Results 41 to 50 of about 40,549 (223)

Diversity of intrinsic frequency encoding patterns in rat cortical neurons : mechanisms and possible functions [PDF]

open access: yes, 2010
Extracellular recordings of single neurons in primary and secondary somatosensory cortices of monkeys in vivo have shown that their firing rate can increase, decrease, or remain constant in different cells, as the external stimulus frequency increases ...
Jenkins, N   +4 more
core   +1 more source

Bayesian population decoding of spiking neurons [PDF]

open access: yesFrontiers in Computational Neuroscience, 2009
The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs.
Gerwinn, S., Macke, J., Bethge, M.
openaire   +5 more sources

Thermal Impact on Spiking Properties in Hodgkin-Huxley Neuron with Synaptic Stimulus

open access: yes, 2007
The effect of environmental temperature on neuronal spiking behaviors is investigated by numerically simulating the temperature dependence of spiking threshold of the Hodgkin-Huxley neuron subject to synaptic stimulus.
A. F. Huxley   +28 more
core   +2 more sources

Fitting Neuron Models to Spike Trains [PDF]

open access: yesFrontiers in Neuroscience, 2011
Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input-output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to ...
Cyrille eRossant   +10 more
openaire   +5 more sources

Large-scale Spatiotemporal Spike Patterning Consistent with Wave Propagation in Motor Cortex [PDF]

open access: yes, 2015
Aggregate signals in cortex are known to be spatiotemporally organized as propagating waves across the cortical surface, but it remains unclear whether the same is true for spiking activity in individual neurons.
Best, Matthew D.   +6 more
core   +2 more sources

Stochastic IMT (insulator-metal-transition) neurons: An interplay of thermal and threshold noise at bifurcation

open access: yes, 2018
Artificial neural networks can harness stochasticity in multiple ways to enable a vast class of computationally powerful models. Electronic implementation of such stochastic networks is currently limited to addition of algorithmic noise to digital ...
Datta, Suman   +3 more
core   +4 more sources

Brain-inspired nanophotonic spike computing: challenges and prospects

open access: yesNeuromorphic Computing and Engineering, 2023
Nanophotonic spiking neural networks (SNNs) based on neuron-like excitable subwavelength (submicrometre) devices are of key importance for realizing brain-inspired, power-efficient artificial intelligence (AI) systems with high degree of parallelism and ...
Bruno Romeira   +19 more
doaj   +1 more source

Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system

open access: yesFEBS 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

A neural circuit for navigation inspired by C. elegans Chemotaxis [PDF]

open access: yes, 2014
We develop an artificial neural circuit for contour tracking and navigation inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to harness the computational advantages spiking neural networks promise over their non-spiking ...
Rajendran, Bipin, Santurkar, Shibani
core  

Prospective Coding by Spiking Neurons

open access: yesPLOS Computational Biology, 2016
Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood.
Johanni Brea   +3 more
openaire   +4 more sources

Home - About - Disclaimer - Privacy