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
.
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
Receives¶
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.