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

Frontiers in Neuroscience

Claudio Mirasso¹  Fernando Maestú²  Ernesto Pereda³  Luis F. Antón-Toro⁴  Gianluca Susi⁵ 

[1] IUNE &CIBER-BBN: Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain;;Departamento de Ingeniería Industrial &Departamento de Psicología Experimental, Facultad de Psicología, Universidad Complutense de Madrid, Madrid, Spain;Department of Civil Engineering and Computer Science, University of Rome “Tor Vergata”, Rome, Italy;UPM-UCM Laboratory of Cognitive and Computational Neuroscience, Centro de Tecnologia Biomedica, Madrid, Spain;

关键词: classification;    delay learning;    MNSD;    online learning;    spike latency;    heterosynaptic plasticity;   

DOI  :  10.3389/fnins.2021.582608

来源: DOAJ

【 摘 要 】

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 this topology can be applied is limited because of 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 analyze 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 (MNIST database) obtaining state-of-the-art accuracies together with advantageous aspects in terms of time- and energy-efficiency if compared to similar classification methods.

【 授权许可】

Unknown