AUTHOR=Susi Gianluca , Antón-Toro Luis F. , Maestú Fernando , Pereda Ernesto , Mirasso Claudio 

TITLE=nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.582608

DOI=10.3389/fnins.2021.582608

ISSN=1662-453X

ABSTRACT=The recent 'multi-neuronal spike sequence detector' (MNSD) architecture integrates the weight- and delay-adjustment methods by combining heterosynaptic plasticity with the neurocomputational feature spike latency, representing a new opportunity to understand the mechanisms underlying biological learning.
Unfortunately the range of problems to which such topology can be applied is limited, due to the low cardinality of the parallel spike trains that it can process, and the lack of a visualization mechanism to understand its internal operation.
We present here the nMNSD structure, which is a generalization of the MNSD to any number of inputs. The mathematical framework of the structure is introduced, together with the 'trapezoid method', that is a reduced method to analyse the recognition mechanism operated by the nMNSD in response to a specific input parallel spike train.\\
We apply the nMNSD to a classification problem previously faced with the classical MNSD from the same authors, showing the new possibilities the nMNSD opens, with associated improvement in classification performances. Finally, we benchmark the nMNSD on the classification of static inputs obtaining state of the art accuracies together with advantageous aspects in terms of time- and energy- efficiency if compared to similar classification methods.