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Single-unit In vivo Recordings from the Optic Chiasm of Rat

Single-unit In vivo Recordings from the Optic Chiasm of Rat

Published on: April 2, 2010

A multichip neuromorphic system for spike-based visual information processing.

R Jacob Vogelstein¹, Udayan Mallik, Eugenio Culurciello

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA. jvogelst@jhu.edu

Neural Computation

|July 26, 2007

Summary

This study introduces a novel neuromorphic system for vision processing, featuring a silicon retina and cortex. It demonstrates efficient object recognition using reconfigurable neural networks and dynamic synapse configuration.

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Area of Science:

Neuromorphic Engineering
Computer Vision
Artificial Intelligence

Background:

Current vision processing systems often lack the efficiency and adaptability of biological systems.
Spike-based processing offers a promising alternative for low-power, high-performance computation.
Neuromorphic hardware aims to emulate the structure and function of the brain for advanced AI tasks.

Purpose of the Study:

To develop and demonstrate a multichip, mixed-signal Very Large Scale Integration (VLSI) system for spike-based vision processing.
To implement and test components of an attention-based hierarchical model for cortical object recognition.
To showcase the system's capability for dynamic reconfiguration of neural connectivity and synaptic parameters.

Main Methods:

Designed an 80 x 60 pixel neuromorphic retina integrated with a 4800 neuron silicon cortex.
Implemented asynchronous routing of neural spike events using a memory-based look-up table for reconfigurable connectivity.
Utilized memory storage for dynamic configuration of synaptic parameters like conductance and reversal potential.

Main Results:

Successfully demonstrated functionality with experimental data on feature coding, salience detection, and foveation.
Validated the system's ability to support arbitrary and reconfigurable neural network architectures.
Showcased dynamic configuration of synapse circuits, enabling flexible and adaptive processing.

Conclusions:

The developed multichip VLSI system provides a powerful platform for advanced spike-based vision processing.
The reconfigurable architecture and dynamic synapse configuration enable efficient emulation of cortical functions.
This work advances the development of neuromorphic systems for complex tasks like object recognition.