brainpy.dyn.synapses.AlphaCOBA#

class brainpy.dyn.synapses.AlphaCOBA(pre, post, conn, conn_type='dense', g_max=1.0, delay_step=None, tau_decay=10.0, E=0.0, method='exp_auto', name=None)[source]#

Conductance-based alpha synapse model.

Model Descriptions

The conductance-based alpha synapse model is similar with the current-based alpha synapse model, except the expression which output onto the post-synaptic neurons:

\[I_{syn}(t) = g_{\mathrm{syn}}(t) (V(t)-E)\]

where \(V(t)\) is the membrane potential of the post-synaptic neuron, \(E\) is the reversal potential.

Model Examples

>>> import brainpy as bp
>>> import matplotlib.pyplot as plt
>>>
>>> neu1 = bp.dyn.HH(1)
>>> neu2 = bp.dyn.HH(1)
>>> syn1 = bp.dyn.AlphaCOBA(neu1, neu2, bp.connect.All2All(), E=0.)
>>> net = bp.dyn.Network(pre=neu1, syn=syn1, post=neu2)
>>>
>>> runner = bp.dyn.DSRunner(net, inputs=[('pre.input', 5.)], monitors=['pre.V', 'post.V', 'syn.g', 'syn.h'])
>>> runner.run(150.)
>>>
>>> fig, gs = bp.visualize.get_figure(2, 1, 3, 8)
>>> fig.add_subplot(gs[0, 0])
>>> plt.plot(runner.mon.ts, runner.mon['pre.V'], label='pre-V')
>>> plt.plot(runner.mon.ts, runner.mon['post.V'], label='post-V')
>>> plt.legend()
>>> fig.add_subplot(gs[1, 0])
>>> plt.plot(runner.mon.ts, runner.mon['syn.g'], label='g')
>>> plt.plot(runner.mon.ts, runner.mon['syn.h'], label='h')
>>> plt.legend()
>>> plt.show()

(Source code, png, hires.png, pdf)

../../../../_images/brainpy-dyn-synapses-AlphaCOBA-1.png
Parameters
  • pre (NeuGroup) – The pre-synaptic neuron group.

  • post (NeuGroup) – The post-synaptic neuron group.

  • conn (optional, ndarray, JaxArray, dict of (str, ndarray), TwoEndConnector) – The synaptic connections.

  • conn_type (str) – The connection type used for model speed optimization. It can be sparse and dense. The default is dense.

  • delay_step (int, ndarray, JaxArray, Initializer, Callable) – The delay length. It should be the value of \(\mathrm{delay\_time / dt}\).

  • E (float, JaxArray, ndarray) – The reversal potential for the synaptic current. [mV]

  • tau_decay (float, JaxArray, ndarray) – The time constant of the synaptic decay phase. [ms]

  • g_max (float, ndarray, JaxArray, Initializer, Callable) – The synaptic strength (the maximum conductance). Default is 1.

  • name (str) – The name of this synaptic projection.

  • method (str) – The numerical integration methods.

References

1

Sterratt, David, Bruce Graham, Andrew Gillies, and David Willshaw. “The Synapse.” Principles of Computational Modelling in Neuroscience. Cambridge: Cambridge UP, 2011. 172-95. Print.

__init__(pre, post, conn, conn_type='dense', g_max=1.0, delay_step=None, tau_decay=10.0, E=0.0, method='exp_auto', name=None)[source]#

Methods

__init__(pre, post, conn[, conn_type, ...])

check_post_attrs(*attrs)

Check whether post group satisfies the requirement.

check_pre_attrs(*attrs)

Check whether pre group satisfies the requirement.

dg(g, t, h)

dh(h, t)

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.

output(g_post)

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.

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.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

global_delay_targets

global_delay_vars

name

steps