A neuromorphic architecture for object recognition and motion anticipation using burst-STDP.
In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct mot...
Main Authors: | Andrew Nere, Umberto Olcese, David Balduzzi, Giulio Tononi |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2012-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3352850?pdf=render |
Similar Items
-
STDP and STDP Variations with Memristors for Spiking Neuromorphic Learning Systems
by: Teresa eSerrano-Gotarredona, et al.
Published: (2013-02-01) -
An Adaptive STDP Learning Rule for Neuromorphic Systems
by: Ashish Gautam, et al.
Published: (2021-09-01) -
Low‐power hybrid memristor‐CMOS spiking neuromorphic STDP learning system
by: Gabriel Maranhão, et al.
Published: (2021-05-01) -
SPICE Study of STDP Characteristics in a Drift and Diffusive Memristor-Based Synapse for Neuromorphic Computing
by: Suman Hu, et al.
Published: (2022-01-01) -
A Neuromorphic System for Video Object Recognition
by: Deepak eKhosla, et al.
Published: (2014-11-01)