5,469 results found
    1. Neuroscience

    Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network

    Ian Cone, Harel Z Shouval
    A computational model shows that it's possible to learn and replay extended temporal sequences in a network of spiking neurons with a modular architecture and a biologically realistic learning rule.
    1. Neuroscience

    Rules and mechanisms for efficient two-stage learning in neural circuits

    Tiberiu Teşileanu et al.
    Teaching signals from "tutor" brain areas should be adapted to the plasticity mechanisms in "student" areas to achieve efficient learning in two-stage systems such as the vocal control circuit of the songbird.
    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

    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. Neuroscience
    2. Computational and Systems Biology

    Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning

    Rui Ponte Costa et al.
    Combined pre- and postsynaptically expressed long-term plasticity of neuronal connections improves sensory discrimination, and enables rapid relearning of previously encountered information.
    1. Neuroscience

    Distinct responses of Purkinje neurons and roles of simple spikes during associative motor learning in larval zebrafish

    Thomas C Harmon et al.
    Discrete classes of cerebellar Purkinje neurons show distinct changes in synaptic and spiking activity during motor learning, with simple spikes playing a shifting role during acquisition, expression, and maintenance of learned responses.
    1. Neuroscience

    Dopaminergic neurons write and update memories with cell-type-specific rules

    Yoshinori Aso, Gerald M Rubin
    Building on previous work (Aso et al., 2014a; Aso et al., 2014b), cell-type-specific drivers and optogenetics are used to control the local release of dopamine and reveal that distinct learning rules are implemented in parallel memory units.
    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

    Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila

    Yoshinori Aso et al.
    Output neurons in the mushroom body of the fruit fly brain encode the positive or negative survival value of stimuli, enabling insects to choose adaptive approach and avoidance behaviors through associative learning.
    1. Neuroscience

    Synaptic learning rules for sequence learning

    Eric Torsten Reifenstein et al.

Refine your results by:

Type
Research categories