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This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

aeif_psc_delta_clopath – Adaptive exponential integrate-and-fire neuron

Description

aeif_psc_delta_clopath is an implementation of the neuron model as it is used in 1. It is an extension of the aeif_psc_delta model and capable of connecting to a Clopath synapse.

Note that there are two points that are not mentioned in the paper but present in a MATLAB implementation by Claudia Clopath 3. The first one is the clamping of the membrane potential to a fixed value after a spike occured to mimik a real spike and not just the upswing. This is important since the finite duration of the spike influences the evolution of the convolved versions (u_bar_[plus/minus]) of the membrane potential and thus the change of the synaptic weight. Secondly, there is a delay with which u_bar_[plus/minus] are used to compute the change of the synaptic weight.

Note: Neither the clamping nor the delayed processing of u_bar_[plus/minus] are mentioned in 1. However, they are part of an reference implementation by Claudia Clopath et al. that can be found on ModelDB 3. The clamping is important to mimic a spike which is otherwise not described by the aeif neuron model.

For implementation details see the aeif_models_implementation notebook.

Parameters

The following parameters can be set in the status dictionary.

Dynamic state variables

V_m

mV

Membrane potential

w

pA

Spike-adaptation current

z

pA

Spike-adaptation current

V_th

mV

Adaptive spike initiation threshold

u_bar_plus

mV

Low-pass filtered Membrane potential

u_bar_minus

mV

Low-pass filtered Membrane potential

u_bar_bar

mV

Low-pass filtered u_bar_minus

Membrane Parameters

C_m

pF

Capacity of the membrane

t_ref

ms

Duration of refractory period

V_reset

mV

Reset value for V_m after a spike

E_L

mV

Leak reversal potential

g_L

nS

Leak conductance

I_e

pA

Constant external input current

tau_plus

ms

Time constant of u_bar_plus

tau_minus

ms

Time constant of u_bar_minus

tau_bar_bar

ms

Time constant of u_bar_bar

Spike adaptation parameters

a

nS

Subthreshold adaptation

b

pA

Spike-triggered adaptation

Delta_T

mV

Slope factor

tau_w

ms

Adaptation time constant

V_peak

mV

Spike detection threshold

V_th_max

mV

Value of V_th afer a spike

V_th_rest

mV

Resting value of V_th

Clopath rule parameters

A_LTD

1/mV

Amplitude of depression

A_LTP

1/mV^2

Amplitude of facilitation

theta_plus

mV

Threshold for u

theta_minus

mV

Threshold for u_bar_[plus/minus]

A_LTD_const

boolean

Flag that indicates whether A_LTD_ should be constant (true, default) or multiplied by u_bar_bar^2 / u_ref_squared (false).

delay_u_bars

real

Delay with which u_bar_[plus/minus] are processed to compute the synaptic weights.

U_ref_squared

real

Reference value for u_bar_bar_^2.

Integration parameters

gsl_error_tol

real

This parameter controls the admissible error of the GSL integrator. Reduce it if NEST complains about numerical instabilities.

Sends

SpikeEvent

Receives

SpikeEvent, CurrentEvent, DataLoggingRequest

References

1(1,2)

Clopath et al. (2010). Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nature Neuroscience 13(3):344-352. DOI: https://doi.org/10.1038/nn.2479

2

Clopath and Gerstner (2010). Voltage and spike timing interact in STDP – a unified model. Frontiers in Synaptic Neuroscience. 2:25 DOI: https://doi.org/10.3389/fnsyn.2010.00025

3(1,2)

Voltage-based STDP synapse (Clopath et al. 2010) on ModelDB https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=144566&file=%2f modeldb_package%2fVoTriCode%2faEIF.m