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We study mean-field descriptions for spatially-extended networks of linear (leaky) and quadratic integrate-and-fire neurons with stochastic spiking times. We consider large-population limits of continuous-time Galves-L\"ocherbach (GL)…

Probability · Mathematics 2025-02-06 Daniele Avitabile , Michel Davydov

Neurophysiologists are nowadays able to record from a large number of extracellular electrodes and to extract, from the raw data, the sequences of action potentials or spikes generated by many neurons. Unfortunately these ''many neurons''…

Applications · Statistics 2026-04-22 Pierre Charitat , Ségolen Geffray , Christophe Pouzat

Cortical neurons include many sub-cellular processes, operating at multiple timescales, which may affect their response to stimulation through non-linear and stochastic interaction with ion channels and ionic concentrations. Since new…

Neurons and Cognition · Quantitative Biology 2014-05-01 Daniel Soudry , Ron Meir

In randomly connected networks of pulse-coupled elements a time-dependent input signal can be buffered over a short time. We studied the signal buffering properties in simulated networks as a function of the networks state, characterized by…

Neurons and Cognition · Quantitative Biology 2009-11-11 Julien Mayor , Wulfram Gerstner

Finite-sized populations of spiking elements are fundamental to brain function, but also used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasi-renewal…

Neurons and Cognition · Quantitative Biology 2015-03-04 Moritz Deger , Tilo Schwalger , Richard Naud , Wulfram Gerstner

The population model of Wilson-Cowan is perhaps the most popular in the history of computational neuroscience. It embraces the nonlinear mean field dynamics of excitatory and inhibitory neuronal populations provided via a temporal…

Neurons and Cognition · Quantitative Biology 2023-09-13 Maryam Saadati , Saba Sadat Khodaei , Yousef Jamali

Neuroscientists formulate very different hypotheses about the nature of neural code. At one extreme, it has been argued that neurons encode information in relatively slow changes of individual spikes arriving "rates codes" and the…

Neurons and Cognition · Quantitative Biology 2016-03-10 Agnieszka Pregowska , Janusz Szczepanski , Eligiusz Wajnryb

We describe a new, computationally simple method for analyzing the dynamics of neuronal spike trains driven by external stimuli. The goal of our method is to test the predictions of simple spike-generating models against extracellularly…

Neurons and Cognition · Quantitative Biology 2007-05-23 Daniel S. Reich , Jonathan D. Victor , Bruce W. Knight

Dale's Principle has historically guided neuroscience research as a valuable rule of thumb, namely that all synapses on each neuron release the same set of neurotransmitters. Most existing Spiking Neuron Network models share this…

Neurons and Cognition · Quantitative Biology 2026-02-27 Ross Ah-Weng , Hardik Rajpal

Recent experimental work has suggested that the neural firing rate can be interpreted as a fractional derivative, at least when signal variation induces neural adaptation. Here, we show that the actual neural spike-train itself can be…

Neurons and Cognition · Quantitative Biology 2010-11-01 Sander M. Bohte , Jaldert O. Rombouts

Research showed that, the information transmitted in biological neurons is encoded in the instants of successive action potentials or their firing rate. In addition to that, in-vivo operation of the neuron makes measurement difficult and…

Neurons and Cognition · Quantitative Biology 2018-01-10 Ozgur Doruk , Kechen Zhang

We consider a population of $N$ interacting neurons, represented by a multivariate Hawkes process: the firing rate of each neuron depends on the history of the connected neurons. Contrary to the mean-field framework where the interaction…

Probability · Mathematics 2022-02-24 Zoé Agathe-Nerine

The coding properties of cells with different types of receptive fields have been studied for decades. ON-type neurons fire in response to positive fluctuations of the time-dependent stimulus, whereas OFF cells are driven by negative…

Neurons and Cognition · Quantitative Biology 2009-09-10 Eugenio Urdapilleta , Ines Samengo

Neuromorphic computing has recently gained momentum with the emergence of various neuromorphic processors. As the field advances, there is an increasing focus on developing training methods that can effectively leverage the unique…

Emerging Technologies · Computer Science 2025-04-15 Sanaz Mahmoodi Takaghaj , Jack Sampson

Network of neurons in the brain apply - unlike processors in our current generation of computer hardware - an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event…

Neural and Evolutionary Computing · Computer Science 2014-12-19 Zeno Jonke , Stefan Habenschuss , Wolfgang Maass

Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time…

Neurons and Cognition · Quantitative Biology 2013-10-31 Michael A. Buice , Carson C. Chow

Consider a compound Poisson process with jump measure $\nu$ supported by finitely many positive integers. We propose a method for estimating $\nu$ from a single, equidistantly sampled trajectory and develop associated statistical…

Statistics Theory · Mathematics 2009-09-29 Werner Ehm , Benjamin Staude , Stefan Rotter

A key aim in biology and psychology is to identify fundamental principles underpinning the behavior of animals, including humans. Analyses of human language and the behavior of a range of non-human animal species have provided evidence for…

Neurons and Cognition · Quantitative Biology 2014-12-03 R. Ferrer-i-Cancho , A. Hernández-Fernández , D. Lusseau , G. Agoramoorthy , M. J. Hsu , S. Semple

We consider a stochastic model describing the spiking activity of a countable set of neurons spatially organized into a homogeneous tree of degree $d$, $d \geq 2$; the degree of a neuron is just the number of connections it has. Roughly,…

Probability · Mathematics 2022-05-17 A. M. B. Nascimento

We study the asymptotic law of a network of interacting neurons when the number of neurons becomes infinite. Given a completely connected network of firing rate neurons in which the synaptic weights are Gaussian correlated random variables,…

Probability · Mathematics 2013-06-03 Olivier Faugeras , James MacLaurin