This paper presents a spiking neuro-evolutionary system which implements memristors as neuromodulatory connections, i.e. whose weights can vary during a ...
Abstract— This paper presents a spiking neuro-evolutionary system which implements memristors as neuromodulatory connections, i.e. whose weights can vary ...
This paper presents a spiking neuro-evolutionary system which implements memristors as neuromodulatory connections, i.e. whose weights can vary during a trial.
May 17, 2015 · A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks.
Aug 1, 2011 · This paper presents a spiking neuro-evolutionary system which implements memristors as neuromodulatory connections, i.e. whose weights can ...
Towards evolving spiking networks with memristive synapses (2011). First ... Attributed to: Learning and computation in disordered networks of memristors: theory ...
In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic ...
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Sep 28, 2023 · In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model.
Feb 25, 2020 · We report on memristive links between brain and silicon spiking neurons that emulate transmission and plasticity properties of real synapses.