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Computing Information in Neuronal Spikes
Neural Processing Letters, 2006This paper provides new insights regarding the transfer of information between input signal and the output of neurons. Simulations of the Hodgkin-Huxley (HH) model combined with computational techniques are used to estimate this transfer of information.
Dorian Aur +2 more
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The transfer function of neuron spike
Neural Networks, 2015The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in ...
Igor Palmieri +2 more
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2002
Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the
Wulfram Gerstner, Werner M. Kistler
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Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the
Wulfram Gerstner, Werner M. Kistler
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Image Segmentation by Networks of Spiking Neurons
Neural Computation, 2005A network of leaky integrate-and-fire (IAF) neurons is proposed to segment gray-scale images. The network architecture with local competition between neurons that encode segment assignments of image blocks is motivated by a histogram clustering approach to image segmentation. Lateral excitatory connections between neighboring image sites yield a local
Buhmann JM, Lange T, Ramacher U
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Capacity of a Single Spiking Neuron Channel
Neural Computation, 2009Information transfer through a single neuron is a fundamental component of information processing in the brain, and computing the information channel capacity is important to understand this information processing. The problem is difficult since the capacity depends on coding, characteristics of the communication channel, and optimization over input ...
Shiro Ikeda, Jonathan H. Manton
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Spiking Neurons Computing Platform
2005A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components. We focus on conductance-based models for neurons that emulate the temporal dynamics of the synaptic integration process.
Eduardo Ros 0001 +5 more
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Coupled fractional spiking neurons
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016We propose a fractional-order (FO) model of two symmetrically coupled Hodgkin-Huxley equations and study the patterns of the neurons' firing rates, for distinct values of the order of the fractional derivative, α, and the temperature, T. We find that, for positive values of the coupling, the neurons exhibit in-phase periodic solutions (neurons fire at ...
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RELIABILITY OF SPIKE TIMING IN A NEURON MODEL
International Journal of Bifurcation and Chaos, 2004Recently, it has been shown experimentally [Mainen & Sejnowski, 1995] that, in contrast to the lack of precision in spike timing associated with flat (dc) stimuli, neocortical neurons of rats respond reliably to weak input fluctuations resembling synaptic activity.
José M. Casado, J. P. Baltanás
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Simple model of spiking neurons
IEEE Transactions on Neural Networks, 2003A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons.
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A spiking neuron model: applications and learning
Neural Networks, 2002This paper presents a biologically inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the model as well as its applications in Computational Neuroscience are demonstrated and a learning algorithm based on postsynaptic delays is proposed.
Christodoulou, Chris C. +5 more
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