PROJECT TITLE :

A Neuromorphic Event-Based Neural Recording System for Smart Brain-Machine-Interfaces

ABSTRACT:

Neural recording systems are a central element of Brain-Machince Interfaces (BMIs). In most of these systems the emphasis is on trustworthy reproduction and transmission of the recorded signal to remote systems for additional processing or information analysis. Here we have a tendency to follow an alternative approach: we propose a neural recording system which will be directly interfaced locally to neuromorphic spiking neural processing circuits for compressing the massive amounts of information recorded, completing signal processing and neural computation to extract relevant info, and transmitting only the low-bandwidth outcome of the processing to remote computing or actuating modules. The fabricated system includes a low-noise amplifier, a delta-modulator analog-to-digital converter, and an occasional-power band-pass filter. The bio-amplifier features a programmable gain of forty five–54 dB, with a Root Mean Squared (RMS) input-referred noise level of two.one $mu rm V$, and consumes 90 $mu rm W$ . The band-pass filter and delta-modulator circuits embrace asynchronous handshaking interface logic compatible with event-based communication protocols. We tend to describe the properties of the neural recording circuits, validating them with experimental measurements, and present system-level application examples, by interfacing these circuits to a reconfigurable neuromorphic processor comprising an array of spiking neurons with plastic and dynamic synapses. The pool of neurons within the neuromorphic processor was configured to implement a recurrent neural network, and to process the events generated by the neural recording system in order to hold out pattern recognition.