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'])
>>> 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()

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

  • 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]#


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


Check whether post group satisfies the requirement.


Check whether pre group satisfies the requirement.



Move all variable into the CPU device.


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, compatible])

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.

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes[, node_cls])

register_implicit_vars(*variables[, var_cls])

reset(*args, **kwargs)

Reset function which reset the whole variables in the model.


Reset local delay variables.


Reset function which reset the states in the model.

save_states(filename[, variables])

Save the model states.


Returns a dictionary containing a whole state of the module.


Moves all variables into the given device.


Move all variables into the TPU device.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.


Flattens the object as a PyTree.

tree_unflatten(aux, dynamic_values)

Unflatten the data to construct an object of this class.

unique_name([name, type_])

Get the unique name for this object.

update(tdi[, pre_spike])

The function to specify the updating rule.


Update local delay variables.

vars([method, level, include_self, ...])

Collect all variables in this node and the children nodes.



Global delay data, which stores the delay variables and corresponding delay targets.


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


Name of the model.