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iaf_cond_beta_neuron.nestml
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iaf_cond_beta_neuron.nestml
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"""
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 post-synaptic change
of conductance modelled by a beta function. The beta function
is normalised such that an event of weight 1.0 results in a peak current of
1 nS at :math:`t = \tau_{rise\_[ex|in]}`.
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.
See also
++++++++
iaf_cond_exp, iaf_cond_alpha
"""
model iaf_cond_beta_neuron:
state:
V_m mV = E_L # Membrane potential
refr_t ms = 0 ms # Refractory period timer
is_refractory boolean = false
# inputs from the inhibitory conductance
g_in real = 0
g_in$ real = g_I_const * (1 / tau_syn_rise_I - 1 / tau_syn_decay_I)
# inputs from the excitatory conductance
g_ex real = 0
g_ex$ real = g_E_const * (1 / tau_syn_rise_E - 1 / tau_syn_decay_E)
equations:
kernel g_in' = g_in$ - g_in / tau_syn_rise_I,
g_in$' = -g_in$ / tau_syn_decay_I
kernel g_ex' = g_ex$ - g_ex / tau_syn_rise_E,
g_ex$' = -g_ex$ / tau_syn_decay_E
inline I_syn_exc pA = (F_E + convolve(g_ex, exc_spikes) * nS) * (V_m - E_ex)
inline I_syn_inh pA = (F_I + convolve(g_in, inh_spikes)* nS) * (V_m - E_in)
inline I_leak pA = g_L * (V_m - E_L) # pA = nS * mV
V_m' = (-I_leak - I_syn_exc - I_syn_inh + I_e + I_stim ) / C_m
parameters:
C_m pF = 250 pF # Capacitance of the membrane
g_L nS = 16.6667 nS # Leak conductance
E_L mV = -70 mV # Leak reversal potential (aka resting potential)
refr_T ms = 2 ms # Duration of refractory period
V_th mV = -55 mV # Threshold potential
V_reset mV = -60 mV # Reset potential
E_ex mV = 0 mV # Excitatory reversal potential
E_in mV = -85 mV # Inhibitory reversal potential
tau_syn_rise_I ms = .2 ms # Synaptic time constant excitatory synapse
tau_syn_decay_I ms = 2 ms # Synaptic time constant for inhibitory synapse
tau_syn_rise_E ms = .2 ms # Synaptic time constant excitatory synapse
tau_syn_decay_E ms = 2 ms # Synaptic time constant for inhibitory synapse
F_E nS = 0 nS # Constant external input conductance (excitatory).
F_I nS = 0 nS # Constant external input conductance (inhibitory).
# constant external input current
I_e pA = 0 pA
internals:
# time of peak conductance excursion after spike arrival at t = 0
t_peak_E real = tau_syn_decay_E * tau_syn_rise_E * ln(tau_syn_decay_E / tau_syn_rise_E) / (tau_syn_decay_E - tau_syn_rise_E)
t_peak_I real = tau_syn_decay_I * tau_syn_rise_I * ln(tau_syn_decay_I / tau_syn_rise_I) / (tau_syn_decay_I - tau_syn_rise_I)
# normalisation constants to ensure arriving spike yields peak conductance of 1 nS
g_E_const real = 1 / (exp(-t_peak_E / tau_syn_decay_E) - exp(-t_peak_E / tau_syn_rise_E))
g_I_const real = 1 / (exp(-t_peak_I / tau_syn_decay_I) - exp(-t_peak_I / tau_syn_rise_I))
input:
exc_spikes <- excitatory spike
inh_spikes <- inhibitory spike
I_stim pA <- continuous
output:
spike
update:
if is_refractory:
# neuron is absolute refractory, do not evolve V_m
refr_t -= resolution()
else:
# neuron not refractory, so evolve all ODEs (including V_m)
integrate_odes()
onCondition(V_m >= V_th):
# threshold crossing
refr_t = refr_T # start of the refractory period
is_refractory = true
V_m = V_reset
emit_spike()
onCondition(is_refractory and refr_t <= resolution() / 2):
# end of refractory period
refr_t = 0 ms
is_refractory = false