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We describe how hierarchical concepts can be represented in three types of layered neural networks. The aim is to support recognition of the concepts when partial information about the concepts is presented, and also when some of the…

Neural and Evolutionary Computing · Computer Science 2025-03-25 Nancy A. Lynch

It has long been debated whether information in the brain is coded at the rate of neuronal spiking or at the precise timing of single spikes. Although this issue is essential to the understanding of neural signal processing, it is not…

Neurons and Cognition · Quantitative Biology 2014-02-17 Yasuhiro Mochizuk , Shigeru Shinomoto

Many biological neuronal networks exhibit highly variable spiking activity. Balanced networks offer a parsimonious model of this variability. In balanced networks, strong excitatory synaptic inputs are canceled by strong inhibitory inputs…

Neurons and Cognition · Quantitative Biology 2016-05-04 Ryan Pyle , Robert Rosenbaum

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are…

Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that…

Neurons and Cognition · Quantitative Biology 2018-08-21 Christopher Kim , Carson Chow

The activation/repression of a given gene is typically regulated by multiple transcription factors (TFs) that bind at the gene regulatory region and recruit RNA polymerase (RNAP). The interactions between the promoter region and TFs and…

Molecular Networks · Quantitative Biology 2009-07-14 Herbert Sauro , Song Yang

Spike propagation for spatially correlated inputs in layered neural networks has been investigated with the use of a semi-analytical dynamical mean-field approximation (DMA) theory recently proposed by the author [H. Hasegawa, Phys. Rev. E…

Disordered Systems and Neural Networks · Physics 2007-05-23 Hideo Hasegawa

Neurons are connected to other neurons by axons and dendrites that conduct signals with finite velocities, resulting in delays between the firing of a neuron and the arrival of the resultant impulse at other neurons. Since delays greatly…

Neurons and Cognition · Quantitative Biology 2021-08-25 Akke Mats Houben

As the need for higher performance from biological and electronic sensors continues to outpace current technologies, new strategies for designing, developing, and implementing novel sensor systems are emerging. A recently introduced…

Dynamical Systems · Mathematics 2026-02-16 Abdullah Ahmed , Maria Cameron , Antonio Palacios , Hengyuan Qi , Samir Sahoo

Critical branching is a theoretical interaction in-between simple units, such as neuronal elements of the human brain. Zhigalov, Kaplan, and Palva (2016, Clin. Neurophysiol., 127(8), 2882-2889) revealed that neurofeedback flash stimulation…

Neurons and Cognition · Quantitative Biology 2017-02-22 Tom Campbell

High-level brain function such as memory, classification or reasoning can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy efficient substrate for the…

Neurons and Cognition · Quantitative Biology 2016-06-10 Thomas Pfeil , Jakob Jordan , Tom Tetzlaff , Andreas Grübl , Johannes Schemmel , Markus Diesmann , Karlheinz Meier

Oscillatory activities are widely observed in specific frequency bands of recorded field potentials in different brain regions, and play critical roles in processing neural information. Understanding the structure of these oscillatory…

Neurons and Cognition · Quantitative Biology 2015-07-23 Pengsheng Zheng

Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. However, the supervised training of SNNs remains a hard problem due to the discontinuity of the spiking neuron…

Neural and Evolutionary Computing · Computer Science 2021-12-20 Mingqing Xiao , Qingyan Meng , Zongpeng Zhang , Yisen Wang , Zhouchen Lin

This paper proposes a neuronal circuitry layout and synaptic plasticity principles that allow the (pyramidal) neuron to act as a "combinatorial switch". Namely, the neuron learns to be more prone to generate spikes given those combinations…

Biological Physics · Physics 2017-05-09 Marat M. Rvachev

Recurrent neural networks are powerful tools for understanding and modeling computation and representation by populations of neurons. Continuous-variable or "rate" model networks have been analyzed and applied extensively for these…

Neurons and Cognition · Quantitative Biology 2016-01-29 Brian DePasquale , Mark M. Churchland , L. F. Abbott

Neurons communicate with downstream systems via sparse and incredibly brief electrical pulses, or spikes. Using these events, they control various targets such as neuromuscular units, neurosecretory systems, and other neurons in connected…

Neurons and Cognition · Quantitative Biology 2026-03-17 Paolo Agliati , André Urbano , Pablo Lanillos , Nasir Ahmad , Marcel van Gerven , Sander Keemink

Sensory neurons are often described in terms of a receptive field, that is, a linear kernel through which stimuli are filtered before they are further processed. If information transmission is assumed to proceed in a feedforward cascade,…

Neurons and Cognition · Quantitative Biology 2015-03-11 Eugenio Urdapilleta , Inés Samengo

In the present work, electroencephalographic recordings of healthy human participants were performed to study the entrainment of brainwaves using a variety of stimulus. First, periodic entrainment of the brainwaves was studied using two…

Neurons and Cognition · Quantitative Biology 2019-01-04 Richa Phogat , P. Parmananda

Robust perception relies on both bottom-up and top-down signals. Bottom-up signals consist of what's directly observed through sensation. Top-down signals consist of beliefs and expectations based on past experience and short-term memory,…

Machine Learning · Computer Science 2020-11-17 Sarthak Mittal , Alex Lamb , Anirudh Goyal , Vikram Voleti , Murray Shanahan , Guillaume Lajoie , Michael Mozer , Yoshua Bengio

Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits yet its computational role still remains elusive. We show that a model of Gamma-band rhythmic inhibition allows networks of coupled cortical circuit motifs to…

Neurons and Cognition · Quantitative Biology 2017-11-08 Hesham Mostafa , Lorenz K. Muller , Giacomo Indiveri
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