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CORTICAL PLASTICITY: From Synapses to Maps
Abstract
It has been clear for almost two decades that cortical representations in adult animals are not fixed entities, but rather, are dynamic and are continuously modified by experience. The cortex can preferentially allocate area to represent the particular peripheral input sources that are proportionally most used. Alterations in cortical representations appear to underlie learning tasks dependent on the use of the behaviorally important peripheral inputs that they represent. The rules governing this cortical representational plasticity following manipulations of inputs, including learning, are increasingly well understood. In parallel with developments in the field of cortical map plasticity, studies of synaptic plasticity have characterized specific elementary forms of plasticity, including associative long-term potentiation and long-term depression of excitatory postsynaptic potentials. Investigators have made many important strides toward understanding the molecular underpinnings of these fundamental plasticity processes and toward defining the learning rules that govern their induction. The fields of cortical synaptic plasticity and cortical map plasticity have been implicitly linked by the hypothesis that synaptic plasticity underlies cortical map reorganization. Recent experimental and theoretical work has provided increasingly stronger support for this hypothesis. The goal of the current paper is to review the fields of both synaptic and cortical map plasticity with an em…
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“These theoretical predictions are entirely consistent with empirical and computational characterisations of plasticity [56],[57].”
From Hierarchical Models in the Brain (2008) · cites this paper as [56]
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Time-lapse electrical recordings of single neurons from the mouse neocortex
Cohen, L., Koffman, N., Meiri, H., Yarom, Y., Lampl, I., & Mizrahi, A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1214434110 · Cited by 31
Neuroplasticity of the Sensorimotor Cortex during Learning
Francis, J. T., & Song, W. (2011) · Neural Plasticity · doi:10.1155/2011/310737 · Cited by 37
“There are several recent publications on changes that take place to the synaptic substrate during sensorimotor learning [33, 48, 55], and as previously mentioned on changes in the sensorimotor map [7, 30, 31, 56].”
BackgroundDistinct Laminar Requirements for NMDA Receptors in Experience-Dependent Visual Cortical Plasticity
Fong, M., Finnie, P. S. B., Kim, T.-K., Thomazeau, A., Kaplan, E. S., Cooke, S. F., & Bear, M. F. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz260 · Cited by 29
Alwis, D. S., & Rajan, R. (2013) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2013.00124 · Cited by 33
“…et al., 1972; Globus et al., 1973; Greenough and Volkmar, 1973; Uylings et al., 1978; Sirevaag and Greenough, 1987; Rosenzweig and Bennett, 1996; Buonomano and Merzenich, 1998; Van Praag et al., 2000; Li and Tang, 2005; Nithianantharajah and Hannan, 2006; Sale et al., 2009), and increased…”
BackgroundInfluentialEmergence of Optimal Decoding of Population Codes Through STDP
Habenschuss, S., Puhr, H., & Maass, W. (2013) · Neural Computation · doi:10.1162/neco_a_00446 · Cited by 32
“The resulting allocation of cortical resources in proportion to relevance is strongly reminiscent of experimentally observed training-dependent cortical map plasticity (Buonomano & Merzenich, 1998).”
BackgroundInfluentialWhen is electrical cortical stimulation more likely to produce afterdischarges?
Lee, H. W., Webber, W. R., Crone, N. E., Miglioretti, D. L., & Lesser, R. P. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.10.001 · Cited by 31
“…connections (Buonomano and Merzenich, 1998; Abbott and Regehr, 2004; Chklovskii et al., 2004), or in the functional state of the network as a whole (Buonomano and Merzenich, 1998; Sanes and Donoghue, 2000; Nudo, 2003; Haider et al., 2007), and are consistent with the idea that rapid adaptive…”
BackgroundResultInfluentialRao, A. R., Rishiq, D., Yu, L., Zhang, Y., & Abrams, H. B. (2016) · Ear and Hearing · doi:10.1097/aud.0000000000000348 · Cited by 30
Goal-Directed Behavior and Instrumental Devaluation: A Neural System-Level Computational Model
Mannella, F., Mirolli, M., & Baldassarre, G. (2016) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2016.00181 · Cited by 33
“…are triggered via the activation of the related outcomes (goals) which are “suggested” by the current environment situation (within PFC circuits) but are then “filtered” by the animal internal states attributing differential incentive values to them (through the BLA/IC-NAc-PL neural pathway).”
BackgroundDing, J., Ding, X., Hua, B., Xiong, X., Wen, Y., Ding, Z., Wang, Q., & Thompson, P. M. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9793-9 · Cited by 30
“Even so, in some cases, cortical plasticity may enable the brain to reorganize by forming new neural or strengthened functional connections (Nudo et al. 1996; Buonomano and Merzenich 1998; Dancause et al. 2005).”
BackgroundHeusler, P., Cebulla, B., Boehmer, G., & Dinse, H. R. (2000) · Experimental Brain Research · doi:10.1007/s002210000530 · Cited by 31
Della‐Maggiore, V., Grady, C. L., & McIntosh, A. R. (2002) · Reviews in the Neurosciences · doi:10.1515/revneuro.2002.13.2.167 · Cited by 31
An associative network with spatially organized connectivityPreprint
Roudi, Y., & Treves, R. (2004) · Cited by 33
Troncoso, E. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhg131 · Cited by 30
Intra– and inter–hemispheric network dynamics supporting object recognition and speech production
Kitazawa, Y., Sonoda, M., Sakakura, K., Mitsuhashi, T., Firestone, E., Ueda, R., Kambara, T., Iwaki, H., Luat, A. F., Marupudi, N. I., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119954 · Cited by 29
“…the enhancement of direct functional connectivity using the concept that cortical regions with significant, simultaneous, and sustained high-frequency neural responses beyond a chance level are likely involved in a coordinated interaction (Singer, 1993, 2018; Buonomano and Merzenich, 1998).”
Hancock, N., Shepstone, L., Winterbotham, W., & Pomeroy, V. M. (2011) · International Journal of Stroke · doi:10.1111/j.1747-4949.2011.00728.x · Cited by 28
Increased excitability of cortical neurons induced by associative learning: an ex vivo study
Bekisz, M., Garkun, Y., Wabno, J., Hess, G., Wróbel, A., & Kossut, M. M. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07453.x · Cited by 29
“In the adult neocortex, representational maps can be modified by sensory experience and by learning (Kossut, 1992; Buonomano & Merzenich, 1998; Kilgard et al., 2002; Feldman & Brecht, 2005; Ohl & Scheich, 2005; Weinberger et al., 2009).”
Background
Metadata
- Authors
- Buonomano, D. V., & Merzenich, M. M.
- Affiliations
- University of California, Los Angeles (US)
- University of California, San Francisco (US)
- Venue
- Annual Review of Neuroscience
- Publisher
- Annual Reviews
- Published
- 1998-03
- Volume
- 21
- Issue
- 1
- Pages
- 149-186
- ISSN
- 0147-006X (ISSN-L)
- 1545-4126
- OpenAlex
- W2158935809
- Cited by
- 2,083
- References
- 192
- Citations by year
- 2026: 21 · 2025: 27 · 2024: 43 · 2023: 40 · 2022: 45 · 2021: 58 · 2020: 49 · 2019: 73 · 2018: 67 · 2017: 61 · 2016: 61 · 2015: 78 · 2014: 72 · 2013: 95 · 2012: 98
- FWCI
- 8.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Public Health Service (10414)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- cortical plasticity, synaptic plasticity, long-term potentiation, long-term depression, excitatory postsynaptic potentials, learning rules
- MeSH terms
- Animals, Brain Mapping, Cerebral Cortex, Neuronal Plasticity, Synapses
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