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Neuronal arithmetic
Open accessAbstract
The vast computational power of the brain has traditionally been viewed as arising from the complex connectivity of neural networks, in which an individual neuron acts as a simple linear summation and thresholding device. However, recent studies show that individual neurons utilize a wealth of nonlinear mechanisms to transform synaptic input into output firing. These mechanisms can arise from synaptic plasticity, synaptic noise, and somatic and dendritic conductances. This tool kit of nonlinear mechanisms confers considerable computational power on both morphologically simple and more complex neurons, enabling them to perform a range of arithmetic operations on signals encoded ina variety of different ways.
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“Our findings indicate that neuromodulation can regulate single-synapse computations at the hippocampal mossy fiber synapse [1,103].”
From Presynaptic cAMP-PKA-mediated potentiation induces reconfiguration of synaptic vesicle pools and channel-vesicle coupling at hippocampal mossy fiber boutons (2024) · cites this paper as [103]
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“This agrees with general evidence that the Hill function fits stimulus-response data in a wide set of biological systems [123–128].”
From Fibromyalgia pathogenesis explained by a neuroendocrine multistable model (2024) · cites this paper as [124]
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“Although recent work has identified some potential mechanisms for excitatory regulation of cortical gain97,98, several lines of evidence suggest that cortical GABAergic inhibition has a crucial role in regulating the gain of sensory responses5,99,100 (FIG. 1).”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [100]
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“The majority of input to a neuron is received on the dendritic tree, potentially allowing transformations that extend far beyond the simple sum-and-threshold operation that is commonly assumed (London and H€ausser, 2005; Silver, 2010; Poirazi and Papoutsi, 2020).”
BackgroundAttentional modulation of neuronal variability in circuit models of cortex
Kanashiro, T., Ocker, G. K., Cohen, M. R., & Doiron, B. (2017) · eLife · doi:10.7554/elife.23978 · Cited by 81
Joshi, S., & Gold, J. I. (2022) · eLife · doi:10.7554/elife.63490 · Cited by 71
Connecting neural codes with behavior in the auditory system of Drosophila
Clemens, J., Girardin, C. C., Coen, P., Guan, X.-J., Dickson, B. J., & Murthy, M. (2015) · Neuron · doi:10.1016/j.neuron.2015.08.014 · Cited by 69
“Finally, the two integrated values are combined by divisive inhibition to yield an output that is used to control female speed (Gabbiani et al., 2002; Silver, 2010).”
Cerebellar granule cell axons support high-dimensional representations
Lanore, F., Cayco-Gajic, N. A., Gurnani, H., Coyle, D., & Silver, R. A. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00873-x · Cited by 80
Self-citationLayer 2/3 Pyramidal Neurons Control the Gain of Cortical Output
Quiquempoix, M., Fayad, S. L., Boutourlinsky, K., Leresche, N., Lambert, R. C., & Bessaïh, T. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.08.038 · Cited by 82
Modulation of Ion Channels in the Axon: Mechanisms and Function
Burke, K. J., & Bender, K. J. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00221 · Cited by 77
“In this way, a neuromodulatory weakening of Pr and increase in synaptic variability can surprisingly improve the encoding capability of a postsynaptic neuron by broadening its dynamic range (Silver, 2010).”
BackgroundBrown, J., Pan, W. X., & Dudman, J. T. (2014) · eLife · doi:10.7554/elife.02397 · Cited by 70
“In the case of gain control mediated by an interneuron this can be achieved in a number of ways, for example, through facilitating inhibitory synaptic transmission (Silver, 2010; Figure 9).”
Petersen, P. C., & Berg, R. W. (2016) · eLife · doi:10.7554/elife.18805 · Cited by 76
Phillips, W. A., & Silverstein, S. M. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00307 · Cited by 75
“There are various mechanisms for controlling gain within the cortex (e.g., Salinas, 2009; Silver, 2010).”
BackgroundInfluentialDu, K., Wu, Y., Lindroos, R., Liu, Y., Rózsa, B. J., Katona, G., Ding, J., & Kotaleski, J. H. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1704893114 · Cited by 73
Single-neuron representation of learned complex sounds in the auditory cortex
Wang, M., Liao, X., Li, R., Liang, S., Ding, R., Li, J., Zhang, J., He, W., Liu, K., Pan, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18142-z · Cited by 74
Ghrelin Inhibits Visceral Afferent Activation of Catecholamine Neurons in the Solitary Tract Nucleus
Cui, R., Li, X., & Appleyard, S. M. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.3187-10.2011 · Cited by 73
“Spontaneous firing is dependent on a complex integration of the effects of different inputs (Silver, 2010), which for TH-EGFP neurons are likely to include ST inputs, other afferent inputs from the CNS, and intra-NTS inputs, in addition to the intrinsic properties of the TH-EGFP neuron (Appleyard et al.”
BackgroundRibbon Synapses and Visual Processing in the Retina
Lagnado, L., & Schmitz, F. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035709 · Cited by 78
Joint coding of visual input and eye/head position in V1 of freely moving mice
Parker, P. R. L., Abe, E. T. T., Leonard, E. S. P., Martins, D. M., & Niell, C. M. (2022) · Neuron · doi:10.1016/j.neuron.2022.08.029 · Cited by 78
Neurosteroids and GABAergic signaling in health and disease
MacKenzie, G., & Maguire, J. L. (2012) · BioMolecular Concepts · doi:10.1515/bmc-2012-0033 · Cited by 72
“In addition, changes in tonic inhibition can impact the sensitivity of a neuron to changes in inputs (the neuronal gain) by shunting the background synaptic noise (54, 88, 89) [but see (90) ].”
Hearing loss and brain plasticity: the hyperactivity phenomenon
Herrmann, B., & Butler, B. E. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02313-9 · Cited by 55
“…can be broadly categorized as being either excitatory or inhibitory, and maintaining a balance between excitation and inhibition is crucial for neural function (Wehr and Zador 2003; Silver 2010; Froemke and Martins 2011; Isaacson and Scanziani 2011; Shew and Plenz 2013; Whitmire and Stanley 2016).”
BackgroundTowards a “canonical” agranular cortical microcircuit
Beul, S. F., & Hilgetag, C. C. (2015) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2014.00165 · Cited by 71
“We thank K.A.C. Martin anf H. Barbas for helpful comments on the manuscript.”
BackgroundO’Callaghan, C., Walpola, I. C., & Shine, J. M. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0699 · Cited by 77
Angelo, K., & Margrie, T. W. (2011) · Scientific Reports · doi:10.1038/srep00050 · Cited by 70
Information Processing in Dendrites and Spines
Spruston, N., Häusser, M., & Stuart, G. J. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-385870-2.00011-1 · Cited by 68
Cheng, Y., Shan, K., Xu, Y., Yang, J., He, J., & Jiang, J. (2020) · Nanoscale · doi:10.1039/d0nr04950f · Cited by 68
Dopamine Regulates Two Classes of Primate Prefrontal Neurons That Represent Sensory Signals
Jacob, S. N., Ott, T., & Nieder, A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0210-13.2013 · Cited by 70
“Modes of operation Inhibition was implemented principally in the form of a subtractive shift in response levels [additive operation (Silver, 2010); Figs.”
BackgroundMethodThe contribution of synaptic location to inhibitory gain control in pyramidal cells
Pouille, F., Watkinson, O., Scanziani, M., & Trevelyan, A. J. (2013) · Physiological Reports · doi:10.1002/phy2.67 · Cited by 65
Drawing inspiration from biological dendrites to empower artificial neural networks
Chavlis, S., & Poirazi, P. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.04.007 · Cited by 74
“The occurrence of these dendritic spikes, their kinetics, and location dependence expand the computing power of individual neurons [59].”
BackgroundBryson, A., Reid, C. A., & Petrou, S. (2023) · Journal of Neurochemistry · doi:10.1111/jnc.15769 · Cited by 67
Artificial synapses based on biopolymer electrolyte-coupled SnO 2 nanowire transistors
Gou, G., Sun, J., Qian, C., He, Y., Kong, L., Fu, Y., Dai, G., Yang, J., & Gao, Y. (2016) · Journal of Materials Chemistry C · doi:10.1039/c6tc03731c · Cited by 61
Cho, S. W., Kwon, S. M., Lee, M., Jo, J., Heo, J. S., Kim, Y., Cho, H. K., & Park, S. K. (2019) · Nano Energy · doi:10.1016/j.nanoen.2019.104097 · Cited by 61
Response-dependent dynamics of cell-specific inhibition in cortical networks in vivo
El-Boustani, S., & Sur, M. (2014) · Nature Communications · doi:10.1038/ncomms6689 · Cited by 61
“We do not exclude the possibility that the nature of GABA receptors targeted by these different inhibitory neuron types may differ 1 and could also participate in this mechanism, although how this would enable SOM+ neurons to switch their function remains to be clarified.”
Metadata
- Authors
- Silver, R. A.
- Affiliations
- University College London (GB)
- Corresponding author
- Silver, R. Angus
- ORCID
- Silver, R. Angus
- Venue
- Nature Reviews Neuroscience
- Published
- 2010-06-09
- Volume
- 11
- Issue
- 7
- Pages
- 474-489
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- PMC
- PMC4750293
- OpenAlex
- W4233766477
- Cited by
- 481
- References
- 177
- Citations by year
- 2026: 23 · 2025: 32 · 2024: 26 · 2023: 22 · 2022: 39 · 2021: 35 · 2020: 35 · 2019: 34 · 2018: 34 · 2017: 23 · 2016: 38 · 2015: 48 · 2014: 30 · 2013: 23 · 2012: 25
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (064413)
- Biotechnology and Biological Sciences Research Council (BB/F005490/1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- neural computation, synaptic plasticity, synaptic noise, neuronal morphology, neural coding, dendritic integration
- MeSH terms
- Animals, Dendrites, Humans, Mathematics, Nerve Net, Synaptic Transmission, Neurons, Synapses
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