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.