Exploiting device mismatch in neuromorphic VLSI systems to implement axonal delays [PDF]
, 2012 Axonal delays are used in neural computation to implement faithful models of biological neural systems, and in spiking neural networks models to solve computationally demanding tasks. While there is an increasing number of software simulations of spiking
Sadique Sheik +8 more
core +1 more source
Sa-SNN: spiking attention neural network for image classification [PDF]
PeerJ Computer ScienceSpiking neural networks (SNNs) are known as third generation neural networks due to their energy efficient and low power consumption. SNNs have received a lot of attention due to their biological plausibility. SNNs are closer to the way biological neural
Yongping Dan +3 more
doaj +2 more sources
Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
Sensors, 2021 This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent ...
Moshe Bensimon +2 more
doaj +1 more source
A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity [PDF]
, 2006 Indiveri G, Chicca E, Douglas RJ. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Transactions on Neural Networks.
Rodney Douglas +5 more
core +1 more source
Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update
Frontiers in Computational Neuroscience, 2021 Among many artificial neural networks, the research on Spike Neural Network (SNN), which mimics the energy-efficient signal system in the brain, is drawing much attention.
Taeyoon Kim +8 more
semanticscholar +1 more source
Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network [PDF]
International Joint Conference on Artificial Intelligence, 2020 The recently discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However designing large-scale and high-performance model is yet a challenge due
Haowen Fang +3 more
semanticscholar +1 more source
Goodness-of-fit tests for neural population models: the multivariate time-rescaling theorem [PDF]
, 2010 Poster Presentation from Nineteenth Annual Computational Neuroscience Meeting: CNS*2010 San Antonio, TX, USA. 24-30 July 2010 Statistical models of neural activity are at the core of the field of modern computational neuroscience.
Haslinger Robert +8 more
core +2 more sources
Equivalence of Additive and Multiplicative Coupling in Spiking Neural Networks
IEEE Access, 2023 Spiking neural network models characterize the emergent collective dynamics of circuits of biological neurons and help engineer neuro-inspired solutions across fields. Most dynamical systems’ models of spiking neural networks typically exhibit one
Georg Borner +2 more
doaj +1 more source
An Adaptive Optimization Spiking Neural P System for Binary Problems [PDF]
, 2021 © 2020 World Scientific Publishing Company. Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack problem ...
Neri, Ferrante +7 more
core +1 more source
Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source