3,492 results found
    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

    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. Neuroscience

    Neuron-specific knockouts indicate the importance of network communication to Drosophila rhythmicity

    Matthias Schlichting et al.
    Network silencing experiments and cell-specific CRISPR/Cas9 knockouts suggest that network communication is necessary for generating robust rhythms within the clock neuron network.
    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

    Self-organization of modular network architecture by activity-dependent neuronal migration and outgrowth

    Samora Okujeni, Ulrich Egert
    Activity-dependent neurite outgrowth and neuronal migration interact to shape modular mesoscale network architecture by homeostatic self-organization.
    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. Medicine

    Performance of a deep learning based neural network in the selection of human blastocysts for implantation

    Charles L Bormann et al.
    A well-trained deep learning neural network can outperform and can potentially assist expertly trained embryologists in selecting embryos based on their implantation potential, even amongst high-quality euploid blastocyst embryos.
    1. Neuroscience

    Interplay between population firing stability and single neuron dynamics in hippocampal networks

    Edden Slomowitz et al.
    Neuronal networks balance flexibility with stability by allowing the firing rate of individual neurons within a network to vary over time, while ensuring that the average firing rate across the network remains constant.
    1. Cancer Biology

    Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer

    Kevin M Elias et al.
    Application of machine learning to serum miRNA profiles generated through next generation sequencing identifies a biologically relevant miRNA signature which can be deployed as a qPCR test to assist the diagnosis of epithelial ovarian cancer.
    1. Computational and Systems Biology
    2. Neuroscience

    Fast and flexible sequence induction in spiking neural networks via rapid excitability changes

    Rich Pang, Adrienne L Fairhall
    Biologically plausible changes in the excitabilities of single neurons may suffice to selectively modulate sequential network dynamics, without modifying of recurrent connectivity.

Refine your results by:

Type
Research categories