ArticleFrontiers in computational neuroscience2019
Unsupervised Learning of Persistent and Sequential Activity.
Article in Frontiers in computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Independence and coherence in temporal sequence computation across the fronto-parietal network.Nature communications · 2026Article
- Discovering plasticity rules that organize and maintain neural circuits.bioRxiv : the preprint server for biology · 2024Article
- Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules.eLife · 2024Article
- Recall tempo of Hebbian sequences depends on the interplay of Hebbian kernel with tutor signal timing.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Optogenetic manipulation of inhibitory interneurons can be used to validate a model of spatiotemporal sequence learning.Frontiers in computational neuroscience · 2023Article
- Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.PLoS computational biology · 2022Article
- Dynamic branching in a neural network model for probabilistic prediction of sequences.Journal of computational neuroscience · 2022Article
- Leveraging Continuous Vital Sign Measurements for Real-Time Assessment of Autonomic Nervous System Dysfunction After Brain Injury: A Narrative Review of Current and Future Applications.Neurocritical care · 2022Review
- Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.PLoS computational biology · 2022Article
- Metastable attractors explain the variable timing of stable behavioral action sequences.Neuron · 2022Article
- From synapse to network: models of information storage and retrieval in neural circuits.Current opinion in neurobiology · 2021Review
- Transient incubation of cultured hippocampal neurons in the absence of magnesium induces rhythmic and synchronized epileptiform-like activity.Scientific reports · 2021Article
- Article
- Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.PloS one · 2019Article
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2 authors.
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Abstract
Two strikingly distinct types of activity have been observed in various brain structures during delay periods of delayed response tasks: Persistent activity (PA), in which a sub-population of neurons maintains an elevated firing rate throughout an entire delay period; and Sequential activity (SA), in which sub-populations of neurons are activated sequentially in time. It has been hypothesized that both types of dynamics can be "learned" by the relevant networks from the statistics of their inputs, thanks to mechanisms of synaptic plasticity. However, the necessary conditions for a synaptic plasticity rule and input statistics to learn these two types of dynamics in a stable fashion are still unclear. In particular, it is unclear whether a single learning rule is able to learn both types of activity patterns, depending on the statistics of the inputs driving the network. Here, we first characterize the complete bifurcation diagram of a firing rate model of multiple excitatory populations with an inhibitory mechanism, as a function of the parameters characterizing its connectivity. We then investigate how an unsupervised temporally asymmetric Hebbian plasticity rule shapes the dynamics of the network. Consistent with previous studies, we find that for stable learning of PA and SA, an additional stabilization mechanism is necessary. We show that a generalized version of the standard multiplicative homeostatic plasticity (Renart et al., 2003; Toyoizumi et al., 2014) stabilizes learning by effectively
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