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Biological systems leverage top-down feedback for visual processing, yet most artificial vision models succeed in image classification using purely feedforward or recurrent architectures, calling into question the functional significance of…

Neurons and Cognition · Quantitative Biology 2025-08-12 Antonino Greco , Marco D'Alessandro , Karl J. Friston , Giovanni Pezzulo , Markus Siegel

The human brain exhibits a relatively stable spatiotemporal organization that supports brain function and can be manipulated via local brain stimulation. Such perturbations to local cortical dynamics are globally integrated by distinct…

Neurons and Cognition · Quantitative Biology 2018-01-19 Leonardo L. Gollo , James A. Roberts , Luca Cocchi

Measured responses from visual cortical neurons show that spike times tend to be correlated rather than exactly Poisson distributed. Fano factors vary and are usually greater than 1 due to the tendency of spikes being clustered into bursts.…

Neurons and Cognition · Quantitative Biology 2007-05-23 Alexander Lerchner , Mandana Ahmadi , John Hertz

According to the theory of efficient coding, sensory systems are adapted to represent natural scenes with high fidelity and at minimal metabolic cost. Testing this hypothesis for sensory structures performing non-linear computations on high…

Neurons and Cognition · Quantitative Biology 2018-04-13 Ulisse Ferrari , Christophe Gardella , Olivier Marre , Thierry Mora

Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation but how neurons self-organize to produce such activity is not well understood. Attractor-based models have been successfully implemented as…

Neurons and Cognition · Quantitative Biology 2013-03-22 Mark Niedringhaus , Xin Chen , Katherine Conant , Rhonda Dzakpasu

Competition between synapses arises in some forms of correlation-based plasticity. Here we propose a game theory-inspired model of synaptic interactions whose dynamics is driven by competition between synapses in their weak and strong…

Disordered Systems and Neural Networks · Physics 2011-10-19 Ajaz Ahmad Bhat , Gaurang Mahajan , Anita Mehta

The magnitude of correlations between stimulus-driven responses of pairs of neurons can itself be stimulus-dependent. We examine how this dependence impacts the information carried by neural populations about the stimuli that drive them.…

Neurons and Cognition · Quantitative Biology 2008-10-14 Kresimir Josic , Eric Shea-Brown , Brent Doiron , Jaime de la Rocha

Biological and artificial neural systems are composed of many local processors, and their capabilities depend upon the transfer function that relates each local processor's outputs to its inputs. This paper uses a recent advance in the…

Information Theory · Computer Science 2018-03-16 Jim W. Kay , William A. Phillips

In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal. In the natural world, intelligent organisms learn from internal drives, bypassing the need for external signals, which is beneficial…

Machine Learning · Computer Science 2020-06-16 Rui Zhao , Yang Gao , Pieter Abbeel , Volker Tresp , Wei Xu

Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions.…

Neurons and Cognition · Quantitative Biology 2014-09-16 Leonardo L. Gollo , Michael Breakspear

Cognitive control is a suite of processes that helps individuals pursue goals despite resistance or uncertainty about what to do. Although cognitive control has been extensively studied as a dynamic feedback loop of perception, valuation,…

Biological and artificial learning systems alike confront the plasticity-stability dilemma. In the brain, neuromodulators such as acetylcholine and noradrenaline relieve this tension by tuning neuronal gain and inhibitory gating, balancing…

Neurons and Cognition · Quantitative Biology 2025-07-14 Alejandro Rodriguez-Garcia , Christopher J. Whyte , Brandon R. Munn , Jie Mei , James M. Shine , Srikanth Ramaswamy

This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons. Inspired from neuropsychology and neurobiology, the model implements top-down…

Neural and Evolutionary Computing · Computer Science 2016-08-16 Anthony Mouraud , Hélène Paugam-Moisy

Commonly studied cellular automata are memoryless and have fixed topology of connections between cells. However by allowing updates of links and short-term memory in cells we may potentially discover novel complex regimes of spatio-temporal…

Cellular Automata and Lattice Gases · Physics 2012-12-13 Ramon Alonso-Sanz , Andrew Adamatzky

Computational modeling is becoming a widely used methodology in modern neuroscience. However, as the complexity of the phenomena under study increases, the analysis of the results emerging from the simulations concomitantly becomes more…

Neurons and Cognition · Quantitative Biology 2020-03-16 Sergio E. Galindo , Pablo Toharia , Oscar D. Robles , Eduardo Ros , Luis Pastor , Jesús A. Garrido

Recent work in continual learning has highlighted the stability gap -- a temporary performance drop on previously learned tasks when new ones are introduced. This phenomenon reflects a mismatch between rapid adaptation and strong retention…

Machine Learning · Computer Science 2026-01-28 Alejandro Rodriguez-Garcia , Anindya Ghosh , Srikanth Ramaswamy

The adaptive fitness of an organism in its ecological niche is highly reliant upon its ability to associate an environmental or internal stimulus with a behavior response through reinforcement. This simple but powerful observation has been…

Neurons and Cognition · Quantitative Biology 2023-12-01 Roy E. Clymer , Sanjeev V. Namjoshi

Flexible cognition requires the ability to rapidly detect systematic functions of variables and guide future behavior based on predictions. The model described here proposes a potential framework for patterns of neural activity to detect…

Neurons and Cognition · Quantitative Biology 2018-10-17 Michael E. Hasselmo

Metastable brain dynamics are characterized by abrupt, jump-like modulations so that the neural activity in single trials appears to unfold as a sequence of discrete, quasi-stationary states. Evidence that cortical neural activity unfolds…

Neurons and Cognition · Quantitative Biology 2019-06-20 Giancarlo La Camera , Alfredo Fontanini , Luca Mazzucato

Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment and decision making. In each of these domains, two mechanisms…

Neurons and Cognition · Quantitative Biology 2023-10-11 Ted Moskovitz , Kevin Miller , Maneesh Sahani , Matthew M. Botvinick
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