Sequence learning, prediction, and replay in networks of spiking neurons

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   A Neuro-Mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization

   Alexander Ororbia, Mary Alexandria Kelly · 2023 · Neurones et cognition · Résumé IA disponible

   Résumé scientifique

   Over the last few years, large neural generative models, capable of synthesizing intricate sequences of words or producing complex image patterns, have recently emerged as a popular representation of what has come to be known as "generative artificial intelligence" (generative AI). Beyond opening the door to new opportunities as well as challenges for the domain of statistical machine learning, the rising popularity of generative AI brings with it interesting questions for Cognitive Science, which seeks to discover the nature of the processes that underpin minds and brains as well as to understand how such functionality might be acquired and instantiated in biological (or artificial) substra…

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   Error correction and fast detectors implemented by ultra-fast neuronal plasticity

   Roni Vardi, Hagar Marmari, Ido Kanter · 2014 · Phys. Rev. E 89, 042712 (2014) · Pas encore résumé

   Résumé scientifique

   We experimentally show that the neuron functions as a precise time-integrator, where the accumulated changes in neuronal response latencies, under complex and random stimulation patterns, are solely a function of a global quantity, the average time-lag between stimulations. In contrast, momentary leaps in the neuronal response latency follow trends of consecutive stimulations, indicating ultra-fast neuronal plasticity. On a circuit level, this ultra-fast neuronal plasticity phenomenon implements error-correction mechanisms and fast detectors for misplaced stimulations. Additionally, at moderate/high stimulation rates this phenomenon destabilizes/stabilizes a periodic neuronal activity disrup…

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   Latent computing by biological neural networks: A dynamical systems framework

   Fatih Dinc, Marta Blanco-Pozo, David Klindt et al. · 2025 · Neurones et cognition · Résumé IA disponible

   Résumé scientifique

   Although individual neurons and neural populations exhibit the phenomenon of representational drift, perceptual and behavioral outputs of many neural circuits can remain stable across time scales over which representational drift is substantial. These observations motivate a dynamical systems framework for neural network activity that focuses on the concept of \emph{latent processing units,} core elements for robust coding and computation embedded in collective neural dynamics. Our theoretical treatment of these latent processing units yields five key attributes of computing through neural network dynamics. First, neural computations that are low-dimensional can nevertheless generate high-di…

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   A Foundational Theory for Decentralized Sensory Learning

   Linus Mårtensson, Jonas M. D. Enander, Udaya B. Rongala et al. · 2025 · Neurones et cognition · Résumé IA disponible

   Résumé scientifique

   In both neuroscience and artificial intelligence, popular functional frameworks and neural network formulations operate by making use of extrinsic error measurements and global learning algorithms. Through a set of conjectures based on evolutionary insights on the origin of cellular adaptive mechanisms, we reinterpret the core meaning of sensory signals to allow the brain to be interpreted as a negative feedback control system, and show how this could lead to local learning algorithms without the need for global error correction metrics. Thereby, a sufficiently good minima in sensory activity can be the complete reward signal of the network, as well as being both necessary and sufficient for…

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   Relaxing the Constraints on Predictive Coding Models

   Beren Millidge, Alexander Tschantz, Anil Seth et al. · 2020 · Neurones et cognition · Pas encore résumé

   Résumé scientifique

   Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs, which underlies both perception and learning, is the minimization of prediction errors. While motivated by high-level notions of variational inference, detailed neurophysiological models of cortical microcircuits which can implements its computations have been developed. Moreover, under certain conditions, predictive coding has been shown to approximate the backpropagation of error algorithm, and thus provides a relatively biologically plausible credit-assignment mechanism for training deep networks. However, standard implementations of the algorithm still involve p…

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   How to build a cognitive map: insights from models of the hippocampal formation

   James C. R. Whittington, David McCaffary, Jacob J. W. Bakermans et al. · 2022 · Neurones et cognition · Résumé IA disponible

   Résumé scientifique

   Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviours for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unravelling the learning and neural representation of such a map has become a central focus of neuroscience. While experimentalists are providing a detailed picture of the neural substrate of cognitive maps in hippocampus and beyond, theorists have been busy building models to bridge the divide between neurons, computation, and behaviour. These models can account for a variety of known representations and neural pheno…

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   Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees

   Ilenna Simone Jones, Konrad Paul Kording · 2020 · Neurones et cognition · Pas encore résumé

   Résumé scientifique

   Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. This is in stark contrast to units in artificial neural networks that are generally linear apart from an output nonlinearity. If dendritic trees can be nonlinear, biological neurons may have far more computational power than their artificial counterparts. Here we use a simple model where the dendrite is implemented as a sequence of thresholded linear units. We find that such dendrites can readily solve machine learning problems, such as MNIST or CIFAR-10, and that they benefit from having the same input onto several branches of the dendritic tree. This dendr…

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   Learning, fast and slow

   Markus Meister · 2022 · Neurones et cognition · Résumé IA disponible

   Résumé scientifique

   Animals can learn efficiently from a single experience and change their future behavior in response. However, in other instances, animals learn very slowly, requiring thousands of experiences. Here I survey tasks involving fast and slow learning and consider some hypotheses for what differentiates the underlying neural mechanisms. It has been proposed that fast learning relies on neural representations that favor efficient Hebbian modification of synapses. These efficient representations may be encoded in the genome, resulting in a repertoire of fast learning that differs across species. Alternatively, the required neural representations may be acquired from experience through a slow process…