Abstract: The present disclosure provides, method, a system, and apparatus for identifying odorants. For example, the apparatus performs sensing an odorant using an olfactory sensor, encoding the sensed odorant to an electrical signal using an input processor, determining an identity representation of the odorant based on the encoded electrical signal, and determining odorant information using a time-dependent hash code based on the identity representation of the odorant.

Abstract: The present disclosure provides, method, a system, and apparatus for identifying odorants. For example, the apparatus performs sensing an odorant using an olfactory sensor, encoding the sensed odorant to an electrical signal using an input processor, determining an identity representation of the odorant based on the encoded electrical signal, and determining odorant information using a time-dependent hash code based on the identity representation of the odorant.

Abstract: Systems and methods for channel identification, encoding and decoding signals, where the signals can have one or more dimensions, are disclosed. An exemplary method can include receiving the input signals and processing the input signals to provide a first output. The method can also encode the first output, at an asynchronous encoder, to provide encoded signals.

Abstract: Techniques for the identification of a spike-processing circuit are provided. An exemplary method includes receiving spike trains corresponding to a circuit input over a time period, and selecting a number of spikes for each of input spike trains over a predetermined time window. Each of the selected spikes can be replaced with a sampled reproducing kernel to obtain a plurality of signals, and each obtained signal can correspond to one of the input spike trains. Each of the obtained signals can be passed through a plurality of receptive fields or filters to obtain an aggregate filter output signal. The filter output signal can be encoded into an output spike train, and the output spike train can correspond to a response of the circuit to the plurality of input spike trains.

Abstract: Techniques for the identification of a spike-processing circuit are provided. An exemplary method includes receiving spike trains corresponding to a circuit input over a time period, and selecting a number of spikes for each of input spike trains over a predetermined time window. Each of the selected spikes can be replaced with a sampled reproducing kernel to obtain a plurality of signals, and each obtained signal can correspond to one of the input spike trains. Each of the obtained signals can be passed through a plurality of receptive fields or filters to obtain an aggregate filter output signal. The filter output signal can be encoded into an output spike train, and the output spike train can correspond to a response of the circuit to the plurality of input spike trains.

Abstract: Methods and systems for encoding and decoding signals using a Multi-input Multi-output Time Encoding Machine (TEM) and Time Decoding Machine are disclosed herein.

Abstract: Methods for decoding a signal encoded by a Time Encoding Machine (TEM) include defining a plurality of time-windows, each time-window corresponding to a portion of a TEM-encoded signal and made up of a plurality of trigger values, at least two of the time-windows overlapping, decoding each of the time-windows using a Time Decoding Machine (TDM) to generate a decoded time-window, and stitching the decoded time-windows together to generate a TEM-decoded signal.

Abstract: Techniques for reconstructing a signal encoded with a time encoding machine (TEM) using a recurrent neural network including receiving a TEM-encoded signal, processing the TEM-encoded signal, and reconstructing the TEM-encoded signal with a recurrent neural network.

Abstract: Systems and methods for system identification, encoding and decoding multi-component signals are disclosed. An exemplary method can include receiving the one or more multi-component signals and separating the multi-component signals into channels. The method can also filter each of the channels using receptive field components. The values for the receptive field components can be aggregated and provided to neurons for encoding.

Abstract: Systems and methods for system identification, encoding and decoding signals in a non-linear system are disclosed. An exemplary method can include receiving the one or more input signals and performing dendritic processing on the input signals. The method can also encode the output of the dendritic processing of the input signals, at a neuron, to provide encoded signals.

Abstract: Systems for untethered sensing and recording of activity of one or more electrically excitable cells in a target region includes at least one untethered probe. Each untethered probe includes at least one signal detector, configured to electrically couple to the target region, measure the activity of the one or more electrically excitable cells, and produce an electrical signal in response to the activity of the one or more electrically excitable cells, and at least one light source, electrically coupled to the at least one signal detector, to receive the electrical signal and emit a light signal representing the activity of the one or more electrically excitable cells. Methods for untethered sensing and recording of activity of one or more electrically excitable cells in a target region are also provided.

Abstract: Techniques for reconstructing a signal encoded with a time encoding machine (TEM) using a recurrent neural network including receiving a TEM-encoded signal, processing the TEM-encoded signal, and reconstructing the TEM-encoded signal with a recurrent neural network.

Abstract: Method for identification of at least one parameter of a sampling system includes transmitting at least one input signal to at least one channel of the sampling system; measuring at least one output signal of the sampling system in response to sampling of the at least one input signal by the receiver; and determining, using a processor, the at least one parameter of the sampling system using the at least one input signal and the at least one output signal of the sampling system. A system for identification of at least one parameter relating to a sampling system in response to at least one input signal is also provided.

Abstract: Methods and systems for encoding and decoding signals using a Multi-input Multi-output Time Encoding Machine (TEM) and Time Decoding Machine are disclosed herein.

Abstract: Methods and systems for encoding and decoding signals using a Multi-input Multi-output Time Encoding Machine (TEM) and Time Decoding Machine are disclosed herein.

Abstract: Methods for decoding a signal encoded by a Time Encoding Machine (TEM) include defining a plurality of time-windows, each time-window corresponding to a portion of a TEM-encoded signal and made up of a plurality of trigger values, at least two of the time-windows overlapping, decoding each of the time-windows using a Time Decoding Machine (TDM) to generate a decoded time-window, and stitching the decoded time-windows together to generate a TEM-decoded signal.

Abstract: Methods and systems for encoding and decoding signals using a Multi-input Multi-output Time Encoding Machine (TEM) and Time Decoding Machine are disclosed herein.

Abstract: Analog signals can be fully encoded as an asynchronous time sequence generated by a time encoding machine (TEM). With knowledge of the parameters of the time encoding machine, the asynchronous time sequence can be decoded using a non-linear time decoding machine. In one embodiment, the non-linear time decoding machine generates a set of weighted Dirac delta functions centered in the intervals of the asynchronous time sequence of the time encoding machine output. The weighting of each of the delta functions is determined by the parameters of the TEM as well as the values of the time sequence. The generation of the series of delta functions is a nonlinear operation (500, 510, 520). The input signal can be recovered from the series of weighted delta functions by (530) applying this series through an impulse response filter.

Abstract: Analog signals can be fully encoded as an asynchronous time sequence generated by a time encoding machine. With knowledge of the parameters of the time encoding machine, the asynchronous time sequence can be decoded using a non-linear time decoding machine. Such a system can be extended into an encoder/decoder in which a signal is processed in M separate channels. An input signal is applied to the encoder where the signal is provided to an M channel encoder circuit including a filter bank having a total bandwidth partitioned among M adjacent or overlapping filters. Each of the M filters are coupled to a corresponding one of M time encoding machines. The encoder output is represented by M sets of time encoded trigger values. The input signal can be recovered from the M sets of time encoded trigger values by applying the trigger signals to a corresponding M channel decoder which includes M TDMs and filters. The TDMs recover the continuous signal from each channel.

Abstract: Analog signals can be fully encoded as an asynchronous time sequence generated by a time encoding machine. With knowledge of the parameters of the time encoding machine, the asynchronous time sequence can be decoded using a non-linear time decoding machine. Such a system can be extended into an encoder/decoder in which a signal is processed in M separate channels. An input signal is applied to the encoder where the signal is provided to an M channel encoder circuit including a filter bank having a total bandwidth partitioned among M adjacent or overlapping filters. Each of the M filters are coupled to a corresponding one of M time encoding machines. The encoder output is represented by M sets of time encoded trigger values. The input signal can be recovered from the M sets of time encoded trigger values by applying the trigger signals to a corresponding M channel decoder which includes M TDMs and filters. The TDMs recover the continuous signal from each channel.