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Items: 1 to 20 of 125

1.

Event-driven neural integration and synchronicity in analog VLSI.

Yu T, Park J, Joshi S, Maier C, Cauwenberghs G.

Conf Proc IEEE Eng Med Biol Soc. 2012;2012:775-8. doi: 10.1109/EMBC.2012.6346046.

PMID:
23366007
2.
3.

Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival.

Jolivet R, Gerstner W.

J Physiol Paris. 2004 Jul-Nov;98(4-6):442-51. Epub 2005 Nov 7.

PMID:
16274972
4.
5.

Classification of correlated patterns with a configurable analog VLSI neural network of spiking neurons and self-regulating plastic synapses.

Giulioni M, Pannunzi M, Badoni D, Dante V, Del Giudice P.

Neural Comput. 2009 Nov;21(11):3106-29. doi: 10.1162/neco.2009.08-07-599.

PMID:
19686067
6.

An event-based neural network architecture with an asynchronous programmable synaptic memory.

Moradi S, Indiveri G.

IEEE Trans Biomed Circuits Syst. 2014 Feb;8(1):98-107. doi: 10.1109/TBCAS.2013.2255873.

PMID:
24681923
7.

Event-driven simulations of nonlinear integrate-and-fire neurons.

Tonnelier A, Belmabrouk H, Martinez D.

Neural Comput. 2007 Dec;19(12):3226-38.

PMID:
17970651
8.

Analytical integrate-and-fire neuron models with conductance-based dynamics and realistic postsynaptic potential time course for event-driven simulation strategies.

Rudolph-Lilith M, Dubois M, Destexhe A.

Neural Comput. 2012 Jun;24(6):1426-61. doi: 10.1162/NECO_a_00278. Epub 2012 Feb 24.

PMID:
22364504
9.

Extracting oscillations. Neuronal coincidence detection with noisy periodic spike input.

Kempter R, Gerstner W, van Hemmen JL, Wagner H.

Neural Comput. 1998 Nov 15;10(8):1987-2017.

PMID:
9804669
10.

Efficient event-driven simulation of large networks of spiking neurons and dynamical synapses.

Mattia M, Del Giudice P.

Neural Comput. 2000 Oct;12(10):2305-29.

PMID:
11032036
11.

Neuron firing in driven nonlinear integrate-and-fire models.

Kostur M, Schindler M, Talkner P, Hänggi P.

Math Biosci. 2007 Jun;207(2):302-11. Epub 2006 Aug 25.

PMID:
17011592
12.

Propagating waves can explain irregular neural dynamics.

Keane A, Gong P.

J Neurosci. 2015 Jan 28;35(4):1591-605. doi: 10.1523/JNEUROSCI.1669-14.2015.

13.

Variability and coding efficiency of noisy neural spike encoders.

Steinmetz PN, Manwani A, Koch C.

Biosystems. 2001 Sep-Oct;62(1-3):87-97.

PMID:
11595321
14.

Spike train statistics and dynamics with synaptic input from any renewal process: a population density approach.

Ly C, Tranchina D.

Neural Comput. 2009 Feb;21(2):360-96. doi: 10.1162/neco.2008.03-08-743.

PMID:
19431264
15.

Learning real-world stimuli in a neural network with spike-driven synaptic dynamics.

Brader JM, Senn W, Fusi S.

Neural Comput. 2007 Nov;19(11):2881-912.

17.

On the performance of voltage stepping for the simulation of adaptive, nonlinear integrate-and-fire neuronal networks.

Kaabi MG, Tonnelier A, Martinez D.

Neural Comput. 2011 May;23(5):1187-204. doi: 10.1162/NECO_a_00112. Epub 2011 Feb 7.

PMID:
21299420
18.

Self-organizing dual coding based on spike-time-dependent plasticity.

Masuda N, Aihara K.

Neural Comput. 2004 Mar;16(3):627-63.

PMID:
15006094
19.

A multichip neuromorphic system for spike-based visual information processing.

Vogelstein RJ, Mallik U, Culurciello E, Cauwenberghs G, Etienne-Cummings R.

Neural Comput. 2007 Sep;19(9):2281-300.

PMID:
17650061
20.

VLSI circuits implementing computational models of neocortical circuits.

Wijekoon JH, Dudek P.

J Neurosci Methods. 2012 Sep 15;210(1):93-109. doi: 10.1016/j.jneumeth.2012.01.019. Epub 2012 Feb 11.

PMID:
22342970

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