A computational paradigm for dynamic logic-gates in neuronal activity.

Amir Goldental

doi:10.3389/fncom.2014.00052

Abstract

In 1943 McCulloch and Pitts suggested that the brain is composed of reliable logic-gates similar to the logic at the core of today's computers. This framework had a limited impact on neuroscience, since neurons exhibit far richer dynamics. Here we propose a new experimentally corroborated paradigm in which the truth tables of the brain's logic-gates are time dependent, i.e., dynamic logic-gates (DLGs). The truth tables of the DLGs depend on the history of their activity and the stimulation frequencies of their input neurons. Our experimental results are based on a procedure where conditioned stimulations were enforced on circuits of neurons embedded within a large-scale network of cortical cells in-vitro. We demonstrate that the underlying biological mechanism is the unavoidable increase of neuronal response latencies to ongoing stimulations, which imposes a non-uniform gradual stretching of network delays. The limited experimental results are confirmed and extended by simulations and theoretical arguments based on identical neurons with a fixed increase of the neuronal response latency per evoked spike. We anticipate our results to lead to better understanding of the suitability of this computational paradigm to account for the brain's functionalities and will require the development of new systematic mathematical methods beyond the methods developed for traditional Boolean algebra.

Highlights

This year we are celebrating the 70th anniversary of the publication of the seminal work by Warren S
We proposed a new computational paradigm in which the brain consists of dynamic logic gates (DLGs) which are governed by time-dependent logic modes
The relevance of our work to the brain’s functionalities has to be evaluated using many aspects including: (a) Do dynamic logic-gates (DLGs) exist in the dynamics of a network of interconnected neurons? (b) Is the concept of DLGs robust to population dynamics and to recurrent networks? (c) Is DLGs a mechanism which the brain could plausibly use to any extent and especially when it is critically rely on precise relative timing of neural activities? (d) Can one find a realistic learning mechanism, e.g., Hebb’s rules, to implement DLGs?

Summary

Introduction

This year we are celebrating the 70th anniversary of the publication of the seminal work by Warren S. Results indicate that the neuronal response latency increases by a few μs per evoked spike, which represents a finer time scale of cortical dynamics, μs, as discussed at (Vardi et al, 2012a).

Results

Conclusion