**Name:**

iaf_psc_delta_canon - Leaky integrate-and-fire neuron model.

**Description:**

iaf_psc_delta_canon is an implementation of a leaky integrate-and-fire model

where the potential jumps on each spike arrival.

The threshold crossing is followed by an absolute refractory period

during which the membrane potential is clamped to the resting

potential.

Spikes arriving while the neuron is refractory, are discarded by

default. If the property "refractory_input" is set to true, such

spikes are added to the membrane potential at the end of the

refractory period, dampened according to the interval between

arrival and end of refractoriness.

The linear subthresold dynamics is integrated by the Exact

Integration scheme [1]. The neuron dynamics are solved exactly in

time. Incoming and outgoing spike times are handled precisely [3].

An additional state variable and the corresponding differential

equation represents a piecewise constant external current.

Spikes can occur either on receipt of an excitatory input spike, or

be caused by a depolarizing input current. Spikes evoked by

incoming spikes, will occur precisely at the time of spike arrival,

since incoming spikes are modeled as instantaneous potential

jumps. Times of spikes caused by current input are determined

exactly by solving the membrane potential equation. Note that, in

contrast to the neuron models discussed in [3,4], this model has so

simple dynamics that no interpolation or iterative spike location

technique is required at all.

The general framework for the consistent formulation of systems with

neuron like dynamics interacting by point events is described in

[1]. A flow chart can be found in [2].

Critical tests for the formulation of the neuron model are the

comparisons of simulation results for different computation step

sizes. sli/testsuite/nest contains a number of such tests.

The iaf_psc_delta_canon is the standard model used to check the consistency

of the nest simulation kernel because it is at the same time complex

enough to exhibit non-trivial dynamics and simple enough compute

relevant measures analytically.

**Parameters:**

The following parameters can be set in the status dictionary.

V_m double - Membrane potential in mV

E_L double - Resting membrane potential in mV.

C_m double - Capacitance of the membrane in pF

tau_m double - Membrane time constant in ms.

t_ref double - Duration of refractory period in ms.

V_th double - Spike threshold in mV.

V_reset double - Reset potential of the membrane in mV.

I_e double - Constant input current in pA.

V_min double - Absolute lower value for the membrane potential in mV.

refractory_input bool - If true, do not discard input during

refractory period. Default: false.

**Receives:**

SpikeEvent, CurrentEvent, DataLoggingRequest

Author: May 2006, Plesser; based on work by Diesmann, Gewaltig, Morrison,

Straube, Eppler

**Sends:**

SpikeEvent

**Remarks:**

The iaf_psc_delta_canon neuron accepts CurrentEvent connections.

However, the present method for transmitting CurrentEvents in

NEST (sending the current to be applied) is not compatible with off-grid

currents, if more than one CurrentEvent-connection exists. Once CurrentEvents

are changed to transmit change-of-current-strength, this problem will

disappear and the canonical neuron will also be able to handle CurrentEvents.

The present implementation uses individual variables for the

components of the state vector and the non-zero matrix elements of

the propagator. Because the propagator is a lower triangular matrix

no full matrix multiplication needs to be carried out and the

computation can be done "in place" i.e. no temporary state vector

object is required.

The template support of recent C++ compilers enables a more succinct

formulation without loss of runtime performance already at minimal

optimization levels. A future version of iaf_psc_delta_canon will probably

address the problem of efficient usage of appropriate vector and

matrix objects.

Please note that this node is capable of sending precise spike times

to target nodes (on-grid spike time plus offset). If this node is

connected to a spike_detector, the property "precise_times" of the

spike_detector has to be set to true in order to record the offsets

in addition to the on-grid spike times.

**References:**

[1] Rotter S & Diesmann M (1999) Exact simulation of time-invariant linear

systems with applications to neuronal modeling. Biologial Cybernetics

81:381-402.

[2] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001) State space

analysis of synchronous spiking in cortical neural networks.

Neurocomputing 38-40:565-571.

[3] Morrison A, Straube S, Plesser H E, & Diesmann M (2006) Exact

Subthreshold Integration with Continuous Spike Times in Discrete Time Neural

Network Simulations. To appear in Neural Computation.

[4] Hanuschkin A, Kunkel S, Helias M, Morrison A & Diesmann M (2010)

A general and efficient method for incorporating exact spike times in

globally time-driven simulations Front Neuroinformatics, 4:113

**SeeAlso:**

**Source:**

/home/nest/work/nest-2.14.0/precise/iaf_psc_delta_canon.h