -- Could NOT find GSL (missing: GSL_INCLUDE_DIR GSL_LIBRARY GSL_CBLAS_LIBRARY) (found version "") -- Check the abort exitcode. -- Check the abort exitcode. -- Check the segmentation fault exitcode. -- Check the segmentation fault exitcode. -- Check whether the compiler ignores cmath makros. -- Check whether the compiler ignores cmath makros. OFF -- Check whether the compiler does NOT include <*.h> headers ISO conformant. -- Check whether the compiler does NOT include <*.h> headers ISO conformant. OFF -- Check whether the compiler respects symbolic signal names in signal.h. -- Check whether the compiler respects symbolic signal names in signal.h. OFF -- Check static template member declaration. -- Check static template member declaration. OFF -- Check for STL vector capacity base unity. -- Check for STL vector capacity base unity. ON -- Check for STL vector capacity doubling strategy. -- Check for STL vector capacity doubling strategy. ON -- Check whether the compiler fails with ICE. -- Check whether the compiler fails with ICE. OFF -- Check if ::nan is available from cmath. -- Check if ::nan is available from cmath. ON -- Check if ::isnan is available from cmath. -- Check if ::isnan is available from cmath. ON -- Check if Random123 generators work. PLEASE_FILL_OUT-NOTFOUND -- Check if Random123 generators work. OFF -- NEST version: 3.3 -------------------------------------------------------------------------------- NEST Configuration Summary -------------------------------------------------------------------------------- Build type : Release Target System : Linux Cross Compiling : TRUE C compiler : GNU 4.8.5 (/opt/bin/x86_64-linux-gnu-libgfortran3-cxx03/x86_64-linux-gnu-gcc) C compiler flags : -O2 -Wall -fopenmp -O3 -DNDEBUG C++ compiler : GNU 4.8.5 (/opt/bin/x86_64-linux-gnu-libgfortran3-cxx03/x86_64-linux-gnu-g++) C++ compiler flags : -O2 -std=c++11 -Wall -fopenmp -O3 -DNDEBUG Build dynamic : ON Built-in modules : models User modules : None Python bindings : No Use threading : Yes (OpenMP: -fopenmp) Use GSL : No Use Readline : No Use libltdl : Yes (LTDL 2.4.6) Includes : /opt/x86_64-linux-gnu/x86_64-linux-gnu/sys-root/usr/local/include Libraries : /opt/x86_64-linux-gnu/x86_64-linux-gnu/sys-root/usr/local/lib/libltdl.so Use doxygen : No Use MPI : No Detailed timers : No Use MUSIC : No Use libneurosim : No Use Boost : Yes (Boost 1.79.0) Includes : /workspace/destdir/include Libraries : Use SIONlib : No -------------------------------------------------------------------------------- The NEST executable will be installed to: /workspace/destdir/bin/ NEST dynamic libraries and user modules will be installed to: /workspace/destdir/lib/nest/ Documentation and examples will be installed to: /workspace/destdir/share/doc/nest/ To set necessary environment variables, add the following line to your ~/.bashrc : source /workspace/destdir/bin/nest_vars.sh -------------------------------------------------------------------------------- ATTENTION! You are about to compile NEST without the GNU Scientific Library or your GSL is too old (before v1.11). This means that conductance-based neuron models and some random number generators will not be available. -------------------------------------------------------------------------------- You can now build and install NEST with make make install make installcheck If you experience problems with the installation or the use of NEST, please see https://www.nest-simulator.org/frequently_asked_questions or go to https://www.nest-simulator.org/community to find out how to join the user mailing list. -- Configuring done -- Generating done -- Build files have been written to: /workspace/srcdir/nest-simulator-3.3 sandbox:${WORKSPACE}/srcdir/nest-simulator-3.3 # sandbox:${WORKSPACE}/srcdir/nest-simulator-3.3/make: but when I try to run make I hit this error: [ 26%] Building CXX object nestkernel/CMakeFiles/nestkernel.dir/recording_backend_ascii.cpp.o cd /workspace/srcdir/nest-simulator-3.3/nestkernel && /opt/bin/x86_64-linux-gnu-libgfortran3-cxx03/x86_64-linux-gnu-g++ --sysroot=/opt/x86_64-linux-gnu/x86_64-linux-gnu/sys-root/ -Dnestkernel_EXPORTS -I/workspace/srcdir/nest-simulator-3.3/thirdparty -I/workspace/srcdir/nest-simulator-3.3/libnestutil -I/workspace/srcdir/nest-simulator-3.3/sli -I/workspace/srcdir/nest-simulator-3.3/nestkernel -I/workspace/srcdir/nest-simulator-3.3/nestkernel/spatial -O2 -std=c++11 -Wall -fopenmp -O3 -DNDEBUG -fPIC -fopenmp -MD -MT nestkernel/CMakeFiles/nestkernel.dir/recording_backend_ascii.cpp.o -MF CMakeFiles/nestkernel.dir/recording_backend_ascii.cpp.o.d -o CMakeFiles/nestkernel.dir/recording_backend_ascii.cpp.o -c /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_algobase.h:64:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/char_traits.h:39, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:40, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_pair.h: In instantiation of ‘constexpr std::pair<_T1, _T2>::pair(const _T1&, const _T2&) [with _T1 = long unsigned int; _T2 = nest::RecordingBackendASCII::DeviceData]’: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_pair.h:281:72: required from ‘constexpr std::pair<typename std::__decay_and_strip<_Tp>::__type, typename std::__decay_and_strip<_T2>::__type> std::make_pair(_T1&&, _T2&&) [with _T1 = const long unsigned int&; _T2 = nest::RecordingBackendASCII::DeviceData; typename std::__decay_and_strip<_T2>::__type = nest::RecordingBackendASCII::DeviceData; typename std::__decay_and_strip<_Tp>::__type = long unsigned int]’ /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:69:108: required from here /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_pair.h:113:31: error: use of deleted function ‘nest::RecordingBackendASCII::DeviceData::DeviceData(const nest::RecordingBackendASCII::DeviceData&)’ : first(__a), second(__b) { } ^ In file included from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:33:0: /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.h:195:10: note: ‘nest::RecordingBackendASCII::DeviceData::DeviceData(const nest::RecordingBackendASCII::DeviceData&)’ is implicitly deleted because the default definition would be ill-formed: struct DeviceData ^ /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.h:195:10: error: use of deleted function ‘std::basic_ofstream<char>::basic_ofstream(const std::basic_ofstream<char>&)’ In file included from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_device.h:27:0, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:27: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:599:11: note: ‘std::basic_ofstream<char>::basic_ofstream(const std::basic_ofstream<char>&)’ is implicitly deleted because the default definition would be ill-formed: class basic_ofstream : public basic_ostream<_CharT,_Traits> ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:599:11: error: use of deleted function ‘std::basic_ostream<char>::basic_ostream(const std::basic_ostream<char>&)’ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:39:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ostream:58:11: note: ‘std::basic_ostream<char>::basic_ostream(const std::basic_ostream<char>&)’ is implicitly deleted because the default definition would be ill-formed: class basic_ostream : virtual public basic_ios<_CharT, _Traits> ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ostream:58:11: error: use of deleted function ‘std::basic_ios<char>::basic_ios(const std::basic_ios<char>&)’ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:44:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/basic_ios.h:66:11: note: ‘std::basic_ios<char>::basic_ios(const std::basic_ios<char>&)’ is implicitly deleted because the default definition would be ill-formed: class basic_ios : public ios_base ^ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:42:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/ios_base.h:786:5: error: ‘std::ios_base::ios_base(const std::ios_base&)’ is private ios_base(const ios_base&); ^ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:44:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/basic_ios.h:66:11: error: within this context class basic_ios : public ios_base ^ In file included from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_device.h:27:0, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:27: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:599:11: error: use of deleted function ‘std::basic_ios<char>::basic_ios(const std::basic_ios<char>&)’ class basic_ofstream : public basic_ostream<_CharT,_Traits> ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:599:11: error: use of deleted function ‘std::basic_filebuf<char>::basic_filebuf(const std::basic_filebuf<char>&)’ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:72:11: note: ‘std::basic_filebuf<char>::basic_filebuf(const std::basic_filebuf<char>&)’ is implicitly deleted because the default definition would be ill-formed: class basic_filebuf : public basic_streambuf<_CharT, _Traits> ^ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:43:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/streambuf:802:7: error: ‘std::basic_streambuf<_CharT, _Traits>::basic_streambuf(const std::basic_streambuf<_CharT, _Traits>&) [with _CharT = char; _Traits = std::char_traits<char>]’ is private basic_streambuf(const basic_streambuf& __sb) ^ In file included from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_device.h:27:0, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:27: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:72:11: error: within this context class basic_filebuf : public basic_streambuf<_CharT, _Traits> ^ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_algobase.h:64:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/char_traits.h:39, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:40, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_pair.h: In instantiation of ‘constexpr std::pair<typename std::__decay_and_strip<_Tp>::__type, typename std::__decay_and_strip<_T2>::__type> std::make_pair(_T1&&, _T2&&) [with _T1 = const long unsigned int&; _T2 = nest::RecordingBackendASCII::DeviceData; typename std::__decay_and_strip<_T2>::__type = nest::RecordingBackendASCII::DeviceData; typename std::__decay_and_strip<_Tp>::__type = long unsigned int]’: /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:69:108: required from here /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_pair.h:281:72: error: use of deleted function ‘constexpr std::pair<_T1, _T2>::pair(std::pair<_T1, _T2>&&) [with _T1 = long unsigned int; _T2 = nest::RecordingBackendASCII::DeviceData]’ return __pair_type(std::forward<_T1>(__x), std::forward<_T2>(__y)); ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_pair.h:128:17: note: ‘constexpr std::pair<_T1, _T2>::pair(std::pair<_T1, _T2>&&) [with _T1 = long unsigned int; _T2 = nest::RecordingBackendASCII::DeviceData]’ is implicitly deleted because the default definition would be ill-formed: constexpr pair(pair&&) = default; ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_pair.h:128:17: error: use of deleted function ‘nest::RecordingBackendASCII::DeviceData::DeviceData(nest::RecordingBackendASCII::DeviceData&&)’ In file included from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:33:0: /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.h:195:10: note: ‘nest::RecordingBackendASCII::DeviceData::DeviceData(nest::RecordingBackendASCII::DeviceData&&)’ is implicitly deleted because the default definition would be ill-formed: struct DeviceData ^ /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.h:195:10: error: use of deleted function ‘std::basic_ofstream<char>::basic_ofstream(const std::basic_ofstream<char>&)’ /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp: In member function ‘virtual void nest::RecordingBackendASCII::enroll(const nest::RecordingDevice&, const DictionaryDatum&)’: /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:69:110: error: no matching function for call to ‘std::map<long unsigned int, nest::RecordingBackendASCII::DeviceData>::insert(std::pair<long unsigned int, nest::RecordingBackendASCII::DeviceData>)’ auto p = device_data_[ t ].insert( std::make_pair( node_id, DeviceData( modelname, vp_node_id_string ) ) ); ^ /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:69:110: note: candidates are: In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/map:61:0, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:39, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:594:7: note: std::pair<typename std::_Rb_tree<_Key, std::pair<const _Key, _Tp>, std::_Select1st<std::pair<const _Key, _Tp> >, _Compare, typename _Alloc::rebind<std::pair<const _Key, _Tp> >::other>::iterator, bool> std::map<_Key, _Tp, _Compare, _Alloc>::insert(const value_type&) [with _Key = long unsigned int; _Tp = nest::RecordingBackendASCII::DeviceData; _Compare = std::less<long unsigned int>; _Alloc = std::allocator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >; typename std::_Rb_tree<_Key, std::pair<const _Key, _Tp>, std::_Select1st<std::pair<const _Key, _Tp> >, _Compare, typename _Alloc::rebind<std::pair<const _Key, _Tp> >::other>::iterator = std::_Rb_tree_iterator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >; std::map<_Key, _Tp, _Compare, _Alloc>::value_type = std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData>] insert(const value_type& __x) ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:594:7: note: no known conversion for argument 1 from ‘std::pair<long unsigned int, nest::RecordingBackendASCII::DeviceData>’ to ‘const value_type& {aka const std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData>&}’ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:602:9: note: template<class _Pair, class> std::pair<typename std::_Rb_tree<_Key, std::pair<const _Key, _Tp>, std::_Select1st<std::pair<const _Key, _Tp> >, _Compare, typename _Alloc::rebind<std::pair<const _Key, _Tp> >::other>::iterator, bool> std::map<_Key, _Tp, _Compare, _Alloc>::insert(_Pair&&) [with _Pair = _Pair; <template-parameter-2-2> = <template-parameter-1-2>; _Key = long unsigned int; _Tp = nest::RecordingBackendASCII::DeviceData; _Compare = std::less<long unsigned int>; _Alloc = std::allocator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >] insert(_Pair&& __x) ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:602:9: note: template argument deduction/substitution failed: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:598:32: error: no type named ‘type’ in ‘struct std::enable_if<false, void>’ template<typename _Pair, typename = typename ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:615:7: note: void std::map<_Key, _Tp, _Compare, _Alloc>::insert(std::initializer_list<std::pair<const _Key, _Tp> >) [with _Key = long unsigned int; _Tp = nest::RecordingBackendASCII::DeviceData; _Compare = std::less<long unsigned int>; _Alloc = std::allocator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >] insert(std::initializer_list<value_type> __list) ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:615:7: note: no known conversion for argument 1 from ‘std::pair<long unsigned int, nest::RecordingBackendASCII::DeviceData>’ to ‘std::initializer_list<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >’ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:644:7: note: std::map<_Key, _Tp, _Compare, _Alloc>::iterator std::map<_Key, _Tp, _Compare, _Alloc>::insert(std::map<_Key, _Tp, _Compare, _Alloc>::const_iterator, const value_type&) [with _Key = long unsigned int; _Tp = nest::RecordingBackendASCII::DeviceData; _Compare = std::less<long unsigned int>; _Alloc = std::allocator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >; std::map<_Key, _Tp, _Compare, _Alloc>::iterator = std::_Rb_tree_iterator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >; std::map<_Key, _Tp, _Compare, _Alloc>::const_iterator = std::_Rb_tree_const_iterator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >; std::map<_Key, _Tp, _Compare, _Alloc>::value_type = std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData>] insert(const_iterator __position, const value_type& __x) ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:644:7: note: candidate expects 2 arguments, 1 provided /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:655:9: note: template<class _Pair, class> std::map<_Key, _Tp, _Compare, _Alloc>::iterator std::map<_Key, _Tp, _Compare, _Alloc>::insert(std::map<_Key, _Tp, _Compare, _Alloc>::const_iterator, _Pair&&) [with _Pair = _Pair; <template-parameter-2-2> = <template-parameter-1-2>; _Key = long unsigned int; _Tp = nest::RecordingBackendASCII::DeviceData; _Compare = std::less<long unsigned int>; _Alloc = std::allocator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >] insert(const_iterator __position, _Pair&& __x) ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:655:9: note: template argument deduction/substitution failed: /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:69:110: note: cannot convert ‘std::make_pair(_T1&&, _T2&&) [with _T1 = const long unsigned int&; _T2 = nest::RecordingBackendASCII::DeviceData; typename std::__decay_and_strip<_T2>::__type = nest::RecordingBackendASCII::DeviceData; typename std::__decay_and_strip<_Tp>::__type = long unsigned int]((* & nest::RecordingBackendASCII::DeviceData(std::basic_string<char>((*(const std::basic_string<char>*)(& modelname))), std::basic_string<char>((*(const std::basic_string<char>*)(& vp_node_id_string))))))’ (type ‘std::pair<long unsigned int, nest::RecordingBackendASCII::DeviceData>’) to type ‘std::map<long unsigned int, nest::RecordingBackendASCII::DeviceData>::const_iterator {aka std::_Rb_tree_const_iterator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >}’ auto p = device_data_[ t ].insert( std::make_pair( node_id, DeviceData( modelname, vp_node_id_string ) ) ); ^ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/map:61:0, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:39, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:670:9: note: template<class _InputIterator> void std::map<_Key, _Tp, _Compare, _Alloc>::insert(_InputIterator, _InputIterator) [with _InputIterator = _InputIterator; _Key = long unsigned int; _Tp = nest::RecordingBackendASCII::DeviceData; _Compare = std::less<long unsigned int>; _Alloc = std::allocator<std::pair<const long unsigned int, nest::RecordingBackendASCII::DeviceData> >] insert(_InputIterator __first, _InputIterator __last) ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/stl_map.h:670:9: note: template argument deduction/substitution failed: /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:69:110: note: candidate expects 2 arguments, 1 provided auto p = device_data_[ t ].insert( std::make_pair( node_id, DeviceData( modelname, vp_node_id_string ) ) ); ^ /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp: In member function ‘void nest::RecordingBackendASCII::DeviceData::open_file()’: /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:266:9: error: use of deleted function ‘std::basic_ofstream<char>& std::basic_ofstream<char>::operator=(const std::basic_ofstream<char>&)’ file_ = std::ofstream( filename.c_str() ); ^ In file included from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_device.h:27:0, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:27: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:599:11: note: ‘std::basic_ofstream<char>& std::basic_ofstream<char>::operator=(const std::basic_ofstream<char>&)’ is implicitly deleted because the default definition would be ill-formed: class basic_ofstream : public basic_ostream<_CharT,_Traits> ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:599:11: error: use of deleted function ‘std::basic_ostream<char>& std::basic_ostream<char>::operator=(const std::basic_ostream<char>&)’ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:39:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ostream:58:11: note: ‘std::basic_ostream<char>& std::basic_ostream<char>::operator=(const std::basic_ostream<char>&)’ is implicitly deleted because the default definition would be ill-formed: class basic_ostream : virtual public basic_ios<_CharT, _Traits> ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ostream:58:11: error: use of deleted function ‘std::basic_ios<char>& std::basic_ios<char>::operator=(const std::basic_ios<char>&)’ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:44:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/basic_ios.h:66:11: note: ‘std::basic_ios<char>& std::basic_ios<char>::operator=(const std::basic_ios<char>&)’ is implicitly deleted because the default definition would be ill-formed: class basic_ios : public ios_base ^ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:42:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/ios_base.h:789:5: error: ‘std::ios_base& std::ios_base::operator=(const std::ios_base&)’ is private operator=(const ios_base&); ^ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:44:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/bits/basic_ios.h:66:11: error: within this context class basic_ios : public ios_base ^ In file included from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_device.h:27:0, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:27: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:599:11: error: use of deleted function ‘std::basic_filebuf<char>& std::basic_filebuf<char>::operator=(const std::basic_filebuf<char>&)’ class basic_ofstream : public basic_ostream<_CharT,_Traits> ^ /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:72:11: note: ‘std::basic_filebuf<char>& std::basic_filebuf<char>::operator=(const std::basic_filebuf<char>&)’ is implicitly deleted because the default definition would be ill-formed: class basic_filebuf : public basic_streambuf<_CharT, _Traits> ^ In file included from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/ios:43:0, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/istream:38, from /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/sstream:38, from /workspace/srcdir/nest-simulator-3.3/thirdparty/compose.hpp:36, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:24: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/streambuf:810:7: error: ‘std::basic_streambuf<_CharT, _Traits>& std::basic_streambuf<_CharT, _Traits>::operator=(const std::basic_streambuf<_CharT, _Traits>&) [with _CharT = char; _Traits = std::char_traits<char>]’ is private operator=(const basic_streambuf&) { return *this; }; ^ In file included from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_device.h:27:0, from /workspace/srcdir/nest-simulator-3.3/nestkernel/recording_backend_ascii.cpp:27: /opt/x86_64-linux-gnu/x86_64-linux-gnu/include/c++/4.8.5/fstream:72:11: error: within this context class basic_filebuf : public basic_streambuf<_CharT, _Traits> ^ make[2]: *** [nestkernel/CMakeFiles/nestkernel.dir/build.make:818: nestkernel/CMakeFiles/nestkernel.dir/recording_backend_ascii.cpp.o] Error 1 make[2]: Leaving directory '/workspace/srcdir/nest-simulator-3.3' make[1]: *** [CMakeFiles/Makefile2:772: nestkernel/CMakeFiles/nestkernel.dir/all] Error 2 make[1]: Leaving directory '/workspace/srcdir/nest-simulator-3.3' make: *** [Makefile:146: all] Error 2 I wonder if the problem might be the GCC version? From this source it looks like 4.8.5 is the default GCC version https://github.com/JuliaPackaging/Yggdrasil/blob/master/RootFS.md#compiler-… GCC Binutils libgfortran SONAME libstdc++ SONAME string ABI 4.8.5 2.24 libgfortran.so.3 libstdc++.so.6.0.19 cxx03 5.2.0 2.25.1 libgfortran.so.3 libstdc++.so.6.0.21 cxx11 6.1.0* 2.26 libgfortran.so.3 libstdc++.so.6.0.22 cxx11 7.1.0 2.27 libgfortran.so.4 libstdc++.so.6.0.23 cxx11 8.1.0 2.31 libgfortran.so.5 libstdc++.so.6.0.25 cxx11 9.1.0 2.33.1 libgfortran.so.5 libstdc++.so.6.0.26 cxx11 10.2.0 2.34 libgfortran.so.5 libstdc++.so.6.0.28 cxx11 11.1.0 2.36 libgfortran.so.5 libstdc++.so.6.0.29 cxx11 Also I wonder if any one has any hints about what to try? I have noticed the Ubuntu Nest dependencies in the Dockerfile here: https://github.com/nest/nest-docker/blob/master/src/base/Dockerfile-build-b… Many of those dependencies have Alpine counter parts, but I'd rather not try to randomly install them. Russell.

3 5

Open NEST Developer Video Conference Monday Jan 30 2023 11:30 CET (UTC+1)
by Dennis Terhorst 30 Jan '23

30 Jan '23

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference, today Monday January 30, 11.30-12.30 CET (UTC+1). Feel free to join the meeting also if it's just to bring your own quick questions for direct discussion in the in-depth section. As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing work in the team and cross-cutting points that need discussion among the teams. The remainder of the meeting we would go into a more in-depth discussion of topics that came up on the mailing list or that are suggested by the teams. Agenda * Welcome * Review of NEST User Mailing List * Project team round * In-depth discussion The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2023-01-30-Open-NEST-Developer-… Looking forward to seeing you! Cheers, Dennis Terhorst ------------------ Log-in information ------------------ We use a virtual conference room provided by DFN (Deutsches Forschungsnetz). You can use the web client to connect. We however encourage everyone to use a headset for better audio quality or even a proper video conferencing system (see below) or software when available. Web client * Visit https://conf.dfn.de/webapp/conference/97938800 * Enter your name and allow your browser to use camera and microphone * The conference does not need a PIN to join, just click join and you're in. In case you see a dfnconf logo and the phrase "Auf den Meetingveranstalter warten", just be patient, the meeting host needs to join first (a voice will tell you). VC system/software How to log in with a video conferencing system, depends on you VC system or software. - Using the H.323 protocol (eg Polycom): vc.dfn.net##97938800 or 194.95.240.2##97938800 - Using the SIP protocol:97938800@vc.dfn.de - By telephone: +49-30-200-97938800 For those who do not have a video conference system or suitable software, Polycom provides a pretty good free app for iOS and Android, so you can join from your tablet (Polycom RealPresence Mobile, available from AppStore/PlayStore). Note that firewalls may interfere with videoconferencing in various and sometimes confusing ways. For more technical information on logging in from various VC systems, please see http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4

1 0

current recording for gap junction
by Hanjia Jiang 30 Jan '23

30 Jan '23

Dear all, I would like to ask if there is a way to measure the actual current that is transmitted through the gap junction in NEST. With the example file gap_junctions_two_neuron.py, I tried to use weight_recorder for the gap junction, but only got a constant value of 0.5 (which is the "weight" set in this file). It seems the "weight" here is only the conductance in nS, after I quickly read the reference Hahne et al., 2015 <https://www.frontiersin.org/articles/10.3389/fninf.2015.00022/full>. Is there a way in PyNEST to record the gap junction current? Thank you for answering! p.s. This is because I am developing astrocyte network models with NEST, which uses a "sic_connection" (slow inward current connection) that is adapted from gap_junction. I am trying to record its current in PyNEST if possible. Unfortunately because the other collaborators developed the sic_connection back then a while ago, I myself do not have an answer to this. I will try other indirect methods, if there is no way to do such a recording in PyNEST. If you have any relevant suggestions, please also kindly let me know. Thanks again. Happy new year, Han-Jia

2 1

Subsetting and executing a small subset of the Allen V1 model (layer23) using cpp intermediate objects where possible.
by Russell Jarvis 28 Jan '23

28 Jan '23

Hi there, I would like to subset and execute the subset of the Allen V1 model. I am also hoping to store intermediate simulation configuration files not as Python code, but if possible as cpp code. I am wondering if there is a way of storing the matrices in a cpp native way? Forinstance I have noticed the class https://github.com/nest/nest-simulator/blob/master/nestkernel/connection_ma… I think that this would class may have I acquired the config.json file from DropBox and all of the assoicated hdf5, and csv files from here: https://www.dropbox.com/sh/w5u31m3hq6u2x5m/AAB0B6OLRyLJwxCKjuWrG26pa/simula… I have found the nodes in the model pertaining to only layer 23 by wrangling data frames in Julia (see the code below). Calling the method `extract_subset` is slower than I would like. I suspect it would be faster to use the SL or /nest_kernel more directly, but I don't know what interventions I would need to do to get the PyNest instance to dump its intermediate objects in a cpp native way. I wonder if any one has any hints on how to do this? subset0 = get_layer_sub_net("i23Htr3a") subset1 = get_layer_sub_net("i23Pvalb") subset2 = get_layer_sub_net("i23Sst") subset3 = get_layer_sub_net("e23Cux2") For instance I would like to be able to use the nestkernel CLI to save using Pkg using Conda using PyCall ENV["PYTHON"] = $HOME/.julia/conda/3/lib/python3.10/ Pkg.build("PyCall") py""" def init_sim_to_call_from_julia():#slice_start,slice_end,nest=None,sim=None) import os, sys import math import numpy as np from bmtk.simulator import pointnet from bmtk.simulator.pointnet.pyfunction_cache import synaptic_weight from bmtk.simulator.pointnet.io_tools import io import nest import h5py try: nest.Install('glifmodule') except Exception as e: pass @synaptic_weight def DirectionRule_others(edges, src_nodes, trg_nodes): src_tuning = src_nodes['tuning_angle'].values tar_tuning = trg_nodes['tuning_angle'].values sigma = edges['weight_sigma'].values nsyn = edges['nsyns'].values syn_weight = edges['syn_weight'].values delta_tuning_180 = np.abs(np.abs(np.mod(np.abs(tar_tuning - src_tuning), 360.0) - 180.0) - 180.0) w_multiplier_180 = np.exp(-(delta_tuning_180 / sigma) ** 2) return syn_weight * w_multiplier_180 * nsyn @synaptic_weight def DirectionRule_EE(edges, src_nodes, trg_nodes): src_tuning = src_nodes['tuning_angle'].values tar_tuning = trg_nodes['tuning_angle'].values x_tar = trg_nodes['x'].values x_src = src_nodes['x'].values z_tar = trg_nodes['z'].values z_src = src_nodes['z'].values sigma = edges['weight_sigma'].values nsyn = edges['nsyns'].values syn_weight = edges['syn_weight'].values delta_tuning_180 = np.abs(np.abs(np.mod(np.abs(tar_tuning - src_tuning), 360.0) - 180.0) - 180.0) w_multiplier_180 = np.exp(-(delta_tuning_180 / sigma) ** 2) delta_x = (x_tar - x_src) * 0.07 delta_z = (z_tar - z_src) * 0.04 theta_pref = tar_tuning * (np.pi / 180.) xz = delta_x * np.cos(theta_pref) + delta_z * np.sin(theta_pref) sigma_phase = 1.0 phase_scale_ratio = np.exp(- (xz ** 2 / (2 * sigma_phase ** 2))) # To account for the 0.07 vs 0.04 dimensions. This ensures the horizontal neurons are scaled by 5.5/4 (from the # midpoint of 4 & 7). Also, ensures the vertical is scaled by 5.5/7. This was a basic linear estimate to get the # numbers (y = ax + b). theta_tar_scale = abs(abs(abs(180.0 - np.mod(np.abs(tar_tuning), 360.0)) - 90.0) - 90.0) phase_scale_ratio = phase_scale_ratio * (5.5 / 4.0 - 11.0 / 1680.0 * theta_tar_scale) return syn_weight * w_multiplier_180 * phase_scale_ratio * nsyn def main(config_file): configure = pointnet.Config.from_json(config_file) configure.build_env() graph = pointnet.PointNetwork.from_config(configure) sim = pointnet.PointSimulator.from_config(configure, graph) node_info = list(graph.get_node_populations()) dfv1 = node_info[0].nodes_df() pop_names = { k:v for k,v in zip(dfv1.index,dfv1["pop_name"].values) } dfv1e = dfv1[dfv1["ei"] == "e"].index dfv1i = dfv1[dfv1["ei"] == "i"].index dfv1e_pop = dfv1[dfv1["ei"] == "e"]["pop_name"].values dfv1i_pop = dfv1[dfv1["ei"] == "i"]["pop_name"].values lgn = node_info[1].nodes_df() lgne = lgn[lgn["ei"] == "e"].index return nest,sim,node_info,dfv1e,dfv1i,lgne,dfv1e_pop,dfv1i_pop,pop_names #,lgni nest,sim,node_info,dfv1e,dfv1i,lgne,dfv1e_pop,dfv1i_pop,pop_names = main('config.json') # ,lgni return (nest,sim,node_info,dfv1e,dfv1i,lgne,dfv1e_pop,dfv1i_pop,pop_names) """ (nest,sim,node_info,dfv1e,dfv1i,lgne,dfv1e_pop,dfv1i_pop,pop_names) = py"init_sim_to_call_from_julia"() function get_layer_sub_net(target) subset = Int64[] for (k,v) in pairs(pop_names) if v==target println(k,v) append!(subset,k) end end subset end subset0 = get_layer_sub_net("i23Htr3a") subset1 = get_layer_sub_net("i23Pvalb") subset2 = get_layer_sub_net("i23Sst") subset3 = get_layer_sub_net("e23Cux2") layer_23_ids = [] layer_23_ids = vcat(layer_23_ids,subset0) layer_23_ids = vcat(layer_23_ids,subset1) layer_23_ids = vcat(layer_23_ids,subset2) layer_23_ids = vcat(layer_23_ids,subset3) py"""def get_conns_from_py_nest(nest,sim,slice_start,nodes=None): conns = nest.GetConnections(nest.NodeCollection([slice_start])) nodes = nest.NodeCollection([slice_start]) node_ = nodes.get() dict_ = conns.get() return (dict_["source"],dict_["target"],dict_["weight"]) """ function extract_subset(nest,sim,subset) N = 230924+1#+17400#230901 exc_matrix = spzeros(N,N) @inbounds for srcv in ProgressBars.ProgressBar(subset) result = py"get_conns_from_py_nest"(nest,sim,srcv) @inbounds for (x,y,z) in zip(Vector{Int64}(result[1]),Vector{Int64}(result[2]),Vector{Float64}(result[3])) exc_matrix[y,x] = z end end exc_matrix end exc_matrix = extract_subset(nest,sim,layer_23_ids) Thanks for your help :) Russell.

1 1

NEST 3.3 installation error
by Petia Koprinkova 16 Jan '23

16 Jan '23

Dear NEST Developers, We are trying to install NEST 3.3 on a Linux machine in Windows 11 desktop computer and the following error report was obtained: -- Docs:https://docs.pytest.org/en/stable/how-to/capture-warnings.html - generated xml file: /nest-3.3.0-build/reports/07_pynesttests.xml - =========================== short testsummary info ============================ FAILED share/nest/testsuite/pytests/test_NodeCollection.py::TestNodeCollection::test_array_indexing FAILEDshare/nest/testsuite/pytests/test_NodeCollection.py::TestNodeCollection::test_array_indexing_bools FAILEDshare/nest/testsuite/pytests/test_NodeCollection.py::TestNodeCollection::test_slice_with_unsorted_raisesFAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_array_list FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_additional_synspec_params FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_different_weights_delays FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_differnt_alpha FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_float_rtype FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_no_delays FAILED share/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_no_weights FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_nonunique FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_nonunique_dict_conn_spec FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_receptor_type FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_rtype FAILEDshare/nest/testsuite/pytests/test_connect_arrays.py::TestConnectArrays::test_connect_arrays_threaded =========== 15 failed, 669 passed, 15skipped, 27 warnings in 43.13s =========== make[3]: *** [CMakeFiles/installcheck.dir/build.make:71:CMakeFiles/installcheck] Error 1 make[2]: *** [CMakeFiles/Makefile2:465:CMakeFiles/installcheck.dir/all] Error 2 make[1]: *** [CMakeFiles/Makefile2:472:CMakeFiles/installcheck.dir/rule] Error 2 make: *** [Makefile:179: installcheck]Error 2 Could you please help to resolve it?Best regards,Petia Koprinkova-Hristova

2 3

Open NEST Developer Video Conference Monday Jan 16 2023 11:30 CET (UTC+1)
by Dennis Terhorst 16 Jan '23

16 Jan '23

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference, today Monday January 16, 11.30-12.30 CET (UTC+1). Feel free to join the meeting also if it's just to bring your own quick questions for direct discussion in the in-depth section. As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing work in the team and cross-cutting points that need discussion among the teams. The remainder of the meeting we would go into a more in-depth discussion of topics that came up on the mailing list or that are suggested by the teams. Agenda * Welcome * Review of NEST User Mailing List * Project team round * In-depth discussion The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2023-01-16-Open-NEST-Developer-… Looking forward to seeing you! Cheers, Dennis Terhorst ------------------ Log-in information ------------------ We use a virtual conference room provided by DFN (Deutsches Forschungsnetz). You can use the web client to connect. We however encourage everyone to use a headset for better audio quality or even a proper video conferencing system (see below) or software when available. Web client * Visit https://conf.dfn.de/webapp/conference/97938800 * Enter your name and allow your browser to use camera and microphone * The conference does not need a PIN to join, just click join and you're in. In case you see a dfnconf logo and the phrase "Auf den Meetingveranstalter warten", just be patient, the meeting host needs to join first (a voice will tell you). VC system/software How to log in with a video conferencing system, depends on you VC system or software. - Using the H.323 protocol (eg Polycom): vc.dfn.net##97938800 or 194.95.240.2##97938800 - Using the SIP protocol:97938800@vc.dfn.de - By telephone: +49-30-200-97938800 For those who do not have a video conference system or suitable software, Polycom provides a pretty good free app for iOS and Android, so you can join from your tablet (Polycom RealPresence Mobile, available from AppStore/PlayStore). Note that firewalls may interfere with videoconferencing in various and sometimes confusing ways. For more technical information on logging in from various VC systems, please see http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4

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Error installing extension module
by bstrohmer＠gmail.com 13 Jan '23

13 Jan '23

I would like to install a custom neuron model in NEST so I followed the instructions for the nest-extension-module. It is able to build the .so files but it errors during the final install of the project as seen in the below message. Install the project... -- Install configuration: "" -- Installing: /home/bs/git_repos/extension/nest-simulator/result/include/piftestmodule/pif_psc_alpha.h -- Installing: /home/bs/git_repos/extension/nest-simulator/result/lib/nest/piftestmodule.so -- Installing: /home/bs/git_repos/extension/nest-simulator/result/lib/nest/libpiftestmodule.so Traceback (most recent call last): File "<string>", line 1, in <module> File "/home/bs/git_repos/extension/nest-simulator/result/lib/python3.8/site-packages/nest/__init__.py", line 444 err = AttributeError(f"Cannot set attribute '{attr}' on module 'nest'") I am trying to install one of the example neurons provided first (pif_psc_alpha) so I do not expect that it is an issue within the source files. Thanks for any advice in correcting this issue!

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stdp_dopamine_synapse model
by Behnam Ghazinouri 05 Jan '23

05 Jan '23

Hi all, Happy new year! I have a question about the "stdp_dopamine_synapse" model. In the documentation, they mentioned that they used it as a benchmark the model in [1], based on [2]. In [1], in the section "2.2 Benchmark Network Models", they have C_1 and C_2. They just mentioned these two parameters are constant coefficients. My question is: Does NEST implement these equations? If yes, how can I find the values of C_1 and C_2? (they are not listed under the nest.GetValues() and are not in the table of the documentation). If not, what is the exact model that the NEST implemented for "stdp_dopamine_synapse"? Best regards, Behnam [1] Potjans W, Morrison A, Diesmann M (2010). Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity. Frontiers in Computational Neuroscience, 4:141. DOI: https://doi.org/10.3389/fncom.2010.00141 <https://doi.org/10.3389/fncom.2010.00141> [2] Izhikevich EM (2007). Solving the distal reward problem through linkage of STDP and dopamine signaling. Cerebral Cortex, 17(10):2443-2452. DOI: https://doi.org/10.1093/cercor/bhl152 <https://doi.org/10.1093/cercor/bhl152>

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Open NEST Developer Video Conference Monday Dec 19 2022 11:30 CET (UTC+1)
by Dennis Terhorst 19 Dec '22

19 Dec '22

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference, today Monday December 19, 11.30-12.30 CET (UTC+1). Feel free to join the meeting also if it's just to bring your own questions for direct discussion in the in-depth section. As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing work in the team and cross-cutting points that need discussion among the teams. The remainder of the meeting we would go into a more in-depth discussion of topics that came up on the mailing list or that are suggested by the teams. Agenda * Welcome * Review of NEST User Mailing List * Project team round * In-depth discussion The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2022-12-19-Open-NEST-Developer-… As I will not be joining today, I delegated chairing the meeting. Looking forward to seeing you again next year! Merry Christmas and a happy new year! Cheers, Dennis Terhorst :*) ------------------ Log-in information ------------------ We use a virtual conference room provided by DFN (Deutsches Forschungsnetz). You can use the web client to connect. We however encourage everyone to use a headset for better audio quality or even a proper video conferencing system (see below) or software when available. Web client * Visit https://conf.dfn.de/webapp/conference/97938800 * Enter your name and allow your browser to use camera and microphone * The conference does not need a PIN to join, just click join and you're in. In case you see a dfnconf logo and the phrase "Auf den Meetingveranstalter warten", just be patient, the meeting host needs to join first (a voice will tell you). VC system/software How to log in with a video conferencing system, depends on you VC system or software. - Using the H.323 protocol (eg Polycom): vc.dfn.net##97938800 or 194.95.240.2##97938800 - Using the SIP protocol:97938800@vc.dfn.de - By telephone: +49-30-200-97938800 For those who do not have a video conference system or suitable software, Polycom provides a pretty good free app for iOS and Android, so you can join from your tablet (Polycom RealPresence Mobile, available from AppStore/PlayStore). Note that firewalls may interfere with videoconferencing in various and sometimes confusing ways. For more technical information on logging in from various VC systems, please see http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4

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how to customize Ion channels and receptor types of "cm_default" neuron model through nestml
by 王梓瑞 08 Dec '22

08 Dec '22

Dear nest community, I wonder if any of you know how to customize Ion channels and receptor types of "cm_default" neuron model through nestml. I noticed some information on Extending NESTML- Running NESTML with custom templates, but I still found myself confused due to my poor understanding. Is there any way to acquire the .nestml file of the cm_default neuron model? Thank you. Best, Zirui

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Open NEST Developer Video Conference Monday Dec 5 2022 11:30 CET (UTC+1)
by Dennis Terhorst 05 Dec '22

05 Dec '22

Dear NEST Users & Developers! I would like to invite you to our next fortnightly Open NEST Developer Video Conference, today Monday December 5, 11.30-12.30 CET (UTC+1). Feel free to join the meeting also if it's just to bring your own questions for direct discussion in the in-depth section. Special topic for the in-depth discussion for today are / Hackathon Results / As usual, in the Project team round, a contact person of each team will give a short statement summarizing ongoing work in the team and cross-cutting points that need discussion among the teams. The remainder of the meeting we would go into a more in-depth discussion of topics that came up on the mailing list or that are suggested by the teams. Agenda * Welcome * Review of NEST User Mailing List * Project team round * In-depth discussion The agenda for this meeting is also available online, see https://github.com/nest/nest-simulator/wiki/2022-12-05-Open-NEST-Developer-… Looking forward to seeing you again soon! Cheers, Dennis Terhorst ------------------ Log-in information ------------------ We use a virtual conference room provided by DFN (Deutsches Forschungsnetz). You can use the web client to connect. We however encourage everyone to use a headset for better audio quality or even a proper video conferencing system (see below) or software when available. Web client * Visit https://conf.dfn.de/webapp/conference/97938800 * Enter your name and allow your browser to use camera and microphone * The conference does not need a PIN to join, just click join and you're in. In case you see a dfnconf logo and the phrase "Auf den Meetingveranstalter warten", just be patient, the meeting host needs to join first (a voice will tell you). VC system/software How to log in with a video conferencing system, depends on you VC system or software. - Using the H.323 protocol (eg Polycom): vc.dfn.net##97938800 or 194.95.240.2##97938800 - Using the SIP protocol:97938800@vc.dfn.de - By telephone: +49-30-200-97938800 For those who do not have a video conference system or suitable software, Polycom provides a pretty good free app for iOS and Android, so you can join from your tablet (Polycom RealPresence Mobile, available from AppStore/PlayStore). Note that firewalls may interfere with videoconferencing in various and sometimes confusing ways. For more technical information on logging in from various VC systems, please see http://vcc.zih.tu-dresden.de/index.php?linkid=1.1.3.4

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docker questions
by 1538356357＠qq.com 05 Dec '22

05 Dec '22

I use Docker to create a nest environment. The version is v2.16.0 But when I execute the code as follows，vt_dummy = nest.Create("global_volume_transmitter", 1, {'deliver_interval': 10})， , I encounter an error, the error is pynestkernel.NESTError: UnknownModelName in GetDefaults_l: /global_volume_transmitter is not a known model name. Please check the modeldict for a list of available models . but after I use the nest2.16 source code to install, the same code executes successfully. I don't know why

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