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Barrages of Synaptic Activity Control the Gain and Sensitivity of Cortical Neurons
Open accessAbstract
Ongoing synaptic activity, ever present in cortical neurons, may vary widely in its amplitude and characteristics, potentially having a strong influence on neuronal processing. Intracellular recordings in layer 5 pyramidal cells in prefrontal and visual cortical slices maintained in vitro revealed spontaneous periods of synaptic bombardment. Testing the responsiveness of these cortical cells to synaptic inputs or the injection of artificial excitatory postsynaptic conductances of various amplitudes revealed that background synaptic activity dramatically increased the probability of response to small inputs, decreased the slope of the input-output curve, and decreased both the latency and jitter of action potential activation. Examining the effects of different components of synaptic barrages (namely, depolarization, increase in membrane conductance, and increase in membrane potential variance) revealed that the effects observed were dominated by the membrane depolarization and increase in variance. Depolarization increased the peak cross-correlation between injected complex in vivo-like waveforms through enhancement of responsiveness to small inputs, whereas increases in variance did so through a shift in firing mode from one of threshold detection to probabilistic discharge. These results indicate that rapid increases in neuronal responsiveness, as well as increases in spike timing precision, can be achieved through balanced barrages of excitatory and inhibitory synaptic activity.
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“Changes in the level of synaptically driven fluctuations in Vm (also called synaptic fluctuations or synaptic noise) may regulate the gain of the I/O curve for individual neurons57–60 (but see REF.61).”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [60]
In the citing paper
“Together, temporally coincident changes in conductance state and synaptic fluctuations exert a powerful effect on neuronal gain at the single-cell level57,58,60,61,83,84.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [60]
In the citing paper
“Unlike synaptically driven Vm fluctuations, changes in membrane conductance and depolarization cause lateral, or additive, shifts in the I/O transfer function of a neuron without changes in neuronal gain57,58,60,61,83,84.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [60]
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Citations
Cites
On the Theory of the Brownian Motion
Uhlenbeck, G. E., & Ornstein, L. S. (1930) · Physical Review · doi:10.1103/physrev.36.823 · Cited by 4,713
Steriade, M., Nunez, A., & Amzica, F. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-08-03252.1993 · Cited by 1,903
Cellular and network mechanisms of rhythmic recurrent activity in neocortex
Sanchez-Vives, M. V., & McCormick, D. A. (2000) · Nature Neuroscience · doi:10.1038/79848 · Cited by 1,539
Self-citationFeature-based attention influences motion processing gain in macaque visual cortex
Treue, S., & Trujillo, J. C. M. (1999) · Nature · doi:10.1038/21176 · Cited by 1,465
Encoding of Spatial Location by Posterior Parietal Neurons
Andersen, R., Essick, G. K., & Siegel, R. M. (1985) · Science · doi:10.1126/science.4048942 · Cited by 1,413
Abeles, M. (1991) · Cambridge University Press eBooks · doi:10.1017/cbo9780511574566 · Cited by 1,341
Natural waking and sleep states: a view from inside neocortical neurons
Steriade, M., Timofeev, I., & Grenier, F. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.85.5.1969 · Cited by 1,296
Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4
McAdams, C. J., & Maunsell, J. H. R. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-01-00431.1999 · Cited by 1,218
Turning on and off recurrent balanced cortical activity
Shu, Y., Hasenstaub, A., & McCormick, D. A. (2003) · Nature · doi:10.1038/nature01616 · Cited by 1,054
Self-citationAttention Increases Sensitivity of V4 Neurons
Reynolds, J. H., Pasternak, T., & Desimone, R. (2000) · Neuron · doi:10.1016/s0896-6273(00)81206-4 · Cited by 1,034
Research works printed but not linked: 2
- 1988, Exp Brain Res, 70, 463
- 1993, Trends Neurosci, 17, 147
Cited by
Sorted by citations
Items 201–297 of 297
Modulation of Firing Rate by Background Synaptic Noise Statistics in Rat Visual Cortical Neurons
Sceniak, M. P., & Sabo, S. L. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00023.2010 · Cited by 9
Mechanisms of Gain Control by Voltage-Gated Channels in Intrinsically-Firing Neurons
Patel, A. X., & Burdakov, D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0115431 · Cited by 10
Self-Regulation of Blood Oxygenation Level Dependent Response: Primary Effect or Epiphenomenon?
Carìa, A. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00117 · Cited by 8
Biologically Motivated Algorithms for Propagating Local Target Representations
Ororbia, A. G., & Mali, A. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014651 · Cited by 11
Effects of Cortical Cooling on Activity Across Layers of the Rat Barrel Cortex
Burkhanova, G., Chernova, K., Khazipov, R., & Sheroziya, M. (2020) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.00052 · Cited by 7
Prescott, S. A., & Koninck, Y. D. (2009) · Springer eBooks · doi:10.1007/978-0-387-89279-5_5 · Cited by 7
High-conductance states on a neuromorphic hardware system
Kaplan, B. A., Brüderle, D., Schemmel, J., & Meier, K. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178951 · Cited by 8
Reuveni, I., Ghosh, S., & Barkai, E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005306 · Cited by 9
Neuroimaging, Neural Population Models for
Bojak, I., & Breakspear, M. J. (2014) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_70-1 · Cited by 8
Neuroscientific Modeling with a Mixed-Signal VLSI Hardware System
Brüderle, D. (2009) · heiDOK (Heidelberg University) · doi:10.11588/heidok.00009656 · Cited by 9
Ross, G., Radtke‐Schuller, S., & Frӧhlich, F. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105701 · Cited by 6
Sadoc, G., Masson, G. L., Foutry, B., Franc, Y. L., Piwkowska, Z., Alain Destexhe, A., & Bal, T. (2009) · Springer eBooks · doi:10.1007/978-0-387-89279-5_14 · Cited by 7
Self-citationRobust effects of corticothalamic feedback and behavioral state on movie responses in mouse dLGNPreprint
Spacek, M. A., Crombie, D., Bauer, Y., Born, G., Liu, X., Katzner, S., & Busse, L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/776237 · Cited by 8
Probing multiplexed basal dendritic computations using two-photon 3D holographic uncaging
Xiao, S., Yadav, S., & Jayant, K. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114413 · Cited by 6
Background synaptic input modulates the visuospatial working memory
Wang, T., Sun, J., Yang, F., Li, J., Wang, W., & Liu, F. (2021) · Physical review. E · doi:10.1103/physreve.104.024416 · Cited by 6
Noise Enhancement of Neural Information Processing
Alain Destexhe, A. (2022) · Entropy · doi:10.3390/e24121837 · Cited by 8
Marrero, K., Aruljothi, K., Zareian, B., Gao, C., Zhang, Z., & Zagha, E. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab339 · Cited by 8
Receiver Operating Characteristic (ROC) Analysis for Characterizing Synaptic Efficacy
Chance, F. S. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00885.2006 · Cited by 6
Akhshi, A., Metzen, M. G., Chacron, M. J., & Khadra, A. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013711 · Cited by 5
Bertero, A., & Apicella, A. J. (2024) · The Journal of Physiology · doi:10.1113/jp286094 · Cited by 5
Kada, H., Teramae, J.-N., & Tokuda, I. T. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00109 · Cited by 5
Tartaglia, E. M., Brunel, N., & Mongillo, G. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004059 · Cited by 5
Greenhill, S., Chamberlain, S. E. L., Lench, A. M., Massey, P. V., Yuill, K. H., Woodhall, G. L., & Jones, R. S. G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085125 · Cited by 5
Altwegg‐Boussac, T., Mahon, S., Chávez, M., Charpier, S., & Schramm, A. E. (2016) · Journal of Visualized Experiments · doi:10.3791/53576 · Cited by 5
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Chance, F. S., & Abbott, L. F. (2009) · Springer eBooks · doi:10.1007/978-0-387-89279-5_4 · Cited by 5
Piwkowska, Z., Alain Destexhe, A., & Bal, T. (2009) · Springer eBooks · doi:10.1007/978-0-387-89279-5_1 · Cited by 5
Self-citationHuo, Q., Tabassum, S., Chen, M., Sun, M., Deng, Y., Zheng, X., Li, Y., Chen, J., Long, C., & Yang, L. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201557 · Cited by 4
Bershadskii, A., & Ikegaya, Y. (2011) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2011.03.001 · Cited by 6
Carton‐Leclercq, A., Lecas, S., Chávez, M., Charpier, S., & Mahon, S. (2020) · The Journal of Physiology · doi:10.1113/jp280266 · Cited by 4
Zacharopoulos, G., & Kadosh, R. C. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.10.037 · Cited by 4
Lee, T., & Tramontano, G. (2021) · World Journal of Neuroscience · doi:10.4236/wjns.2021.112014 · Cited by 3
Rosenkranz, J. A. (2012) · Neuroscience · doi:10.1016/j.neuroscience.2012.09.012 · Cited by 4
Sun, Q., & Zhang, Z. (2010) · Journal of Neuroscience Research · doi:10.1002/jnr.22530 · Cited by 4
COMPLEXITY IN NEURONAL NETWORKS
Frégnac, Y., Rudolph, M., Davison, A. P., & Alain Destexhe, A. (2007) · Complex systems and interdisciplinary science · doi:10.1142/9789812772367_0009 · Cited by 6
Cortical modulation by exogenous electric fields is consistent with electric dipoles
Covelo, J., Colom, J., Weinert, J. F., D’Andola, M., Alain Destexhe, A., & Sanchez-Vives, M. V. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.10.019 · Cited by 3
Wu, R., Yang, P., Wang, F., Liu, Q., Gore, J. C., & Chen, L. M. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0034-22.2022 · Cited by 3
The influence of increased membrane conductance on response properties of spinal motoneurons
Grigonis, R., Guzulaitis, R., Buišas, R., & Alaburda, A. (2016) · Brain Research · doi:10.1016/j.brainres.2016.07.030 · Cited by 3
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Timofeev, I., Bonjean, M., & Bazhenov, M. (2020) · Springer eBooks · doi:10.1007/978-1-0716-0653-7_5 · Cited by 4
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Elliott, T., Kuang, X., Shadbolt, N. R., & Zauner, K. (2008) · Network Computation in Neural Systems · doi:10.1080/09548980802244221 · Cited by 3
Shinozaki, T., Naruse, Y., & Câteau, H. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.04.011 · Cited by 3
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Mihalaş, Ş., Millman, D. J., Iyer, R., Kirkwood, A., & Niebur, E. (2014) · Wiley eBooks · doi:10.1002/9783527651009.ch21 · Cited by 3
Neuroimaging, Neural Population Models for
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Critical scaling of novelty in the cortex
Ribeiro, T. L., Vakili, A., Gifford, B., Siddiqui, R., Sinfuego, V., Pajevic, S., & Plenz, D. (2026) · Nature Communications · doi:10.1038/s41467-025-68277-0 · Cited by 3
Plenz, D. (2010) · Nature Physics · doi:10.1038/nphys1796 · Cited by 2
Liao, S., Hou, G., Pan, X. S., Liao, C., & Li, D. (2012) · Neural Plasticity · doi:10.1155/2012/931780 · Cited by 2
Visual stimulation quenches global alpha range activity in awake primate V4: a case study
Deneux, T., Masquelier, T., Bermúdez, M. A., Masson, G. S., Deco, G., & Vanzetta, I. (2017) · Neurophotonics · doi:10.1117/1.nph.4.3.031222 · Cited by 2
Kada, H., Teramae, J.-N., & Tokuda, I. T. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00104 · Cited by 2
Shu, Y., Hasenstaub, A. R., & McCormick, D. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.12.548753 · Cited by 2
Self-citationUnpredictable movement-evoked pain alters cortical motor preparatory activity
Ogalo, E., Ro, H. J., Ortiz, O., Linde, L. D., Berger, M., & Kramer, J. L. K. (2025) · Journal of Neurophysiology · doi:10.1152/jn.00237.2025 · Cited by 1
Adaptation in multisensory neurons: Impact on cross-modal enhancement
Elliott, T., Kuang, X., Shadbolt, N. R., & Zauner, K. (2009) · Network Computation in Neural Systems · doi:10.1080/09548980902751752 · Cited by 1
Noisy Dendrites: Models of Dendritic Integration In Vivo
Alain Destexhe, A., & Rudolph, M. (2013) · Springer series in computational neuroscience · doi:10.1007/978-1-4614-8094-5_11 · Cited by 1
How neuronal computations depend on network state: another piece in the puzzle
Alain Destexhe, A. (2014) · The Journal of Physiology · doi:10.1113/jphysiol.2014.277392 · Cited by 1
Brette, R., Piwkowska, Z., Monier, C., Francisco, J., González, G., Frégnac, Y., Bal, T., & Alain Destexhe, A. (2009) · Springer eBooks · doi:10.1007/978-0-387-89279-5_16 · Cited by 1
Self-citationGain Modulation and Stability in Neural Networks
Chance, F. S. (2008) · Elsevier eBooks · doi:10.1016/b978-012373649-9.50014-4 · Cited by 1
Piwkowska, Z., Pospischil, M., Rudolph, M., Bal, T., & Alain Destexhe, A. (2009) · Springer eBooks · doi:10.1007/978-0-387-89279-5_6 · Cited by 1
Self-citationHumble, J. (2013) · PEARL (University of Plymouth) · doi:10.24382/2202 · Cited by 1
Inhibitory conductance dynamics in cortical neurons during activated states
Pospischil, M., Piwkowska, Z., Rudolph, M., Bal, T., & Alain Destexhe, A. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.10.040 · Cited by 1
Self-citationInformation Circulation in The Advanced Neural Activities of Central Neural SystemPreprint
Ma, F. (2020) · Preprints.org · doi:10.20944/preprints202008.0195.v1
Visual modulation of spectrotemporal receptive fields in mouse auditory cortexPreprint
Bigelow, J., Morrill, R. J., Olsen, T., Bazarini, S. N., & Hasenstaub, A. R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.06.455445 · Cited by 1
Self-citationTseng, K.-Y. (2007) · Springer eBooks · doi:10.1007/978-0-387-72256-6_11
Recurrent Cortical Network Activity and Modulation of Synaptic Transmission
Shu, Y. (2008) · Wiley eBooks · doi:10.1002/9783527625192.ch6
Self-citationThe role of synaptic noise in cortical excitability.
Greenhill, S. (2008) · The University of Bath Online Publications Store (The University of Bath)
Theta Frequency Background Tunes Transmission but Not Summation of Spiking Responses
Parameshwaran, D., & Bhalla, U. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055607
Chang, C. Y. (2010) · Open Scholarship Institutional Repository (Washington University in St. Louis) · doi:10.7936/k7xg9p5c
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Mehaffey, W. H. (2008) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/2082
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Li, X., Morita, K., Robinson, H. P. C., & SMALL, M. P. (2010) · Springer eBooks · doi:10.1007/978-90-481-9695-1_28
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Hollender, L. (2010) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t3st7q0v
The role of serotonin in cortical excitability and network dynamics
Puzerey, P. A. (2015) · OhioLink ETD Center (Ohio Library and Information Network)
Pena, R. F. O., Zaks, M. A., & Roque, A. C. (2017) · arXiv (Cornell University)
Implications of a spontaneously active ground state for computing with brain-inspired circuits
Srinivasa, N. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050929
Pena, R. F. O. (2019) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.59.2019.tde-18012019-095012
Lampl, I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/690719
Ham, G. X. (2021) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/155180
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Mashour, G. A. (2014) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.723876467.793500207
Neuroimaging, Neural Population Models for
Bojak, I., & Breakspear, M. J. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_70
Xiao, S., Yadav, S., & Jayant, K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.26.509562
Gaugain, G., Modolo, J., & Nikolayev, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.24.690108
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deRosenroll, G., Sethuramanujam, S., & Awatramani, G. B. (2026) · Cell Reports · doi:10.1016/j.celrep.2025.116833
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Marco, R., Ortega, E., López, P., & Martínez, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6380715
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Pouille, F., & Theyel, B. B. (2026) · Journal of Neurophysiology · doi:10.1152/jn.00577.2025
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Corlier, J. (2015) · theses.fr (ABES) · doi:10.70675/7ceb116bz9921z42dfzbb8fz9f534a47bbcd
Altwegg‐Boussac, T. (2015) · theses.fr (ABES) · doi:10.70675/b00291fezcae2z498az82e5zaf11f0672b45
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Faini, G. (2017) · theses.fr (ABES) · doi:10.70675/7b38d504z7edfz47dezbf9az0a8668ae448a
Les déterminants du seuil du potentiel d'action dans les neurones corticaux
Fékété, A. (2018) · theses.fr (ABES) · doi:10.70675/d163929dz45c2z4184za5baz6ae759b34ff3
Structure and sensitivity of neural population responses in the retina
Gardella, C. (2017) · theses.fr (ABES) · doi:10.70675/8c3c933fzb5afz434cz8207z919452629f2d
Physiopathologie des comas isoélectriques : de l’EEG au neurone
Carton‐Leclercq, A. (2023) · theses.fr (ABES) · doi:10.70675/7e9150a6z8959z43baza4b4zcaf0222fc163
Resting-State Alpha Band Characteristics and Oral Language Abilities in Middle ChildhoodPreprint
Chahal, K., Gibbings, A., Cardy, J. O., & Janes, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7589194
Ham, G. X., & Augustine, G. J. (2026) · Cells · doi:10.3390/cells15211937
Alain Destexhe, A. (2013) · Springer eBooks · doi:10.1007/978-1-4614-1424-7_8
Metadata
- Authors
- Shu, Y., Hasenstaub, A., Badoual, M., Bal, T., & McCormick, D. A.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2003-11-12
- Volume
- 23
- Issue
- 32
- Pages
- 10388-10401
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6741011
- OpenAlex
- W1955923252
- Cited by
- 298
- References
- 51
- Citations by year
- 2026: 7 · 2025: 4 · 2024: 4 · 2023: 7 · 2022: 13 · 2021: 12 · 2020: 10 · 2019: 9 · 2018: 8 · 2017: 14 · 2016: 13 · 2015: 15 · 2014: 12 · 2013: 13 · 2012: 11
- FWCI
- 7.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- European Commission (IST-2001-34712)
- Centre National de la Recherche Scientifique
- National Institutes of Health
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Keywords
- cortical neurons, layer 5 pyramidal cells, prefrontal cortex, visual cortex, neuronal gain, neuronal sensitivity, input-output curves, membrane depolarization, spike timing precision, threshold detection, excitation-inhibition balance, spontaneous synaptic activity
- MeSH terms
- Action Potentials, Animals, Cerebral Cortex, Electric Stimulation, Ferrets, Nerve Net, Synaptic Transmission, Neurons, Occipital Lobe, Reaction Time, Sensory Thresholds, Prefrontal Cortex, Patch-Clamp Techniques, Excitatory Postsynaptic Potentials, In Vitro Techniques
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