Synaptic plasticity: pyramid->pyr and pyr->interneuron (Tsodyks et al 1998)

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An implementation of a model of short-term synaptic plasticity with NEURON. The model was originally described by Tsodyks et al., who assumed that the synapse acted as a current source, but this implementation treats it as a conductance change. Tsodyks, M., Pawelzik, K., Markram, H. Neural networks with dynamic synapses. Neural Computation 10:821-835, 1998. Tsodyks, M., Uziel, A., Markram, H. Synchrony generation in recurrent networks with frequency-dependent synapses. J. Neurosci. 2000 RC50.
1 . Tsodyks M, Pawelzik K, Markram H (1998) Neural networks with dynamic synapses. Neural Comput 10:821-35 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Receptor(s): AMPA;
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Short-term Synaptic Plasticity; Facilitation; Depression;
Implementer(s): Carnevale, Ted [Ted.Carnevale at];
Search NeuronDB for information about:  AMPA; Glutamate;
Revised 12/15/2000 in light of a personal communication 
from Misha Tsodyks that u is incremented _before_ x is 
converted to y--a point that was not clear in the paper.
If u is incremented _after_ x is converted to y, then 
the first synaptic activation after a long interval of 
silence will produce smaller and smaller postsynaptic 
effect as the length of the silent interval increases, 
eventually becoming vanishingly small.

Implementation of a model of short-term facilitation and depression 
based on the kinetics described in
  Tsodyks et al.
  Synchrony generation in recurrent networks 
  with frequency-dependent synapses
  Journal of Neuroscience 20:RC50:1-5, 2000.
Their mechanism represented synapses as current sources.
The mechanism implemented here uses a conductance change instead.

The basic scheme is

x -------> y    Instantaneous, spike triggered.
                Increment is u*x (see discussion of u below).
                x == fraction of "synaptic resources" that have 
                     "recovered" (fraction of xmtr pool that is 
                     ready for release, or fraction of postsynaptic 
                     channels that are ready to be opened, or some 
                     joint function of these two factors)
                y == fraction of "synaptic resources" that are in the 
                     "active state."  This is proportional to the 
                     number of channels that are open, or the 
                     fraction of max synaptic current that is 
                     being delivered. 
y -------> z    z == fraction of "synaptic resources" that are 
                     in the "inactive state"

z -------> x

where x + y + z = 1

The active state y is multiplied by a synaptic weight to compute
the actual synaptic conductance (or current, in the original form 
of the model).

In addition, there is a "facilition" term u that 
governs the fraction of x that is converted to y 
on each synaptic activation.

  -------> u    Instantaneous, spike triggered, 
                happens _BEFORE_ x is converted to y.
                Increment is U*(1-u) where U and u both 
                lie in the range 0 - 1.
u ------->      decay of facilitation

This implementation for NEURON offers the user a parameter 
u0 that has a default value of 0 but can be used to specify 
a nonzero initial value for u.

When tau_facil = 0, u is supposed to equal U.

Note that the synaptic conductance in this mechanism 
has the same kinetics as y, i.e. decays with time 
constant tau_1.

This mechanism can receive multiple streams of 
synaptic input via NetCon objects.  
Each stream keeps track of its own 
weight and activation history.

The printf() statements are for testing purposes only.

	RANGE e, i
	RANGE tau_1, tau_rec, tau_facil, U, u0

	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)

	: e = -90 mV for inhibitory synapses,
	:     0 mV for excitatory
	e = -90	(mV)
	: tau_1 was the same for inhibitory and excitatory synapses
	: in the models used by T et al.
	tau_1 = 3 (ms) < 1e-9, 1e9 >
	: tau_rec = 100 ms for inhibitory synapses,
	:           800 ms for excitatory
	tau_rec = 100 (ms) < 1e-9, 1e9 >
	: tau_facil = 1000 ms for inhibitory synapses,
	:             0 ms for excitatory
	tau_facil = 1000 (ms) < 0, 1e9 >
	: U = 0.04 for inhibitory synapses, 
	:     0.5 for excitatory
	: the (1) is needed for the < 0, 1 > to be effective
	:   in limiting the values of U and u0
	U = 0.04 (1) < 0, 1 >
	: initial value for the "facilitation variable"
	u0 = 0 (1) < 0, 1 >

	v (mV)
	i (nA)

	g (umho)


	SOLVE state METHOD cnexp
	i = g*(v - e)

	g' = -g/tau_1

NET_RECEIVE(weight (umho), y, z, u, tsyn (ms)) {
: these are in NET_RECEIVE to be per-stream
	y = 0
	z = 0
:	u = 0
	u = u0
	tsyn = t
: this header will appear once per stream
: printf("t\t t-tsyn\t y\t z\t u\t newu\t g\t dg\t newg\t newy\n")

	: first calculate z at event-
	:   based on prior y and z
	z = z*exp(-(t - tsyn)/tau_rec)
	z = z + ( y*(exp(-(t - tsyn)/tau_1) - exp(-(t - tsyn)/tau_rec)) / ((tau_1/tau_rec)-1) )
	: now calc y at event-
	y = y*exp(-(t - tsyn)/tau_1)

	x = 1-y-z

	: calc u at event--
	if (tau_facil > 0) {
		u = u*exp(-(t - tsyn)/tau_facil)
	} else {
		u = U

: printf("%g\t%g\t%g\t%g\t%g", t, t-tsyn, y, z, u)

	if (tau_facil > 0) {
		state_discontinuity(u, u + U*(1-u))

: printf("\t%g\t%g\t%g", u, g, weight*x*u)

	state_discontinuity(g, g + weight*x*u)
	state_discontinuity(y, y + x*u)

	tsyn = t

: printf("\t%g\t%g\n", g, y)

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