brainpy.dyn.synapses.STP
brainpy.dyn.synapses.STP#
- class brainpy.dyn.synapses.STP(pre, post, conn, U=0.15, tau_f=1500.0, tau_d=200.0, tau=8.0, A=1.0, delay_step=None, method='exp_auto', name=None)[source]#
Short-term plasticity model.
Model Descriptions
Short-term plasticity (STP) 1 2 3, also called dynamical synapses, refers to the changes of synaptic strengths over time in a way that reflects the history of presynaptic activity. Two types of STP, with opposite effects on synaptic efficacy, have been observed in experiments. They are known as Short-Term Depression (STD) and Short-Term Facilitation (STF).
In the model proposed by Tsodyks and Markram 4 5, the STD effect is modeled by a normalized variable \(x (0 \le x \le 1)\), denoting the fraction of resources that remain available after neurotransmitter depletion. The STF effect is modeled by a utilization parameter \(u\), representing the fraction of available resources ready for use (release probability). Following a spike,
(i) \(u\) increases due to spike-induced calcium influx to the presynaptic terminal, after which
(ii) a fraction \(u\) of available resources is consumed to produce the post-synaptic current.
Between spikes, \(u\) decays back to zero with time constant \(\tau_f\) and \(x\) recovers to 1 with time constant \(\tau_d\).
In summary, the dynamics of STP is given by
\[\begin{split}\begin{aligned} \frac{du}{dt} & = -\frac{u}{\tau_f}+U(1-u^-)\delta(t-t_{sp}),\nonumber \\ \frac{dx}{dt} & = \frac{1-x}{\tau_d}-u^+x^-\delta(t-t_{sp}), \\ \frac{dI}{dt} & = -\frac{I}{\tau_s} + Au^+x^-\delta(t-t_{sp}), \end{aligned}\end{split}\]where \(t_{sp}\) denotes the spike time and \(U\) is the increment of \(u\) produced by a spike. \(u^-, x^-\) are the corresponding variables just before the arrival of the spike, and \(u^+\) refers to the moment just after the spike. The synaptic current generated at the synapse by the spike arriving at \(t_{sp}\) is then given by
\[\Delta I(t_{spike}) = Au^+x^-\]where \(A\) denotes the response amplitude that would be produced by total release of all the neurotransmitter (\(u=x=1\)), called absolute synaptic efficacy of the connections.
Model Examples
STD
>>> import brainpy as bp >>> import matplotlib.pyplot as plt >>> >>> neu1 = bp.dyn.LIF(1) >>> neu2 = bp.dyn.LIF(1) >>> syn1 = bp.dyn.STP(neu1, neu2, bp.connect.All2All(), U=0.2, tau_d=150., tau_f=2.) >>> net = bp.dyn.Network(pre=neu1, syn=syn1, post=neu2) >>> >>> runner = bp.dyn.DSRunner(net, inputs=[('pre.input', 28.)], monitors=['syn.I', 'syn.u', 'syn.x']) >>> runner.run(150.) >>> >>> >>> # plot >>> fig, gs = bp.visualize.get_figure(2, 1, 3, 7) >>> >>> fig.add_subplot(gs[0, 0]) >>> plt.plot(runner.mon.ts, runner.mon['syn.u'][:, 0], label='u') >>> plt.plot(runner.mon.ts, runner.mon['syn.x'][:, 0], label='x') >>> plt.legend() >>> >>> fig.add_subplot(gs[1, 0]) >>> plt.plot(runner.mon.ts, runner.mon['syn.I'][:, 0], label='I') >>> plt.legend() >>> >>> plt.xlabel('Time (ms)') >>> plt.show()
(Source code, png, hires.png, pdf)
STF
>>> import brainpy as bp >>> import matplotlib.pyplot as plt >>> >>> neu1 = bp.dyn.LIF(1) >>> neu2 = bp.dyn.LIF(1) >>> syn1 = bp.dyn.STP(neu1, neu2, bp.connect.All2All(), U=0.1, tau_d=10, tau_f=100.) >>> net = bp.dyn.Network(pre=neu1, syn=syn1, post=neu2) >>> >>> runner = bp.dyn.DSRunner(net, inputs=[('pre.input', 28.)], monitors=['syn.I', 'syn.u', 'syn.x']) >>> runner.run(150.) >>> >>> >>> # plot >>> fig, gs = bp.visualize.get_figure(2, 1, 3, 7) >>> >>> fig.add_subplot(gs[0, 0]) >>> plt.plot(runner.mon.ts, runner.mon['syn.u'][:, 0], label='u') >>> plt.plot(runner.mon.ts, runner.mon['syn.x'][:, 0], label='x') >>> plt.legend() >>> >>> fig.add_subplot(gs[1, 0]) >>> plt.plot(runner.mon.ts, runner.mon['syn.I'][:, 0], label='I') >>> plt.legend() >>> >>> plt.xlabel('Time (ms)') >>> plt.show()
(Source code, png, hires.png, pdf)
Model Parameters
Parameter
Init Value
Unit
Explanation
tau_d
200
ms
Time constant of short-term depression.
tau_f
1500
ms
Time constant of short-term facilitation.
U
.15
The increment of \(u\) produced by a spike.
A
1
The response amplitude that would be produced by total release of all the neurotransmitter
delay
0
ms
The decay time of the current \(I\) output onto the post-synaptic neuron groups.
Model Variables
Member name
Initial values
Explanation
u
0
Release probability of the neurotransmitters.
x
1
A Normalized variable denoting the fraction of remain neurotransmitters.
I
0
Synapse current output onto the post-synaptic neurons.
References
- 1
Stevens, Charles F., and Yanyan Wang. “Facilitation and depression at single central synapses.” Neuron 14, no. 4 (1995): 795-802.
- 2
Abbott, Larry F., J. A. Varela, Kamal Sen, and S. B. Nelson. “Synaptic depression and cortical gain control.” Science 275, no. 5297 (1997): 221-224.
- 3
Abbott, L. F., and Wade G. Regehr. “Synaptic computation.” Nature 431, no. 7010 (2004): 796-803.
- 4
Tsodyks, Misha, Klaus Pawelzik, and Henry Markram. “Neural networks with dynamic synapses.” Neural computation 10.4 (1998): 821-835.
- 5
Tsodyks, Misha, and Si Wu. “Short-term synaptic plasticity.” Scholarpedia 8, no. 10 (2013): 3153.
- __init__(pre, post, conn, U=0.15, tau_f=1500.0, tau_d=200.0, tau=8.0, A=1.0, delay_step=None, method='exp_auto', name=None)[source]#
Methods
__init__
(pre, post, conn[, U, tau_f, tau_d, ...])check_post_attrs
(*attrs)Check whether post group satisfies the requirement.
check_pre_attrs
(*attrs)Check whether pre group satisfies the requirement.
get_delay_data
(name, delay_step, *indices)Get delay data according to the provided delay steps.
ints
([method])Collect all integrators in this node and the children nodes.
load_states
(filename[, verbose])Load the model states.
nodes
([method, level, include_self])Collect all children nodes.
register_delay
(name, delay_step, delay_target)Register delay variable.
register_implicit_nodes
(nodes)register_implicit_vars
(variables)reset
()Reset function which reset the whole variables in the model.
reset_delay
(name, delay_target)Reset the delay variable.
save_states
(filename[, variables])Save the model states.
train_vars
([method, level, include_self])The shortcut for retrieving all trainable variables.
unique_name
([name, type_])Get the unique name for this object.
update
(t, dt)The function to specify the updating rule.
update_delay
(name, delay_data)Update the delay according to the delay data.
vars
([method, level, include_self])Collect all variables in this node and the children nodes.
Attributes
derivative
global_delay_vars
name
steps