iaf_cond_beta – Simple conductance based leaky integrate-and-fire neuron model¶

Description¶

iaf_cond_beta is an implementation of a spiking neuron using IAF dynamics with conductance-based synapses. Incoming spike events induce a postsynaptic change of conductance modelled by a beta function. The beta function is normalized such that an event of weight 1.0 results in a peak current of 1 nS at $$t = \tau_{rise\_[ex|in]}$$.

Note

Per 2009-04-17, this class has been revised to our newest insights into class design. Please use THIS CLASS as a reference when designing your own models with nonlinear dynamics. One weakness of this class is that it distinguishes between inputs to the two synapses by the sign of the synaptic weight. It would be better to use receptor_types, cf iaf_cond_alpha_mc.

See also 1, 2, 3, 4, 5.

Parameters¶

The following parameters can be set in the status dictionary.

 V_m mV Membrane potential E_L mV Leak reversal potential C_m pF Capacity of the membrane t_ref ms Duration of refractory period V_th mV Spike threshold V_reset mV Reset potential of the membrane E_ex mV Excitatory reversal potential E_in mV Inhibitory reversal potential g_L nS Leak conductance tau_rise_ex ms Rise time of the excitatory synaptic beta function tau_decay_ex ms Decay time of the excitatory synaptic beta function tau_rise_in ms Rise time of the inhibitory synaptic beta function tau_decay_in ms Decay time of the inhibitory synaptic beta function I_e pA Constant input current

Sends¶

SpikeEvent

SpikeEvent, CurrentEvent, DataLoggingRequest

References¶

1

Meffin H, Burkitt AN, Grayden DB (2004). An analytical model for the large, fluctuating synaptic conductance state typical of neocortical neurons in vivo. Journal of Computational Neuroscience, 16:159-175. DOI: https://doi.org/10.1023/B:JCNS.0000014108.03012.81

2

Bernander O, Douglas RJ, Martin KAC, Koch C (1991). Synaptic background activity influences spatiotemporal integration in single pyramidal cells. Proceedings of the National Academy of Science USA, 88(24):11569-11573. DOI: https://doi.org/10.1073/pnas.88.24.11569

3

Kuhn A, Rotter S (2004) Neuronal integration of synaptic input in the fluctuation- driven regime. Journal of Neuroscience, 24(10):2345-2356 DOI: https://doi.org/10.1523/JNEUROSCI.3349-03.2004

4

Rotter S, Diesmann M (1999). Exact simulation of time-invariant linear systems with applications to neuronal modeling. Biologial Cybernetics 81:381-402. DOI: https://doi.org/10.1007/s004220050570

5

Roth A and van Rossum M (2010). Chapter 6: Modeling synapses. in De Schutter, Computational Modeling Methods for Neuroscientists, MIT Press.