Results 51 to 60 of about 52,867 (269)
Modeling rhythmic patterns in the hippocampus
, 2012 We investigate different dynamical regimes of neuronal network in the CA3 area of the hippocampus. The proposed neuronal circuit includes two fast- and two slowly-spiking cells which are interconnected by means of dynamical synapses.
A. I. Lavrova +7 more
core +1 more source
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor [PDF]
, 2018 Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building ...
Glatz, Sebastian +4 more
core +1 more source
Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron [PDF]
International Journal of Neural Systems, 2020 We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carries information.
Kheradpisheh, Saeed Reza +1 more
openaire +3 more sources
, 2017 We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from ...
Paraskevov, A. V., Zendrikov, D. K.
core +1 more source
Synaptic plasticity enables adaptive self-tuning critical networks. [PDF]
PLoS Computational Biology, 2015 During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous.
Nigel Stepp +2 more
doaj +1 more source
Integration of continuous-time dynamics in a spiking neural network simulator
, 2016 Contemporary modeling approaches to the dynamics of neural networks consider two main classes of models: biologically grounded spiking neurons and functionally inspired rate-based units.
Bolten, Matthias +6 more
core +1 more source
Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses [PDF]
, 2011 In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion.
A Aertsen +50 more
core +1 more source
RIPK4 function interferes with melanoma cell adhesion and metastasis
Molecular Oncology, EarlyView.RIPK4 promotes melanoma growth and spread. RIPK4 levels increase as skin lesions progress to melanoma. CRISPR/Cas9‐mediated deletion of RIPK4 causes melanoma cells to form less compact spheroids, reduces their migratory and invasive abilities and limits tumour growth and dissemination in mouse models.
Norbert Wronski +9 more
wiley +1 more source
Implementing Signature Neural Networks with Spiking Neurons
Frontiers in Computational Neuroscience, 2016 Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes.
Carrillo-Medina, José Luis +1 more
openaire +4 more sources
Brain Sciences, 2022 The Locus coeruleus (LC) modulates various neuronal circuits throughout the brain. Its unique architectural organization encompasses a net of axonal innervation that spans the entire brain, while its somatic core is highly compact.
Shristi Baral +5 more
doaj +1 more source