A signed spatial contrast event spike retina chip

We couple a previously studied, biologically inspired neurotrophic model of activity-dependent competitive synaptic plasticity and neuronal development to a neuromorphic retina chip. Using this system, we examine the development and refinement of a topographic mapping between an array of afferent neurons (the retinal ganglion cells) and an array of target neurons. We find that the plasticity model can indeed drive topographic refinement in the presence of afferent activity patterns generated by a real-world device. We examine the resilience of the developing system to the presence of high levels of noise by adjusting the spontaneous firing rate of the silicon neurons.

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Implementation of Visual Tracking System using Artificial Retina Chip and Shape Memory Alloy Actuator

Neural Information Processing: Research and Development - Studies in Fuzziness and Soft Computing ◽

10.1007/978-3-540-39935-3_25 ◽

2004 ◽

pp. 460-477

Author(s):

W. C. Kim ◽

M. Lee ◽

J. K. Shin ◽

H. S. Yang

Keyword(s):

Shape Memory Alloy ◽

Shape Memory ◽

Visual Tracking ◽

Tracking System ◽

Artificial Retina ◽

Shape Memory Alloy Actuator ◽

Retina Chip

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Analog Learning Neural Network using Two-Stage Mode by Multiple and Sample Hold Circuits

International Journal of Software Innovation ◽

10.4018/ijsi.2014010105 ◽

2014 ◽

Vol 2 (1) ◽

pp. 61-72 ◽

Cited By ~ 1

Author(s):

Masashi Kawaguchi ◽

Naohiro Ishii ◽

Takashi Jimbo

Keyword(s):

Neural Network ◽

Vision System ◽

Network Models ◽

Input Voltage ◽

Neural Network Models ◽

Artificial Retina ◽

The Neural Network ◽

Connection Coefficient ◽

Retina Chip ◽

Short Time

In the neural network field, many application models have been proposed. A neuro chip and an artificial retina chip are developed to comprise the neural network model and simulate the biomedical vision system. Previous analog neural network models were composed of the operational amplifier and fixed resistance. It is difficult to change the connection coefficient. In this study, we used analog electronic multiple and sample hold circuits. The connecting weights describe the input voltage. It is easy to change the connection coefficient. This model works only on analog electronic circuits. It can finish the learning process in a very short time and this model will enable more flexible learning.

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