brainpy.dyn.synapses.Delta#

class brainpy.dyn.synapses.Delta(pre, post, conn, output=CUBA, stp=None, comp_method='sparse', g_max=1.0, delay_step=None, post_ref_key=None, name=None, mode=NormalMode, stop_spike_gradient=False)[source]#

Voltage Jump Synapse Model, or alias of Delta Synapse Model.

Model Descriptions

\[I_{syn} (t) = \sum_{j\in C} g_{\mathrm{max}} * \mathrm{STP} * \delta(t-t_j-D)\]

where \(g_{\mathrm{max}}\) denotes the chemical synaptic strength, \(t_j\) the spiking moment of the presynaptic neuron \(j\), \(C\) the set of neurons connected to the post-synaptic neuron, \(D\) the transmission delay of chemical synapses, and \(\mathrm{STP}\) the short-term plasticity effect. For simplicity, the rise and decay phases of post-synaptic currents are omitted in this model.

Model Examples

>>> import brainpy as bp
>>> from brainpy.dyn import synapses, neurons
>>> import matplotlib.pyplot as plt
>>>
>>> neu1 = neurons.LIF(1)
>>> neu2 = neurons.LIF(1)
>>> syn1 = synapses.Alpha(neu1, neu2, bp.connect.All2All(), weights=5.)
>>> net = bp.dyn.Network(pre=neu1, syn=syn1, post=neu2)
>>>
>>> runner = bp.dyn.DSRunner(net, inputs=[('pre.input', 25.), ('post.input', 10.)], monitors=['pre.V', 'post.V', 'pre.spike'])
>>> runner.run(150.)
>>>
>>> fig, gs = bp.visualize.get_figure(1, 1, 3, 8)
>>> 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.xlim(40, 150)
>>> plt.legend()
>>> plt.show()

(Source code)

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.

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

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

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

  • post_ref_key (str) – Whether the post-synaptic group has refractory period.

__init__(pre, post, conn, output=CUBA, stp=None, comp_method='sparse', g_max=1.0, delay_step=None, post_ref_key=None, name=None, mode=NormalMode, stop_spike_gradient=False)[source]#

Methods

__init__(pre, post, conn[, output, stp, ...])

check_post_attrs(*attrs)

Check whether post group satisfies the requirement.

check_pre_attrs(*attrs)

Check whether pre group satisfies the requirement.

clear_input()

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

init_weights(weight, comp_method[, sparse_data])

rtype

Union[float, TypeVar(Array, JaxArray, Variable, TrainVar, Array, ndarray)]

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

offline_fit(target, fit_record)

offline_init()

online_fit(target, fit_record)

online_init()

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

reset([batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state([batch_size])

Reset function which reset the states in the model.

save_states(filename[, variables])

Save the model states.

syn2post_with_all2all(syn_value, syn_weight)

syn2post_with_dense(syn_value, syn_weight, ...)

syn2post_with_one2one(syn_value, syn_weight)

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(tdi[, pre_spike])

The function to specify the updating rule.

update_local_delays([nodes])

Update local delay variables.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

global_delay_data

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.