Brief. Bioinformatics - Real-time classification of datasets with hardware embedded neuromorphic neural networks.

Tópicos

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Resumo

Neuromorphic artificial neural networks attempt to understand the essential computations that take place in the dense networks of interconnected neurons making up the central nervous systems in living creatures. This article demonstrates that artificial spiking neural networks--built to resemble the biological model--encoding information in the timing of single spikes, are capable of computing and learning clusters from realistic data. It shows how a spiking neural network based on spike-time coding can successfully perform unsupervised and supervised clustering on real-world data. A temporal encoding procedure of continuously valued data is developed, together with a hardware implementation oriented new learning rule set. Solutions that make use of embedded soft-core microcontrollers are investigated, to implement some of the most resource-consuming components of the artificial neural network. Details of the implementations are given, with benchmark application evaluation and test bench description. Measurement results are presented, showing real-time and adaptive data processing capabilities, comparing these to related findings in the specific literature.

Resumo Limpo

neuromorph artifici neural network attempt understand essenti comput take place dens network interconnect neuron make central nervous system live creatur articl demonstr artifici spike neural networksbuilt resembl biolog modelencod inform time singl spike capabl comput learn cluster realist data show spike neural network base spiketim code can success perform unsupervis supervis cluster realworld data tempor encod procedur continu valu data develop togeth hardwar implement orient new learn rule set solut make use embed softcor microcontrol investig implement resourceconsum compon artifici neural network detail implement given benchmark applic evalu test bench descript measur result present show realtim adapt data process capabl compar relat find specif literatur

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