Assembly-based STDP: A New Learning Rule for Spiking Neural Networks Inspired by Biological Assemblies
Spiking Neural Networks (SNNs), An alternative to sigmoidal neural networks, include time
into their operations using discrete signals called spikes. Employing spikes enables SNNs to …
into their operations using discrete signals called spikes. Employing spikes enables SNNs to …
Enhancing learning in artificial neural networks through cellular heterogeneity and neuromodulatory signaling
Recent progress in artificial intelligence (AI) has been driven by insights from neuroscience,
particularly with the development of artificial neural networks (ANNs). This has significantly …
particularly with the development of artificial neural networks (ANNs). This has significantly …
Evolutionary FPGA-Based Spiking Neural Networks for Continual Learning
A Otero, G Sanllorente, E de la Torre… - … Symposium on Applied …, 2023 - Springer
Abstract Spiking Neural Networks (SNNs) constitute a representative example of
neuromorphic computing in which event-driven computation is mapped to neuron spikes …
neuromorphic computing in which event-driven computation is mapped to neuron spikes …
Deep Predictive Coding with Bi-directional Propagation for Classification and Reconstruction
This paper presents a new learning algorithm, termed Deep Bi-directional Predictive Coding
(DBPC) that allows developing networks to simultaneously perform classification and …
(DBPC) that allows developing networks to simultaneously perform classification and …
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A Otero¹, G Sanllorente¹, E de la Torre¹… - … , ARC 2023, Cottbus …, 2023 - books.google.com
Spiking Neural Networks (SNNs) constitute a representative example of neuromorphic
computing in which event-driven computation is mapped to neuron spikes reducing power …
computing in which event-driven computation is mapped to neuron spikes reducing power …