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Spontaneous and driven cortical activity: implications for computation.
Open accessAbstract
The traditional view of spontaneous neural activity as 'noise' has been challenged by recent findings suggesting that: (a) spontaneous activity in cortical populations is highly structured in both space and time, (b) the spatio-temporal structure of spontaneous activity is linked to the underlying connectivity of the cortical network, (c) spontaneous cortical activity interacts with external stimulation to generate responses to the individual presentations of a stimulus, (d) network connectivity is shaped in part by the statistics of natural signals and (e) ongoing cortical activity represents a continuous top-down prediction/expectation signal that interacts with incoming input to generate an updated representation of the world. These results can be integrated to provide a new framework for the study of cortical computation.
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“several experimental studies arrive at the conclusion that external stimuli only modulate the highly stochastic spontaneous firing activity of cortical networks of neurons [7], [8].”
From Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons (2011) · cites this paper as [8]
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Dynamic functional connectivity: Promise, issues, and interpretations
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.079 · Cited by 2,962
Normalization as a canonical neural computation
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“The membrane potential of neurons is not only dependent on the afferent signals that are meant to drive the neuron but also on other signals originating from the rest of the brain in the form of ongoing activity”
BackgroundSpontaneous behaviors drive multidimensional, brainwide activity
Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., & Harris, K. D. (2019) · Science · doi:10.1126/science.aav7893 · Cited by 1,510
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Statistically optimal perception and learning: from behavior to neural representations
Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672
Metadata
- Authors
- Ringach, D. L.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Ringach, Dario L
- ORCID
- Ringach, Dario L
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2009-08
- Volume
- 19
- Issue
- 4
- Pages
- 439-444
- ISSN
- 0959-4388 (ISSN-L)
- PMC
- PMC3319344
- OpenAlex
- W2148439693
- Cited by
- 234
- References
- 50
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 11 · 2023: 29 · 2022: 14 · 2021: 21 · 2020: 8 · 2019: 13 · 2018: 14 · 2017: 18 · 2016: 15 · 2015: 17 · 2014: 7 · 2013: 13 · 2012: 16
- FWCI
- 0.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Opinion in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Eye Institute (R01EY018322, EY-12816, EY18322, R01EY012816)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- spontaneous neuronal activity, cortical activity, cortical connectivity, spatiotemporal structure, top-down prediction, expectations, cortical computation, noise
- MeSH terms
- Animals, Cerebral Cortex, Models, Neurological, Nerve Net, Neurons, Photic Stimulation, Visual Perception, Neural Networks, Computer
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