3,029 results found
    1. Neuroscience

    Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

    Aditya Gilra, Wulfram Gerstner
    Recurrent neuronal networks learn to predict movement in a self-supervised way using biologically plausible learning rules.
    1. Computational and Systems Biology
    2. Neuroscience

    Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

    Thomas Miconi
    A biologically plausible learning rule allows recurrent neural networks to learn nontrivial tasks, using only sparse, delayed rewards, and the neural dynamics of trained networks exhibit complex dynamics observed in animal frontal cortices.
    1. Neuroscience

    Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks

    Vishwa Goudar, Dean V Buonomano
    A recurrent network model trained to transcribe temporally scaled spoken digits into handwritten digits proposes that the brain flexibly encodes time-varying stimuli as neural trajectories that can be traversed at different speeds.
    1. Neuroscience

    Reward-based training of recurrent neural networks for cognitive and value-based tasks

    H Francis Song et al.
    A two-part neural network models reward-based training and provides a unified framework in which to study diverse computations that can be compared to electrophysiological recordings from behaving animals.
    1. Neuroscience

    Learning excitatory-inhibitory neuronal assemblies in recurrent networks

    Owen Mackwood et al.
    The synaptic structure in mouse V1 is explained by a synergy of homeostatic plasticity in incoming and outgoing synapses of inhibitory interneurons, establishing a stimulus-specific balance of excitation and inhibition.
    1. Neuroscience

    Local online learning in recurrent networks with random feedback

    James M Murray
    A biologically plausible learning rule enables recurrent neural networks to model the way in which neural circuits use supervised learning to perform time-dependent computations.
    1. Computational and Systems Biology

    PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

    Daniel Griffith, Alex S Holehouse
    PARROT makes it easy for anyone to train and use system- or data-specific deep learning models that map between protein sequence and arbitrary sequence annotations.
    1. Neuroscience

    Distributed functions of prefrontal and parietal cortices during sequential categorical decisions

    Yang Zhou et al.
    Prefrontal cortex plays a leading role in sequential decisions compared to posterior parietal cortex and relies on nonlinear integration of sensory and mnemonic information for decision formation.
    1. Neuroscience

    Network dynamics underlying OFF responses in the auditory cortex

    Giulio Bondanelli et al.
    Computational modeling demonstrates that population dynamics of neural calcium activity following stimulus offset are consistent with a network mechanism based on recurrent interactions.
    1. Neuroscience

    A multilayer circuit architecture for the generation of distinct locomotor behaviors in Drosophila

    Aref Arzan Zarin et al.
    Generation of a premotor/motor neuron comprehensive TEM reconstruction, functional optogenetics, and recurrent network modeling reveals different phase relationships among a subset of Drosophila motor neurons in forward versus backward locomotion.

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