# SpikeQueue class¶

(Shortest import: from brian2.synapses.spikequeue import SpikeQueue)

class brian2.synapses.spikequeue.SpikeQueue(source_start, source_end)[source]

Bases: object

Data structure saving the spikes and taking care of delays.

Parameters

source_start : int

The start of the source indices (for subgroups)

source_end : int

The end of the source indices (for subgroups)

Notes :

—– :

**Data structure** :

A spike queue is implemented as a 2D array X that is circular in the time :

direction (rows) and dynamic in the events direction (columns). The :

row index corresponding to the current timestep is currentime. :

Each element contains the target synapse index. :

**Offsets** :

Offsets are used to solve the problem of inserting multiple synaptic events :

with the same delay. This is difficult to vectorise. If there are n synaptic :

events with the same delay, these events are given an offset between 0 and :

n-1, corresponding to their relative position in the data structure. :

Attributes

 _dt The dt used for storing the spikes (will be set in prepare) _source_end The end of the source indices (for subgroups) _source_start The start of the source indices (for subgroups) currenttime The current time (in time steps) n number of events in each time step

Methods

 Advances by one timestep Returns the all the synaptic events corresponding to the current time, as an array of synapse indexes. prepare(delays, dt, synapse_sources) Prepare the data structures push(sources) Push spikes to the queue.

Details

_dt

The dt used for storing the spikes (will be set in prepare)

_source_end

The end of the source indices (for subgroups)

_source_start

The start of the source indices (for subgroups)

currenttime

The current time (in time steps)

n

number of events in each time step

advance()[source]

peek()[source]

Returns the all the synaptic events corresponding to the current time, as an array of synapse indexes.

prepare(delays, dt, synapse_sources)[source]

Prepare the data structures

This is called every time the network is run. The size of the of the data structure (number of rows) is adjusted to fit the maximum delay in delays, if necessary. A flag is set if delays are homogeneous, in which case insertion will use a faster method implemented in insert_homogeneous.

push(sources)[source]

Push spikes to the queue.

Parameters

sources : ndarray of int

The indices of the neurons that spiked.