Results 21 to 30 of about 215,411 (303)
, 2006 Simulating large networks of spiking neurons is a very common task in the areas of Neuroinformatics and Computational Neurosciences. These simulations are time-consuming but also often intrinsically parallel. The recent advent of powerful and programmable graphic cards seems to be a pertinent solution to the problem: they offer a cheap but efficient ...
Fabrice Bernhard, Renaud Keriven
openaire +2 more sources
Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
Frontiers in Neuroinformatics, 2015 Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging ...
Eoin Patrick Lynch +2 more
doaj +1 more source
Systematic construction of finite state automata using VLSI spiking neurons [PDF]
, 2012 Spiking neural networks implemented using electronic Very Large Scale Integration (VLSI) circuits are promising information processing architectures for carrying out complex cognitive tasks in real-world applications.
Neftci, Emre +14 more
core +1 more source
Independent component analysis in spiking neurons [PDF]
, 2010 Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such ...
Cristina Savin +8 more
core +2 more sources
IEEE Access, 2020 In this second part of a two-part study, we extend to nonlinear synaptic responses a new framework, called Response Surfaces (RSs), for analyzing, designing, and visualizing spiking neurons and networks.
Fatemeh Koohestan-Mahalian +1 more
doaj +1 more source
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
What Causes a Neuron to Spike? [PDF]
Neural Computation, 2003 The computation performed by a neuron can be formulated as a combination of dimensional reduction in stimulus space and the nonlinearity inherent in a spiking output. White noise stimulus and reverse correlation (the spike-triggered average and spike-triggered covariance) are often used in experimental neuroscience to “ask” neurons which dimensions in
Blaise Agüera y Arcas +1 more
openaire +5 more sources
A systematic method for configuring VLSI networks of spiking neurons [PDF]
, 2011 Neftci E, Chicca E, Indiveri G, Douglas RJ. A systematic method for configuring VLSI networks of spiking neurons. Neural Computation. 2011;23(10):2457-2497.An increasing number of research groups are developing custom hybrid analog/digital very large ...
Rodney Douglas +11 more
core +1 more source
A biologically inspired spiking model of visual processing for image feature detection [PDF]
, 2015 To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence edge or corner features, commonly called interest points are often used for this purpose.
Kerr, D +3 more
core +1 more source
npj 2D Materials and Applications, 2023 Accurate, timely and selective detection of moving obstacles is crucial for reliable collision avoidance in autonomous robots. The area- and energy-inefficiency of CMOS-based spiking neurons for obstacle detection can be addressed through the ...
Kartikey Thakar +2 more
doaj +1 more source