TR06-138 Authors: Kei Uchizawa, Rodney Douglas

Publication: 15th November 2006 10:15

Downloads: 2759

Keywords:

Circuits composed of threshold gates (McCulloch-Pitts neurons, or

perceptrons) are simplified models of neural circuits with the

advantage that they are theoretically more tractable than their

biological counterparts. However, when such threshold circuits are

designed to perform a specific computational task they usually

differ in one important respect from computations in the brain: they

require very high activity. On average every second threshold gate

fires (sets a "1" as output) during a computation. By contrast,

the activity of neurons in the brain is much more sparse, with only

about 1% of neurons firing. This mismatch between threshold and

neuronal circuits is due to the particular complexity measures

(circuit size and circuit depth) that have been minimized in

previous threshold circuit constructions. In this article we

investigate a new complexity measure for threshold circuits,

energy complexity, whose minimization yields computations with

sparse activity. We prove that all computations by threshold

circuits of polynomial size with entropy O(log n) can be

restructured so that their energy complexity is reduced to a level

near the entropy of circuit states. This entropy of circuit

states is a novel circuit complexity measure, which is of interest

not only in the context of threshold circuits, but for circuit

complexity in general. As an example of how this measure can be

applied we show that any polynomial size threshold circuit with

entropy O(log n) can be simulated by a polynomial size threshold

circuit of depth 3.