brainpy.dyn.synapses.DeltaSynapse

class brainpy.dyn.synapses.DeltaSynapse(pre, post, conn, conn_type='sparse', weights=1.0, delay_step=None, post_key='V', post_has_ref=False, name=None)

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

Model Descriptions

\[I_{syn} (t) = \sum_{j\in C} w \delta(t-t_j-D)\]

where \(w\) 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, and \(D\) the transmission delay of chemical synapses. For simplicity, the rise and decay phases of post-synaptic currents are omitted in this model.

Model Examples

>>> import brainpy as bp
>>> import matplotlib.pyplot as plt
>>>
>>> neu1 = bp.dyn.LIF(1)
>>> neu2 = bp.dyn.LIF(1)
>>> syn1 = bp.dyn.DeltaSynapse(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, png, hires.png, pdf)

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 sparse.

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

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

• post_key (str) – The key of the post variable. It should be a string. The key should be the attribute of the post-synaptic neuron group.

• post_has_ref (bool) – Whether the post-synaptic group has refractory period.

__init__(pre, post, conn, conn_type='sparse', weights=1.0, delay_step=None, post_key='V', post_has_ref=False, name=None)

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.
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

global_delay_vars
name
steps