Neuromorphic circuit

Abstract

Embodiments of the present invention are directed to neuromorphic circuits containing two or more internal neuron computational units. Each internal neuron computational unit includes a synchronization-signal input for receiving a synchronizing signal, at least one input for receiving input signals, and at least one output for transmitting an output signal. A memristive synapse connects an output signal line carrying output signals from a first set of one or more internal neurons to an input signal line that carries signals to a second set of one or more internal neurons.

Description

[0001] Cross References to Related Applications [0002] This application claims the benefit of Provisional Application No. 61 / 036,864, filed March 14, 2008. technical field [0003] The present invention relates to electronic devices and computer hardware, and in particular to methods for machine learning through changes in the physical properties of synapse-like junctions in neuromorphic circuits, and systems for performing such machine learning. Background technique [0004] Early in the history of computing, computer scientists became interested in biological computing structures, including the human brain. Although sequential instruction processing engines have evolved technologically at a rapid rate over the past 50 years, with enormous increases in processor speed and component density, although these advances have been accompanied by mass storage devices and random access memory even greater increases in capacity and access speed, and while modern computer systems b...

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Problems solved by technology

In many of these proposed implementations, the overall circuit ends up being constrained by the physical properties of the memristive junction, and undesired levels of power consumption are often encountered and difficult to ameliorate

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