ArticleFrontiers in computational neuroscience2013
Stimulus number, duration and intensity encoding in randomly connected attractor networks with synaptic depression.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Inhibitory-stabilization is sufficient for history-dependent computation in a randomly connected attractor network.Journal of computational neuroscience · 2026Article
- Perpendicular neuromorphic channels facilitate lateral inhibition for tactile location.Nature communications · 2026Article
- Inhibitory-stabilization is sufficient for history-dependent computation in a randomly connected attractor network.bioRxiv : the preprint server for biology · 2025Article
- Multistability in neural systems with random cross-connections.Biological cybernetics · 2023Article
- Role of Lateral Inhibition on Visual Number Sense.Frontiers in computational neuroscience · 2022Article
- Attractor-state itinerancy in neural circuits with synaptic depression.Journal of mathematical neuroscience · 2020Article
- Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity.Journal of computational neuroscience · 2019Article
- A random-matrix theory of the number sense.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Article
- Itinerancy between attractor states in neural systems.Current opinion in neurobiology · 2016Review
- Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.PLoS computational biology · 2016Article
- Dynamical systems, attractors, and neural circuits.F1000Research · 2016Review
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Abstract
Randomly connected recurrent networks of excitatory groups of neurons can possess a multitude of attractor states. When the internal excitatory synapses of these networks are depressing, the attractor states can be destabilized with increasing input. This leads to an itinerancy, where with either repeated transient stimuli, or increasing duration of a single stimulus, the network activity advances through sequences of attractor states. We find that the resulting network state, which persists beyond stimulus offset, can encode the number of stimuli presented via a distributed representation of neural activity with non-monotonic tuning curves for most neurons. Increased duration of a single stimulus is encoded via different distributed representations, so unlike an integrator, the network distinguishes separate successive presentations of a short stimulus from a single presentation of a longer stimulus with equal total duration. Moreover, different amplitudes of stimulus cause new, distinct activity patterns, such that changes in stimulus number, duration and amplitude can be distinguished from each other. These properties of the network depend on dynamic depressing synapses, as they disappear if synapses are static. Thus, short-term synaptic depression allows a network to store separately the different dynamic properties of a spatially constant stimulus.
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