Time Encoding and Decoding Toolbox
Time Encoding Machines (TEMs) are asynchronous signal processors that encode analog information in the time domain. TEMs play a key role in modeling the sensing of the natural world by biological sensory systems.
Time Decoding Machines (TDMs) recover stimuli encoded as time (spike) sequences by TEMs. Assuming that Nyquist-type rate conditions are satisfied by the encoding architecture, arbitrarily precise stimulus recovery of time-encoded 1D and 2D (video) signals can be achieved.
The Time Encoding and Decoding Toolbox contains Python implementations of a selection of SISO, SIMO, and MIMO TEMs and corresponding TDMs that include encoding circuits utilizing classical neuron models (LIF, Hodgkin-Huxley, etc.), feedback, random thresholds, and/or Asynchronous Sigma/Delta Modulators. These can be used to develop new models of information encoding and processing in neural circuits.
Time Decoding Machines (TDMs) recover stimuli encoded as time (spike) sequences by TEMs. Assuming that Nyquist-type rate conditions are satisfied by the encoding architecture, arbitrarily precise stimulus recovery of time-encoded 1D and 2D (video) signals can be achieved.
The Time Encoding and Decoding Toolbox contains Python implementations of a selection of SISO, SIMO, and MIMO TEMs and corresponding TDMs that include encoding circuits utilizing classical neuron models (LIF, Hodgkin-Huxley, etc.), feedback, random thresholds, and/or Asynchronous Sigma/Delta Modulators. These can be used to develop new models of information encoding and processing in neural circuits.
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