: Izhikevich artificial neuron model from
: EM Izhikevich "Simple Model of Spiking Neurons"
: IEEE Transactions On Neural Networks, Vol. 14, No. 6, November 2003 pp 1569-1572
: V is the voltage analog, u controls
: see COMMENT below or izh.hoc for typical parameter values
: uncomment lines with dvv,du to graph derivatives
NEURON {
POINT_PROCESS Izhi2003a
RANGE a,b,c,d,f,g,Iin,fflag,thresh,erev,taug
}
INITIAL {
V=-65
u=0.2*V
gsyn=0
net_send(0,1)
}
PARAMETER {
a = 0.02
b = 0.2
c = -65
d = 2
f = 5
g = 140
Iin = 10
taug = 1
thresh=30
erev = 0
fflag = 1
}
STATE { u V gsyn } : use V for voltage so don't interfere with built-in v of cell
ASSIGNED {
}
BREAKPOINT {
SOLVE states METHOD derivimplicit
}
DERIVATIVE states {
V' = 0.04*V*V + f*V + g - u + Iin - gsyn*(V-erev)
u' = a*(b*V-u)
gsyn' = -gsyn/taug
}
NET_RECEIVE (w) {
if (flag == 1) {
WATCH (V>thresh) 2
} else if (flag == 2) {
net_event(t)
V = c
u = u+d
} else { : synaptic activation
gsyn = gsyn+w
}
}
:** vers gives version
PROCEDURE version () {
}
COMMENT
a b c d Iin
================================================================================
0.02 0.2 -65 6 14 % tonic spiking
0.02 0.25 -65 6 0.5 % phasic spiking
0.02 0.2 -50 2 15 % tonic bursting
0.02 0.25 -55 0.05 0.6 % phasic bursting
0.02 0.2 -55 4 10 % mixed mode
0.01 0.2 -65 8 30 % spike frequency adaptation
0.02 -0.1 -55 6 0 % Class 1
0.2 0.26 -65 0 0 % Class 2
0.02 0.2 -65 6 7 % spike latency
0.05 0.26 -60 0 0 % subthreshold oscillations
0.1 0.26 -60 -1 0 % resonator
0.02 -0.1 -55 6 0 % integrator
0.03 0.25 -60 4 0 % rebound spike
0.03 0.25 -52 0 0 % rebound burst
0.03 0.25 -60 4 0 % threshold variability
1 1.5 -60 0 -65 % bistability
1 0.2 -60 -21 0 % DAP
0.02 1 -55 4 0 % accomodation
-0.02 -1 -60 8 80 % inhibition-induced spiking
-0.026 -1 -45 0 80 % inhibition-induced bursting
ENDCOMMENT