Results 101 to 110 of about 68,442 (287)
Phase diagram of spiking neural networks [PDF]
In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2\%, 20\% of neurons are inhibitory and 80\% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I
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A MXene/PEDOT coating enables multimodal functionality and dual‐analyte detection of dopamine and serotonin in flexible microelectrode arrays while enhancing electrophysiological recording quality. The anti‐fouling, low‐impedance interface overcomes key limitations of conventional coatings, providing a robust and versatile platform to investigate the ...
Ilaria Gatti +8 more
wiley +1 more source
Hydrogen‐Bond–Driven Ion Retention in Electrolyte‐Gated Synaptic Transistors
Anion molecular design governs ion–polymer interactions in electrolyte‐gated synaptic transistors. Asymmetric anions induce hydrogen‐bond interactions that suppress ion back‐diffusion and stabilize doping, enabling enhanced nonvolatile synaptic properties.
Donghwa Lee +5 more
wiley +1 more source
High-performance deep spiking neural networks with 0.3 spikes per neuron
Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks.
Ana Stanojevic +5 more
doaj +1 more source
Self‐Healing and Stretchable Synaptic Transistor
A self‐healing stretchable synaptic transistor (3S‐T) is realized using a p‐PVDF‐HFP‐DBP/PDMS‐MPU‐IU bilayer as gate insulator, where dipole‐dipole interaction enhances polarization to achieve a large memory window. Leveraging its neuronal biomimicry, the synaptic transistor demonstrates electrically compatibility with the biological brain. Furthermore,
Hyongsuk Choo +10 more
wiley +1 more source
Implementing Signature Neural Networks with Spiking Neurons
Spiking Neural Networks constitute the most promising approach to develop realistic ArtificialNeural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding inspiking models is based on the precise timing of individual spikes.
José Luis Carrillo-Medina +1 more
doaj +1 more source
Cortical neural circuits display highly irregular spiking in individual neurons but variably sized collective firing, oscillations and critical avalanches at the population level, all of which have functional importance for information processing ...
Junhao Liang +4 more
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Wafer‐scale two‐dimensioanl In2Se3 oxidized into InOx on sodium‐embedded beta‐alumina enables multifunctional reconfigurable electronics. Sodium ions accumulate within distinct spatial distribution under drain‐controlle and gate‐controlled operation. Drain‐control operation gives controllability of ultraviolet‐driven optoelectronic synaptic conductance
Jinhong Min +13 more
wiley +1 more source
SNNAX - Spiking Neural Networks in JAX
Spiking Neural Networks (SNNs) simulators are essential tools to prototype biologically inspired models and neuromorphic hardware architectures and predict their performance. For such a tool, ease of use and flexibility are critical, but so is simulation speed especially given the complexity inherent to simulating SNN.
Lohoff, Jamie +2 more
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Propagation of Spike Sequences in Neural Networks [PDF]
Precise spatiotemporal sequences of action potentials are observed in many brain areas and are thought to be involved in the neural processing of sensory stimuli. Here, we examine the ability of spiking neural networks to propagate stably a spatiotemporal sequence of spikes in the limit where each neuron fires only one spike.
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