""" Name: madexp_psc_alpha_ref - Energy-based leaky integrate-and-fire neuron. Description: The dynamics are given by: C_m dV_m/dt = g_L*(V-E_L) - w + I_e + I_syn_ex + I_syn_in tau_w dw/dt = a(V-E_L) - epsilon/epsilon_0 w + I_KATP*epsilon_0/(epsilon_0 + epsilon) tau_e depsilon/dt = (1-epsilon/(alpha*epsilon_0))**3 - (V-E_f)/(E_d-E_f) - gamma*w E_L = E_0 + (E_u - E_0)(1-epsilon/epsilon_0) if V_m >= V_th and epsilon > epsilon_c: V_m is set to V_reset On each spike arrival, the membrane potential feels an alpha-shaped current of the form: I_syn = I_0 * t * exp(-t/tau_syn) / tau_syn. Sends: SpikeEvent Receives: SpikeEvent, CurrentEvent, DataLoggingRequest FirstVersion: 2019 Author: Tanguy Fardet """ neuron madexp_psc_alpha_ref: state: r integer = 0 # number of steps for refractory phase epsilon real = alpha*epsilon_0 # Energy V_m mV = E_0 + (E_u - E_0)*(1 - epsilon/epsilon_0) # Membrane potential w pA = 0*pA # Adaptation current end function I_spike(epsilon real, V_m mV) pA: Ispk pA = 0. pA arg_exp real = 0. if Delta_T > 0. mV: arg_exp = clip((V_m - V_th) / Delta_T, -20., 20.) Ispk = (epsilon-epsilon_c)*g_L*Delta_T*exp(arg_exp) / epsilon_0 end return Ispk end equations: inline eps_bound real = max(epsilon, 0.) # non-negative energy inline V_bound mV = min(V_m, V_peak) # prevent exponential divergence # synapses: alpha functions kernel I_syn_in = (e/tau_syn_in) * t * exp(-t/tau_syn_in) kernel I_syn_ex = (e/tau_syn_ex) * t * exp(-t/tau_syn_ex) inline E_L mV = E_0 + (E_u - E_0)*(1 - eps_bound / epsilon_0) inline I_in pA = convolve(I_syn_in, spikesInh) inline I_ex pA = convolve(I_syn_ex, spikesExc) inline Ispike pA = I_spike(eps_bound, V_bound) V_m' = (g_L*(E_L - V_bound) + Ispike - w + I_e + I_in + I_ex + currents) / C_m w' = (a*(V_bound-E_L) - w + I_KATP*epsilon_c/(eps_bound + epsilon_c)) / tau_w epsilon' = ((1 - eps_bound / (alpha*epsilon_0))*(1 - eps_bound / (alpha*epsilon_0))*(1 - eps_bound / (alpha*epsilon_0)) - (V_bound-E_f)/(E_d-E_f) - w / gamma) / tau_e end parameters: C_m pF = 100. pF # Membrane capacitance g_L nS = 9. nS # leak conductance Delta_T mV = 1.0 mV # Slope factor V_peak mV = 0 mV # Spike detection threshold E_0 mV = -65. mV # resting potential E_u mV = -58. mV # upper potential E_d mV = -50. mV # energy depletion potential E_f mV = -60. mV # energy inflexion potential epsilon_0 real = 0.5 # standard resting energy level epsilon_c real = 0.2 # energy threshold for spike generation alpha real = 1. # energetic health delta real = 0.1 # energy consumption per spike tau_e ms = 1000. ms # time constant for energy production I_e pA = 0. pA # Constant input current V_th mV = -55. mV # Spike generation threshold a nS = 1. nS # subthreshold adaptation b pA = 2. pA # spike-triggered adaptation gamma pA = 1000. pA # normalization of adaptation energy I_KATP pA = 10. pA # peak ATP-gated potassium current tau_w ms = 300. ms # timescale of the adaptation current V_reset mV = -62. mV # Reset potential tau_syn_ex ms = 0.2 ms # Synaptic Time Constant Excitatory Synapse tau_syn_in ms = 2.0 ms # Synaptic Time Constant for Inhibitory Synapse t_ref ms = 2.0 ms # Refractory period end internals: RefractoryCounts integer = steps(t_ref) # refractory time in steps Vspike real = Delta_T == 0 ? min(V_th, V_peak) : V_peak end input: spikesInh pA <- inhibitory spike spikesExc pA <- excitatory spike currents pA <- continuous end output: spike update: integrate_odes() if epsilon < 0.: epsilon = 0. end # refractoriness and threshold crossing if r > 0: # is refractory? r -= 1 V_m = V_reset elif V_m >= Vspike and epsilon > epsilon_c: # this test is necessary to support Delta_T = 0 V_m = V_reset epsilon -= delta w += b emit_spike() r = RefractoryCounts end end end