Results 21 to 30 of about 60,411 (286)
Introducing the Dendrify framework for incorporating dendrites to spiking neural networks
Nature Communications, 2023 Biologically inspired spiking neural networks are highly promising, but remain simplified omitting relevant biological details. The authors introduce here theoretical and numerical frameworks for incorporating dendritic features in spiking neural ...
Michalis Pagkalos +2 more
doaj +1 more source
Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays [PDF]
, 2012 Sheik S, Chicca E, Indiveri G. Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays. Presented at the International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia.Axonal delays are used in neural ...
Chicca, Elisabetta +2 more
core +4 more sources
Multitask computation through dynamics in recurrent spiking neural networks
Scientific Reports, 2023 In this work, inspired by cognitive neuroscience experiments, we propose recurrent spiking neural networks trained to perform multiple target tasks.
Mechislav M. Pugavko +2 more
doaj +1 more source
Bifurcation Spiking Neural Network
J. Mach. Learn. Res., 2019 Spiking neural networks (SNNs) has attracted much attention due to its great potential of modeling time-dependent signals. The firing rate of spiking neurons is decided by control rate which is fixed manually in advance, and thus, whether the firing rate is adequate for modeling actual time series relies on fortune.
Shao-Qun Zhang +2 more
openaire +4 more sources
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
Frontiers in Neuroinformatics, 2018 The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms.
Hananel Hazan +6 more
doaj +1 more source
Learning Universal Computations with Spikes. [PDF]
PLoS Computational Biology, 2016 Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the ...
Dominik Thalmeier +3 more
doaj +1 more source
Models developed for spiking neural networks
MethodsX, 2023 Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these networks are inspired by the brain, they lack biological plausibility, and they have structural differences compared ...
Shahriar Rezghi Shirsavar +2 more
openaire +4 more sources
Targeting operational regimes of interest in recurrent neural networks.
PLoS Computational Biology, 2023 Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons.
Pierre Ekelmans +2 more
doaj +1 more source
Neural Sampling by Irregular Gating Inhibition of Spiking Neurons and Attractor Networks [PDF]
, 2016 A long tradition in theoretical neuroscience casts sensory processing in the brain as the process of inferring the maximally consistent interpretations of imperfect sensory input.
Indiveri, Giacomo, Muller, Lorenz K.
core +1 more source
Stochasticity from function -- why the Bayesian brain may need no noise [PDF]
, 2019 An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing.
Baumbach, Andreas +8 more
core +2 more sources