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 and in the representation of analog waveforms in information systems; examples of TEMs arising in neural encoding include models of retinas, cochleas, and olfactory systems. There is also considerable interest in the use of TEMs as front ends of brain machine interfaces, i.e., as building blocks connecting biological and silicon-based information 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 can be achieved. Faithful recovery of natural video and auditory signals encoded by receptive fields in cascade with neural circuits comprising canonical neuron models (such as Integrate-and-Fire, Threshold-and-Fire, and Hodgkin-Huxley) has also been demonstrated.

The Time Encoding and Decoding Toolbox contains Python, PyCUDA, and MATLAB implementations of a selection of single-input single-output, single-input multi-output, and multi-input multi-output TEMs and corresponding TDMs. These include encoding circuits that use classical spiking neuron models, employ feedback, use random thresholds, or use Asynchronous Sigma/Delta Modulators.