Spatially distinct, self-sustained oscillations in artificial neural networks are fundamental to information encoding, storage, and processing in these systems. Here, we develop a method to induce a large variety of self-sustained oscillatory pattern...
This paper is dedicated to exploring the global Mittag-Leffler stability of fractional-order complex-valued (CV) neural networks (NNs) with asynchronous time delays, which generates exponential stability of integer-order (IO) CVNNs. Here, asynchronou...
In this article, the standard theoretical model accounting for a double barrier quantum well resonant tunneling diode (RTD) connected to a direct current source of voltage is simplified by representing its current-voltage characteristic with an analy...
We propose an object recognition architecture relying on a neural network algorithm in optical sensors. Precisely, by applying the high-speed and low-power Fourier transform operation in the optical domain, we can transfer the high-cost part of the t...
Frontiers in bioscience (Landmark edition)
Oct 30, 2021
: Ever since the seminal work by McCulloch and Pitts, the theory of neural computation and its philosophical foundation known as 'computationalism' have been central to brain-inspired artificial intelligence (AI) technologies. The present study descr...
Our real-time actions in everyday life reflect a range of spatiotemporal dynamic brain activity patterns, the consequence of neuronal computation with spikes in the brain. Most existing models with spiking neurons aim at solving static pattern recogn...
In this issue of Neuron, Bonnen et al. (2021) use artificial neural networks to resolve a long-standing controversy surrounding the neurocognitive dichotomy between memory and perception. They show that the perirhinal cortex supports performance on t...
A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity prope...
Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), ...
Learning new concepts rapidly from a few examples is an open issue in spike-based machine learning. This few-shot learning imposes substantial challenges to the current learning methodologies of spiking neuron networks (SNNs) due to the lack of task-...
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