ArticleFrontiers in neuroscience2018
A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity.Biology · 2021Article
- FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency.Scientific reports · 2021Article
- Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers.Biomimetics (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors.
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Abstract
Humans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired in brain circuits, have been designed and used to tackle spatio-temporal pattern recognition tasks. In this paper we present a multi-neuronal spike pattern detection structure able to autonomously implement online learning and recognition of parallel spike sequences (i.e., sequences of pulses belonging to different neurons/neural ensembles). The operating principle of this structure is based on two spiking/synaptic neurocomputational characteristics:
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