brainpy.synapses.Delta#

class brainpy.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=None, 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 import synapses, neurons
>>> import matplotlib.pyplot as plt
>>>
>>> neu1 = neurons.LIF(1)
>>> neu2 = neurons.LIF(1)
>>> syn1 = synapses.Alpha(neu1, neu2, bp.connect.All2All(), g_max=5.)
>>> net = bp.Network(pre=neu1, syn=syn1, post=neu2)
>>>
>>> runner = bp.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, png, hires.png, pdf)

../../../_images/brainpy-synapses-Delta-1.png

Parameters

pre (NeuGroup) – The pre-synaptic neuron group.
post (NeuGroup) – The post-synaptic neuron group.
conn (optional, ArrayType, 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, ArrayType, Initializer, Callable) – The delay length. It should be the value of \(\mathrm{delay\_time / dt}\).
g_max (float, ArrayType, 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=None, 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`()
`cpu`()	Move all variable into the CPU device.
`cuda`()	Move all variables into the GPU device.
`get_delay_data`(identifier, delay_step, *indices)	Get delay data according to the provided delay steps.
`load_state_dict`(state_dict[, warn])	Copy parameters and buffers from `state_dict` into this module and its descendants.
`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[, node_cls])
`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.
`state_dict`()	Returns a dictionary containing a whole state of the module.
`to`(device)	Moves all variables into the given device.
`tpu`()	Move all variables into the TPU device.
`train_vars`([method, level, include_self])	The shortcut for retrieving all trainable variables.
`tree_flatten`()	Flattens the object as a PyTree.
`tree_unflatten`(aux, dynamic_values)	New in version 2.3.1.
`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`	Global delay data, which stores the delay variables and corresponding delay targets.
`mode`	Mode of the model, which is useful to control the multiple behaviors of the model.
`name`	Name of the model.