Spike Input/Output

slayerSNN.spikeFileIO.animTD(TD, fig=None, frameRate=24, preComputeFrames=True, repeat=True)[source]

Reutrn animation object for TD event.

Arguments:

  • TD: spike event to visualize.

  • fig: figure to plot animation. Default is None, in which case a figure is created.

  • frameRate: framerate of visualization.

  • preComputeFrames: flag to enable precomputation of frames for faster visualization. Default is True.

  • repeat: flag to enable repeat of animation. Default is True.

Usage:

>>> anim = animTD(TD)
slayerSNN.spikeFileIO.encode1DnumSpikes(filename, nID, tSt, tEn, nSp)[source]

Writes binary spike file given a tuple specifying neuron, start of spike region, end of spike region and number of spikes.

The binary file is encoded as follows:

  • Number of spikes data is represented by an 80 bit number

  • First 16 bits (bits 79-64) represent the neuronID

  • Next 24 bits (bits 63-40) represents the start time in microseconds

  • Next 24 bits (bits 39-16) represents the end time in microseconds

  • Last 16 bits (bits 15-0) represents the number of spikes

Arguments:

  • filename (string): path to the binary file

  • nID (numpy array): neuron ID

  • tSt (numpy array): region start time (in milliseconds)

  • tEn (numpy array): region end time (in milliseconds)

  • nSp (numpy array): number of spikes in the region

Usage:

>>> spikeFileIO.encode1DnumSpikes(file_path, nID, tSt, tEn, nSp)
slayerSNN.spikeFileIO.encode1Dspikes(filename, TD)[source]

Writes one dimensional binary spike file from a TD event.

The binary file is encoded as follows:

  • Each spike event is represented by a 40 bit number.

  • First 16 bits (bits 39-24) represent the neuronID.

  • Bit 23 represents the sign of spike event: 0=>OFF event, 1=>ON event.

  • the last 23 bits (bits 22-0) represent the spike event timestamp in microseconds.

Arguments:

  • filename (string): path to the binary file.

  • TD (an spikeFileIO.event): TD event.

Usage:

>>> spikeFileIO.write1Dspikes(file_path, TD)
slayerSNN.spikeFileIO.encode2Dspikes(filename, TD)[source]

Writes two dimensional binary spike file from a TD event. It is the same format used in neuromorphic datasets NMNIST & NCALTECH101.

The binary file is encoded as follows:

  • Each spike event is represented by a 40 bit number.

  • First 8 bits (bits 39-32) represent the xID of the neuron.

  • Next 8 bits (bits 31-24) represent the yID of the neuron.

  • Bit 23 represents the sign of spike event: 0=>OFF event, 1=>ON event.

  • The last 23 bits (bits 22-0) represent the spike event timestamp in microseconds.

Arguments:

  • filename (string): path to the binary file.

  • TD (an spikeFileIO.event): TD event.

Usage:

>>> spikeFileIO.write2Dspikes(file_path, TD)
slayerSNN.spikeFileIO.encode3Dspikes(filename, TD)[source]

Writes binary spike file for TD event in height, width and channel dimension.

The binary file is encoded as follows:

  • Each spike event is represented by a 56 bit number.

  • First 12 bits (bits 56-44) represent the xID of the neuron.

  • Next 12 bits (bits 43-32) represent the yID of the neuron.

  • Next 8 bits (bits 31-24) represents the channel ID of the neuron.

  • The last 24 bits (bits 23-0) represent the spike event timestamp in microseconds.

Arguments:

  • filename (string): path to the binary file.

  • TD (an spikeFileIO.event): TD event.

Usage:

>>> spikeFileIO.write3Dspikes(file_path, TD)
slayerSNN.spikeFileIO.encodeNpSpikes(filename, TD, fmt='xypt', timeUnit=0.001)[source]

Writes TD event into numpy file.

Arguments:

  • filename (string): path to the binary file.

  • TD (an spikeFileIO.event): TD event.

Usage:

>>> spikeFileIO.write1Dspikes(file_path, TD)
>>> spikeFileIO.write1Dspikes(file_path, TD, fmt='xypt')
class slayerSNN.spikeFileIO.event(xEvent, yEvent, pEvent, tEvent)[source]

This class provides a way to store, read, write and visualize spike event.

Members:

  • x (numpy int array): x index of spike event.

  • y (numpy int array): y index of spike event (not used if the spatial dimension is 1).

  • p (numpy int array): polarity or channel index of spike event.

  • t (numpy double array): timestamp of spike event. Time is assumend to be in ms.

Usage:

>>> TD = spikeFileIO.event(xEvent, yEvent, pEvent, tEvent)
toSpikeArray(samplingTime=1, dim=None)[source]

Returns a numpy tensor that contains the spike events sampled in bins of samplingTime. The array is of dimension (channels, height, time) or``CHT`` for 1D data. The array is of dimension (channels, height, width, time) or``CHWT`` for 2D data.

Arguments:

  • samplingTime: the width of time bin to use.

  • dim: the dimension of the desired tensor. Assignes dimension itself if not provided.

Usage:

>>> spike = TD.toSpikeArray()
toSpikeTensor(emptyTensor, samplingTime=1, randomShift=False, binningMode='OR')[source]

Returns a numpy tensor that contains the spike events sampled in bins of samplingTime. The tensor is of dimension (channels, height, width, time) or``CHWT``.

Arguments:

  • emptyTensor (numpy or torch tensor): an empty tensor to hold spike data

  • samplingTime: the width of time bin to use.

  • randomShift: flag to shift the sample in time or not. Default: False.

  • binningMode: the way spikes are binned. ‘SUM’ or ‘OR’ are supported. Default: ‘OR’

Usage:

>>> spike = TD.toSpikeTensor( torch.zeros((2, 240, 180, 5000)) )
slayerSNN.spikeFileIO.read1DnumSpikes(filename)[source]

Reads a tuple specifying neuron, start of spike region, end of spike region and number of spikes from binary spike file.

The binary file is encoded as follows:

  • Number of spikes data is represented by an 80 bit number.

  • First 16 bits (bits 79-64) represent the neuronID.

  • Next 24 bits (bits 63-40) represents the start time in microseconds.

  • Next 24 bits (bits 39-16) represents the end time in microseconds.

  • Last 16 bits (bits 15-0) represents the number of spikes.

Arguments:

  • filename (string): path to the binary file

Usage:

>>> nID, tSt, tEn, nSp = spikeFileIO.read1DnumSpikes(file_path)
``tSt`` and ``tEn`` are returned in milliseconds
slayerSNN.spikeFileIO.read1Dspikes(filename)[source]

Reads one dimensional binary spike file and returns a TD event.

The binary file is encoded as follows:

  • Each spike event is represented by a 40 bit number.

  • First 16 bits (bits 39-24) represent the neuronID.

  • Bit 23 represents the sign of spike event: 0=>OFF event, 1=>ON event.

  • the last 23 bits (bits 22-0) represent the spike event timestamp in microseconds.

Arguments:

  • filename (string): path to the binary file.

Usage:

>>> TD = spikeFileIO.read1Dspikes(file_path)
slayerSNN.spikeFileIO.read2Dspikes(filename)[source]

Reads two dimensional binary spike file and returns a TD event. It is the same format used in neuromorphic datasets NMNIST & NCALTECH101.

The binary file is encoded as follows:

  • Each spike event is represented by a 40 bit number.

  • First 8 bits (bits 39-32) represent the xID of the neuron.

  • Next 8 bits (bits 31-24) represent the yID of the neuron.

  • Bit 23 represents the sign of spike event: 0=>OFF event, 1=>ON event.

  • The last 23 bits (bits 22-0) represent the spike event timestamp in microseconds.

Arguments:

  • filename (string): path to the binary file.

Usage:

>>> TD = spikeFileIO.read2Dspikes(file_path)
slayerSNN.spikeFileIO.read3Dspikes(filename)[source]

Reads binary spike file for spike event in height, width and channel dimension and returns a TD event.

The binary file is encoded as follows:

  • Each spike event is represented by a 56 bit number.

  • First 12 bits (bits 56-44) represent the xID of the neuron.

  • Next 12 bits (bits 43-32) represent the yID of the neuron.

  • Next 8 bits (bits 31-24) represents the channel ID of the neuron.

  • The last 24 bits (bits 23-0) represent the spike event timestamp in microseconds.

Arguments:

  • filename (string): path to the binary file.

Usage:

>>> TD = spikeFileIO.read3Dspikes(file_path)
slayerSNN.spikeFileIO.readNpSpikes(filename, fmt='xypt', timeUnit=0.001)[source]

Reads numpy spike event and returns a TD event. The numpy array is assumed to be of nEvent x event diension.

Arguments:

  • filename (string): path to the file.

  • fmt (string): format of event. For e.g.’xypt’ means the event data is arrange in x data, y data, p data and time data.

  • timeUnit (double): factor to scale the time data to convert it into seconds. Default: 1e-3 (ms).

Usage:

>>> TD = spikeFileIO.readNpSpikes(file_path)
>>> TD = spikeFileIO.readNpSpikes(file_path, fmt='xypt')
>>> TD = spikeFileIO.readNpSpikes(file_path, timeUnit=1e-6)
slayerSNN.spikeFileIO.showTD(TD, fig=None, frameRate=24, preComputeFrames=True, repeat=False)[source]

Visualizes TD event.

Arguments:

  • TD: spike event to visualize.

  • fig: figure to plot animation. Default is None, in which case a figure is created.

  • frameRate: framerate of visualization.

  • preComputeFrames: flag to enable precomputation of frames for faster visualization. Default is True.

  • repeat: flag to enable repeat of animation. Default is False.

Usage:

>>> showTD(TD)
slayerSNN.spikeFileIO.spikeArrayToEvent(spikeMat, samplingTime=1)[source]

Returns TD event from a numpy array (of dimension 3 or 4). The numpy array must be of dimension (channels, height, time) or``CHT`` for 1D data. The numpy array must be of dimension (channels, height, width, time) or``CHWT`` for 2D data.

Arguments:

  • spikeMat: numpy array with spike information.

  • samplingTime: time width of each time bin.

Usage:

>>> TD = spikeFileIO.spikeArrayToEvent(spike)