ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2010
Stochastic transitions between neural states in taste processing and decision-making.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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Who cites it
79 citing papers in PubMed, 127 citations in OpenAlex.
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- Drifting population dynamics with transient resets characterize sensorimotor transformation in the monkey superior colliculus.Communications biology · 2026Article
- Sensory and palatability coding of taste stimuli in cortex involves dynamic and asymmetric cortico-amygdalar interactions.Journal of neurophysiology · 2026Article
- Noise-induced collective memory in schooling fish.Bioinspiration & biomimetics · 2026Article
- Inhibitory-stabilization is sufficient for history-dependent computation in a randomly connected attractor network.bioRxiv : the preprint server for biology · 2025Article
- Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Linear and categorical coding units in the mouse gustatory cortex drive population dynamics and behavior in taste decision-making.bioRxiv : the preprint server for biology · 2025Article
- A solvable neural circuit model revealing the dynamical principle of non-optimal temporal weighting in perceptual decision making.Journal of computational neuroscience · 2025Article
- A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets.bioRxiv : the preprint server for biology · 2025Article
- A sticky Poisson Hidden Markov Model for solving the problem of over-segmentation and rapid state switching in cortical datasets.PloS one · 2025Article
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- Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits.PLoS computational biology · 2024Article
- Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits.bioRxiv : the preprint server for biology · 2024Article
- Active Learning for Discrete Latent Variable Models.Neural computation · 2024Article
- A machine learning approach for real-time cortical state estimation.Journal of neural engineering · 2024Article
- Multistability in neural systems with random cross-connections.Biological cybernetics · 2023Article
- Temporal progression along discrete coding states during decision-making in the mouse gustatory cortex.PLoS computational biology · 2023Article
19 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Noise, which is ubiquitous in the nervous system, causes trial-to-trial variability in the neural responses to stimuli. This neural variability is in turn a likely source of behavioral variability. Using Hidden Markov modeling, a method of analysis that can make use of such trial-to-trial response variability, we have uncovered sequences of discrete states of neural activity in gustatory cortex during taste processing. Here, we advance our understanding of these patterns in two ways. First, we reproduce the experimental findings in a formal model, describing a network that evinces sharp transitions between discrete states that are deterministically stable given sufficient noise in the network; as in the empirical data, the transitions occur at variable times across trials, but the stimulus-specific sequence is itself reliable. Second, we demonstrate that such noise-induced transitions between discrete states can be computationally advantageous in a reduced, decision-making network. The reduced network produces binary outputs, which represent classification of ingested substances as palatable or nonpalatable, and the corresponding behavioral responses of "spit" or "swallow". We evaluate the performance of the network by measuring how reliably its outputs follow small biases in the strengths of its inputs. We compare two modes of operation: deterministic integration ("ramping") versus stochastic decision-making ("jumping"), the latter of which relies on state-to-state transitions. We find that the stochastic mode of operation can be optimal under typical levels of internal noise and that, within this mode, addition of random noise to each input can improve optimal performance when decisions must be made in limited time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.