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Neklyudova A, Kuramagomedova R, Voinova V, Sysoeva O. Atypical brain responses to 40-Hz click trains in girls with Rett syndrome: Auditory steady-state response and sustained wave. Psychiatry Clin Neurosci 2024; 78:282-290. [PMID: 38321640 DOI: 10.1111/pcn.13638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/01/2023] [Accepted: 12/27/2023] [Indexed: 02/08/2024]
Abstract
AIM The current study aimed to infer neurophysiological mechanisms of auditory processing in children with Rett syndrome (RTT)-rare neurodevelopmental disorders caused by MECP2 mutations. We examined two brain responses elicited by 40-Hz click trains: auditory steady-state response (ASSR), which reflects fine temporal analysis of auditory input, and sustained wave (SW), which is associated with integral processing of the auditory signal. METHODS We recorded electroencephalogram findings in 43 patients with RTT (aged 2.92-17.1 years) and 43 typically developing children of the same age during 40-Hz click train auditory stimulation, which lasted for 500 ms and was presented with interstimulus intervals of 500 to 800 ms. Mixed-model ancova with age as a covariate was used to compare amplitude of ASSR and SW between groups, taking into account the temporal dynamics and topography of the responses. RESULTS Amplitude of SW was atypically small in children with RTT starting from early childhood, with the difference from typically developing children decreasing with age. ASSR showed a different pattern of developmental changes: the between-group difference was negligible in early childhood but increased with age as ASSR increased in the typically developing group, but not in those with RTT. Moreover, ASSR was associated with expressive speech development in patients, so that children who could use words had more pronounced ASSR. CONCLUSION ASSR and SW show promise as noninvasive electrophysiological biomarkers of auditory processing that have clinical relevance and can shed light onto the link between genetic impairment and the RTT phenotype.
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Affiliation(s)
- Anastasia Neklyudova
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia
| | - Rabiat Kuramagomedova
- Veltischev Research and Clinical Institute for Pediatrics of the Pirogov, Russian National Research Medical University, Ministry of Health of Russian Federation, Moscow, Russia
| | - Victoria Voinova
- Veltischev Research and Clinical Institute for Pediatrics of the Pirogov, Russian National Research Medical University, Ministry of Health of Russian Federation, Moscow, Russia
| | - Olga Sysoeva
- Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Science, Moscow, Russia
- Faculty of Biology and Biotechnology, HSE University, Moscow, Russia
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2
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Aydin AG, Lemenze A, Bieszczad KM. Functional diversities within neurons and astrocytes in the adult rat auditory cortex revealed by single-nucleus RNA sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.16.589831. [PMID: 38659766 PMCID: PMC11042262 DOI: 10.1101/2024.04.16.589831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
The mammalian cerebral cortex is composed of a rich diversity of cell types. Cortical cells are organized into networks that rely on their functional diversity to ultimately carry out a variety of sophisticated cognitive functions. To investigate the breadth of transcriptional diverse cell types in the sensory cortex, we have used single-nucleus RNA sequencing (snRNA-seq) in the auditory cortex of the adult rat. A variety of unique excitatory and inhibitory neuron types were identified. In addition, we report for the first time a diversity of astrocytes in the auditory cortex that may represent functionally unique subtypes. Together, these results pave the way for building models of how neurons in the sensory cortex work in concert with astrocytes at synapses to fulfill high-cognitive functions like learning and memory.
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3
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Dura-Bernal S, Griffith EY, Barczak A, O'Connell MN, McGinnis T, Moreira JVS, Schroeder CE, Lytton WW, Lakatos P, Neymotin SA. Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics. Cell Rep 2023; 42:113378. [PMID: 37925640 PMCID: PMC10727489 DOI: 10.1016/j.celrep.2023.113378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/05/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023] Open
Abstract
We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Erica Y Griffith
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Annamaria Barczak
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Monica N O'Connell
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Tammy McGinnis
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Departments of Psychiatry and Neurology, Columbia University Medical Center, New York, NY, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Kings County Hospital Center, Brooklyn, NY, USA
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA.
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4
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Chien VSC, Wang P, Maess B, Fishman Y, Knösche TR. Laminar neural dynamics of auditory evoked responses: Computational modeling of local field potentials in auditory cortex of non-human primates. Neuroimage 2023; 281:120364. [PMID: 37683810 DOI: 10.1016/j.neuroimage.2023.120364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 08/15/2023] [Accepted: 09/04/2023] [Indexed: 09/10/2023] Open
Abstract
Evoked neural responses to sensory stimuli have been extensively investigated in humans and animal models both to enhance our understanding of brain function and to aid in clinical diagnosis of neurological and neuropsychiatric conditions. Recording and imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), local field potentials (LFPs), and calcium imaging provide complementary information about different aspects of brain activity at different spatial and temporal scales. Modeling and simulations provide a way to integrate these different types of information to clarify underlying neural mechanisms. In this study, we aimed to shed light on the neural dynamics underlying auditory evoked responses by fitting a rate-based model to LFPs recorded via multi-contact electrodes which simultaneously sampled neural activity across cortical laminae. Recordings included neural population responses to best-frequency (BF) and non-BF tones at four representative sites in primary auditory cortex (A1) of awake monkeys. The model considered major neural populations of excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SOM) neurons across layers 2/3, 4, and 5/6. Unknown parameters, including the connection strength between the populations, were fitted to the data. Our results revealed similar population dynamics, fitted model parameters, predicted equivalent current dipoles (ECD), tuning curves, and lateral inhibition profiles across recording sites and animals, in spite of quite different extracellular current distributions. We found that PV firing rates were higher in BF than in non-BF responses, mainly due to different strengths of tonotopic thalamic input, whereas SOM firing rates were higher in non-BF than in BF responses due to lateral inhibition. In conclusion, we demonstrate the feasibility of the model-fitting approach in identifying the contributions of cell-type specific population activity to stimulus-evoked LFPs across cortical laminae, providing a foundation for further investigations into the dynamics of neural circuits underlying cortical sensory processing.
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Affiliation(s)
- Vincent S C Chien
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Institute of Computer Science of the Czech Academy of Sciences, Czech Republic
| | - Peng Wang
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany; Institute of Psychology, University of Greifswald, Germany; Institute of Psychology, University of Regensburg, Germany
| | - Burkhard Maess
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany
| | - Yonatan Fishman
- Departments of Neurology and Neuroscience, Albert Einstein College of Medicine, USA
| | - Thomas R Knösche
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany.
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5
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Audette NJ, Schneider DM. Stimulus-Specific Prediction Error Neurons in Mouse Auditory Cortex. J Neurosci 2023; 43:7119-7129. [PMID: 37699716 PMCID: PMC10601367 DOI: 10.1523/jneurosci.0512-23.2023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 08/07/2023] [Accepted: 09/04/2023] [Indexed: 09/14/2023] Open
Abstract
Comparing expectation with experience is an important neural computation performed throughout the brain and is a hallmark of predictive processing. Experiments that alter the sensory outcome of an animal's behavior reveal enhanced neural responses to unexpected self-generated stimuli, indicating that populations of neurons in sensory cortex may reflect prediction errors (PEs), mismatches between expectation and experience. However, enhanced neural responses to self-generated stimuli could also arise through nonpredictive mechanisms, such as the movement-based facilitation of a neuron's inherent sound responses. If sensory prediction error neurons exist in sensory cortex, it is unknown whether they manifest as general error responses, or respond with specificity to errors in distinct stimulus dimensions. To answer these questions, we trained mice of either sex to expect the outcome of a simple sound-generating behavior and recorded auditory cortex activity as mice heard either the expected sound or sounds that deviated from expectation in one of multiple distinct dimensions. Our data reveal that the auditory cortex learns to suppress responses to self-generated sounds along multiple acoustic dimensions simultaneously. We identify a distinct population of auditory cortex neurons that are not responsive to passive sounds or to the expected sound but that encode prediction errors. These prediction error neurons are abundant only in animals with a learned motor-sensory expectation, and encode one or two specific violations rather than a generic error signal. Together, these findings reveal that cortical predictions about self-generated sounds have specificity in multiple simultaneous dimensions and that cortical prediction error neurons encode specific violations from expectation.SIGNIFICANCE STATEMENT Audette et. al record neural activity in the auditory cortex while mice perform a sound-generating forelimb movement and measure neural responses to sounds that violate an animal's expectation in different ways. They find that predictions about self-generated sounds are highly specific across multiple stimulus dimensions and that a population of typically nonsound-responsive neurons respond to sounds that violate an animal's expectation in a specific way. These results identify specific prediction error (PE) signals in the mouse auditory cortex and suggest that errors may be calculated early in sensory processing.
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Affiliation(s)
- Nicholas J Audette
- Center for Neural Science, New York University, New York, New York 10003
| | - David M Schneider
- Center for Neural Science, New York University, New York, New York 10003
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6
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Kern FB, Chao ZC. Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks. PLoS Comput Biol 2023; 19:e1011554. [PMID: 37831721 PMCID: PMC10599548 DOI: 10.1371/journal.pcbi.1011554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/25/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023] Open
Abstract
Sensory areas of cortex respond more strongly to infrequent stimuli when these violate previously established regularities, a phenomenon known as deviance detection (DD). Previous modeling work has mainly attempted to explain DD on the basis of synaptic plasticity. However, a large fraction of cortical neurons also exhibit firing rate adaptation, an underexplored potential mechanism. Here, we investigate DD in a spiking neuronal network model with two types of short-term plasticity, fast synaptic short-term depression (STD) and slower threshold adaptation (TA). We probe the model with an oddball stimulation paradigm and assess DD by evaluating the network responses. We find that TA is sufficient to elicit DD. It achieves this by habituating neurons near the stimulation site that respond earliest to the frequently presented standard stimulus (local fatigue), which diminishes the response and promotes the recovery (global fatigue) of the wider network. Further, we find a synergy effect between STD and TA, where they interact with each other to achieve greater DD than the sum of their individual effects. We show that this synergy is caused by the local fatigue added by STD, which inhibits the global response to the frequently presented stimulus, allowing greater recovery of TA-mediated global fatigue and making the network more responsive to the deviant stimulus. Finally, we show that the magnitude of DD strongly depends on the timescale of stimulation. We conclude that highly predictable information can be encoded in strong local fatigue, which allows greater global recovery and subsequent heightened sensitivity for DD.
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Affiliation(s)
- Felix Benjamin Kern
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan
| | - Zenas C. Chao
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan
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7
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Maes A, Barahona M, Clopath C. Long- and short-term history effects in a spiking network model of statistical learning. Sci Rep 2023; 13:12939. [PMID: 37558704 PMCID: PMC10412617 DOI: 10.1038/s41598-023-39108-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/20/2023] [Indexed: 08/11/2023] Open
Abstract
The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability distributions. In other words, the network spends more time in states which encode high-probability stimuli. Starting from the neural assembly, increasingly thought of to be the building block for computation in the brain, we focus on how arbitrary prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing learning.
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Affiliation(s)
- Amadeus Maes
- Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, USA.
- Department of Bioengineering, Imperial College London, London, UK.
| | | | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, UK
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8
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Bernáez Timón L, Ekelmans P, Kraynyukova N, Rose T, Busse L, Tchumatchenko T. How to incorporate biological insights into network models and why it matters. J Physiol 2023; 601:3037-3053. [PMID: 36069408 DOI: 10.1113/jp282755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/24/2022] [Indexed: 11/08/2022] Open
Abstract
Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.
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Affiliation(s)
- Laura Bernáez Timón
- Institute for Physiological Chemistry, University of Mainz Medical Center, Mainz, Germany
| | - Pierre Ekelmans
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
| | - Nataliya Kraynyukova
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Tobias Rose
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Laura Busse
- Division of Neurobiology, Faculty of Biology, LMU Munich, Munich, Germany
- Bernstein Center for Computational Neuroscience, Munich, Germany
| | - Tatjana Tchumatchenko
- Institute for Physiological Chemistry, University of Mainz Medical Center, Mainz, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
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9
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Sawicki J, Berner R, Loos SAM, Anvari M, Bader R, Barfuss W, Botta N, Brede N, Franović I, Gauthier DJ, Goldt S, Hajizadeh A, Hövel P, Karin O, Lorenz-Spreen P, Miehl C, Mölter J, Olmi S, Schöll E, Seif A, Tass PA, Volpe G, Yanchuk S, Kurths J. Perspectives on adaptive dynamical systems. CHAOS (WOODBURY, N.Y.) 2023; 33:071501. [PMID: 37486668 DOI: 10.1063/5.0147231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/24/2023] [Indexed: 07/25/2023]
Abstract
Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.
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Affiliation(s)
- Jakub Sawicki
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Rico Berner
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
| | - Sarah A M Loos
- DAMTP, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Mehrnaz Anvari
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53757 Sankt-Augustin, Germany
| | - Rolf Bader
- Institute of Systematic Musicology, University of Hamburg, Hamburg, Germany
| | - Wolfram Barfuss
- Transdisciplinary Research Area: Sustainable Futures, University of Bonn, 53113 Bonn, Germany
- Center for Development Research (ZEF), University of Bonn, 53113 Bonn, Germany
| | - Nicola Botta
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Computer Science and Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Nuria Brede
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Computer Science, University of Potsdam, An der Bahn 2, 14476 Potsdam, Germany
| | - Igor Franović
- Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia
| | - Daniel J Gauthier
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
| | - Sebastian Goldt
- Department of Physics, International School of Advanced Studies (SISSA), Trieste, Italy
| | - Aida Hajizadeh
- Research Group Comparative Neuroscience, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Philipp Hövel
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
| | - Omer Karin
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Philipp Lorenz-Spreen
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Christoph Miehl
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Jan Mölter
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748 Garching bei München, Germany
| | - Simona Olmi
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Eckehard Schöll
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Alireza Seif
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, USA
| | - Peter A Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California 94304, USA
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
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10
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Zhou B, Tomioka R, Song WJ. Temporal profiles of neuronal responses to repeated tone stimuli in the mouse primary auditory cortex. Hear Res 2023; 430:108710. [PMID: 36758331 DOI: 10.1016/j.heares.2023.108710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/26/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023]
Abstract
How the auditory system processes temporal information of sound has been investigated extensively using repeated stimuli. Recent studies on how the response of neurons in the primary auditory cortex (A1) changes with the progression of stimulus repetition, have reported response temporal profiles of two categories: "adaptation", i.e., gradual decrease, and "facilitation", i.e., gradual increase. To explore the existence of profiles of other categories and to examine the tone-frequency-dependence of the profile category in single neurons, here we studied the response of mouse A1 neurons to four or five tone-trains; each train comprised 10 identical tone pips, with 0.5-s inter-tone-intervals, and the four or five trains differed only in tone frequency. The response to each tone in a train was evaluated using the peak of the ON response, and how the peak response changed with the tone number in the train, i.e., the response temporal profile, was examined. We confirmed the existence of profiles of both "adaptation" and "facilitation" categories; "adaptation" could be further subcategorized into "slow adaptation" and "fast adaptation" profiles, with the latter being encountered more frequently. Moreover, two new categories of non-monotonic profiles were identified: an "adaptation with recovery" profile and a "facilitation followed by adaptation" profile. Examination of single neurons with trains of different tone frequencies revealed that some A1 neurons exhibited profiles of the same category to tone trains of different tone frequencies, whereas others exhibited profiles of different categories, depending on the tone frequency. These results demonstrate the variety in the response temporal profiles of mouse A1 neurons, which may benefit the encoding of individual tones in a train.
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Affiliation(s)
- Bo Zhou
- Department of Sensory and Cognitive Physiology, Graduate School of Medical Sciences, Kumamoto University 860-8556, Japan
| | - Ryohei Tomioka
- Department of Sensory and Cognitive Physiology, Graduate School of Medical Sciences, Kumamoto University 860-8556, Japan.
| | - Wen-Jie Song
- Department of Sensory and Cognitive Physiology, Graduate School of Medical Sciences, Kumamoto University 860-8556, Japan; Center for Metabolic Regulation of Healthy Aging, Faculty of Life Sciences, Kumamoto University, Kumamoto 860-8556, Japan.
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11
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Park Y, Geffen MN. Correction: A circuit model of auditory cortex. PLoS Comput Biol 2022; 18:e1010700. [PMID: 36378628 PMCID: PMC9665356 DOI: 10.1371/journal.pcbi.1010700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pcbi.1008016.].
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12
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Concina G, Renna A, Milano L, Manassero E, Stabile F, Sacchetti B. Expression of IGF-2 Receptor in the Auditory Cortex Improves the Precision of Recent Fear Memories and Maintains Detailed Remote Fear Memories Over Time. Cereb Cortex 2021; 31:5381-5395. [PMID: 34145441 DOI: 10.1093/cercor/bhab165] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/20/2021] [Accepted: 05/20/2021] [Indexed: 12/11/2022] Open
Abstract
Traumatic memories may become less precise over time and lead to the development of fear responses to novel stimuli, a process referred to as time-dependent fear generalization. The conditions that cause the growth of fear generalization over time are poorly understood. Here, we found that, in male rats, the level of discrimination at the early time point contributes to determining whether fear generalization will develop with the passage of time or not, suggesting a link between the precision of recent memory and the stability of remote engrams. We also found that the expression of insulin-like growth factor 2 receptor in layer 2/3 of the auditory cortex is linked to the precision of recent memories and to the stability of remote engrams and the development of fear generalization over time. These findings provide new insights on the neural mechanisms that underlie the time-dependent development of fear generalization that may occur over time after a traumatic event.
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Affiliation(s)
- Giulia Concina
- Rita Levi-Montalcini Department of Neuroscience, University of Turin, I-10125 Turin, Italy
| | - Annamaria Renna
- Rita Levi-Montalcini Department of Neuroscience, University of Turin, I-10125 Turin, Italy
| | - Luisella Milano
- Rita Levi-Montalcini Department of Neuroscience, University of Turin, I-10125 Turin, Italy
| | - Eugenio Manassero
- Rita Levi-Montalcini Department of Neuroscience, University of Turin, I-10125 Turin, Italy
| | - Francesca Stabile
- Rita Levi-Montalcini Department of Neuroscience, University of Turin, I-10125 Turin, Italy
| | - Benedetto Sacchetti
- Rita Levi-Montalcini Department of Neuroscience, University of Turin, I-10125 Turin, Italy
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13
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Schulz A, Miehl C, Berry MJ, Gjorgjieva J. The generation of cortical novelty responses through inhibitory plasticity. eLife 2021; 10:e65309. [PMID: 34647889 PMCID: PMC8516419 DOI: 10.7554/elife.65309] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 09/22/2021] [Indexed: 12/17/2022] Open
Abstract
Animals depend on fast and reliable detection of novel stimuli in their environment. Neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular, and synaptic mechanisms underlie those responses. Here, we show that spike-timing-dependent plasticity of inhibitory-to-excitatory synapses generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. The generation of novelty responses does not depend on the periodicity but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make experimentally testable predictions.
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Affiliation(s)
- Auguste Schulz
- Max Planck Institute for Brain ResearchFrankfurtGermany
- Technical University of Munich, Department of Electrical and Computer EngineeringMunichGermany
| | - Christoph Miehl
- Max Planck Institute for Brain ResearchFrankfurtGermany
- Technical University of Munich, School of Life SciencesFreisingGermany
| | - Michael J Berry
- Princeton University, Princeton Neuroscience InstitutePrincetonUnited States
| | - Julijana Gjorgjieva
- Max Planck Institute for Brain ResearchFrankfurtGermany
- Technical University of Munich, School of Life SciencesFreisingGermany
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14
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Todd J, Yeark MD, Paton B, Jermyn A, Winkler I. Shorter Contextual Timescale Rather Than Memory Deficit in Aging. Cereb Cortex 2021; 32:2412-2423. [PMID: 34564713 DOI: 10.1093/cercor/bhab344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 08/23/2021] [Accepted: 08/25/2021] [Indexed: 11/14/2022] Open
Abstract
Many aspects of cognitive ability and brain function that change as we age look like deficits on account of measurable differences in comparison to younger adult groups. One such difference occurs in auditory sensory responses that index perceptual learning. Meta-analytic findings show reliable age-related differences in auditory responses to repetitive patterns of sound and to rare violations of those patterns, variously attributed to deficits in auditory sensory memory and inhibition. Here, we determine whether proposed deficits would render older adults less prone to primacy effects, robustly observed in young adults, which present as a tendency for first learning to have a disproportionate influence over later perceptual inference. The results confirm this reduced sensitivity to primacy effects but do not support impairment in auditory sensory memory as the origin of this difference. Instead, the aging brain produces data consistent with shorter timescales of contextual reference. In conclusion, age-related differences observed previously for perceptual inference appear highly context-specific necessitating reconsideration of whether and to what function the notion of deficit should be attributed, and even whether the notion of deficit is appropriate at all.
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Affiliation(s)
- Juanita Todd
- School of Psychology, University of Newcastle, University Drive, Callaghan, NSW 2308, USA
| | - Mattsen D Yeark
- School of Psychology, University of Newcastle, University Drive, Callaghan, NSW 2308, USA
| | - Bryan Paton
- School of Psychology, University of Newcastle, University Drive, Callaghan, NSW 2308, USA
| | - Alexandra Jermyn
- School of Psychology, University of Newcastle, University Drive, Callaghan, NSW 2308, USA
| | - István Winkler
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest H-1117, Hungary
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15
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Vanattou-Saïfoudine N, Han C, Krause R, Vasilaki E, von der Behrens W, Indiveri G. A robust model of Stimulus-Specific Adaptation validated on neuromorphic hardware. Sci Rep 2021; 11:17904. [PMID: 34504155 PMCID: PMC8429557 DOI: 10.1038/s41598-021-97217-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 08/10/2021] [Indexed: 02/08/2023] Open
Abstract
Stimulus-Specific Adaptation (SSA) to repetitive stimulation is a phenomenon that has been observed across many different species and in several brain sensory areas. It has been proposed as a computational mechanism, responsible for separating behaviorally relevant information from the continuous stream of sensory information. Although SSA can be induced and measured reliably in a wide variety of conditions, the network details and intracellular mechanisms giving rise to SSA still remain unclear. Recent computational studies proposed that SSA could be associated with a fast and synchronous neuronal firing phenomenon called Population Spikes (PS). Here, we test this hypothesis using a mean-field rate model and corroborate it using a neuromorphic hardware. As the neuromorphic circuits used in this study operate in real-time with biologically realistic time constants, they can reproduce the same dynamics observed in biological systems, together with the exploration of different connectivity schemes, with complete control of the system parameter settings. Besides, the hardware permits the iteration of multiple experiments over many trials, for extended amounts of time and without losing the networks and individual neural processes being studied. Following this "neuromorphic engineering" approach, we therefore study the PS hypothesis in a biophysically inspired recurrent networks of spiking neurons and evaluate the role of different linear and non-linear dynamic computational primitives such as spike-frequency adaptation or short-term depression (STD). We compare both the theoretical mean-field model of SSA and PS to previously obtained experimental results in the area of novelty detection and observe its behavior on its neuromorphic physical equivalent model. We show how the approach proposed can be extended to other computational neuroscience modelling efforts for understanding high-level phenomena in mechanistic models.
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Affiliation(s)
- Natacha Vanattou-Saïfoudine
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
- Department of Computer Science, University of Sheffield, Sheffield, UK.
| | - Chao Han
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Renate Krause
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Eleni Vasilaki
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
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16
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Ferrario A, Rankin J. Auditory streaming emerges from fast excitation and slow delayed inhibition. JOURNAL OF MATHEMATICAL NEUROSCIENCE 2021; 11:8. [PMID: 33939042 PMCID: PMC8093365 DOI: 10.1186/s13408-021-00106-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 04/22/2021] [Indexed: 05/29/2023]
Abstract
In the auditory streaming paradigm, alternating sequences of pure tones can be perceived as a single galloping rhythm (integration) or as two sequences with separated low and high tones (segregation). Although studied for decades, the neural mechanisms underlining this perceptual grouping of sound remains a mystery. With the aim of identifying a plausible minimal neural circuit that captures this phenomenon, we propose a firing rate model with two periodically forced neural populations coupled by fast direct excitation and slow delayed inhibition. By analyzing the model in a non-smooth, slow-fast regime we analytically prove the existence of a rich repertoire of dynamical states and of their parameter dependent transitions. We impose plausible parameter restrictions and link all states with perceptual interpretations. Regions of stimulus parameters occupied by states linked with each percept match those found in behavioural experiments. Our model suggests that slow inhibition masks the perception of subsequent tones during segregation (forward masking), whereas fast excitation enables integration for large pitch differences between the two tones.
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Affiliation(s)
- Andrea Ferrario
- Department of Mathematics, College of Engineering, Mathematics & Physical Sciences, University of Exeter, Exeter, UK.
| | - James Rankin
- Department of Mathematics, College of Engineering, Mathematics & Physical Sciences, University of Exeter, Exeter, UK
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17
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Asokan MM, Williamson RS, Hancock KE, Polley DB. Inverted central auditory hierarchies for encoding local intervals and global temporal patterns. Curr Biol 2021; 31:1762-1770.e4. [PMID: 33609455 DOI: 10.1016/j.cub.2021.01.076] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 12/01/2020] [Accepted: 01/21/2021] [Indexed: 01/02/2023]
Abstract
In sensory systems, representational features of increasing complexity emerge at successive stages of processing. In the mammalian auditory pathway, the clearest change from brainstem to cortex is defined by what is lost, not by what is gained, in that high-fidelity temporal coding becomes increasingly restricted to slower acoustic modulation rates.1,2 Here, we explore the idea that sluggish temporal processing is more than just an inability for fast processing, but instead reflects an emergent specialization for encoding sound features that unfold on very slow timescales.3,4 We performed simultaneous single unit ensemble recordings from three hierarchical stages of auditory processing in awake mice - the inferior colliculus (IC), medial geniculate body of the thalamus (MGB) and primary auditory cortex (A1). As expected, temporal coding of brief local intervals (0.001 - 0.1 s) separating consecutive noise bursts was robust in the IC and declined across MGB and A1. By contrast, slowly developing (∼1 s period) global rhythmic patterns of inter-burst interval sequences strongly modulated A1 spiking, were weakly captured by MGB neurons, and not at all by IC neurons. Shifts in stimulus regularity were not represented by changes in A1 spike rates, but rather in how the spikes were arranged in time. These findings show that low-level auditory neurons with fast timescales encode isolated sound features but not the longer gestalt, while the extended timescales in higher-level areas can facilitate sensitivity to slower contextual changes in the sensory environment.
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Affiliation(s)
- Meenakshi M Asokan
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear Infirmary, Boston MA 02114 USA; Division of Medical Sciences, Harvard Medical School, Boston MA 02114 USA
| | - Ross S Williamson
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear Infirmary, Boston MA 02114 USA; Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston MA 02114 USA
| | - Kenneth E Hancock
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear Infirmary, Boston MA 02114 USA; Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston MA 02114 USA
| | - Daniel B Polley
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear Infirmary, Boston MA 02114 USA; Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston MA 02114 USA.
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18
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Differential Short-Term Plasticity of PV and SST Neurons Accounts for Adaptation and Facilitation of Cortical Neurons to Auditory Tones. J Neurosci 2020; 40:9224-9235. [PMID: 33097639 DOI: 10.1523/jneurosci.0686-20.2020] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 09/16/2020] [Accepted: 10/14/2020] [Indexed: 11/21/2022] Open
Abstract
Cortical responses to sensory stimuli are strongly modulated by temporal context. One of the best studied examples of such modulation is sensory adaptation. We first show that in response to repeated tones pyramidal (Pyr) neurons in male mouse auditory cortex (A1) exhibit facilitating and stable responses, in addition to adapting responses. To examine the potential mechanisms underlying these distinct temporal profiles, we developed a reduced spiking model of sensory cortical circuits that incorporated the signature short-term synaptic plasticity (STP) profiles of the inhibitory parvalbumin (PV) and somatostatin (SST) interneurons. The model accounted for all three temporal response profiles as the result of dynamic changes in excitatory/inhibitory balance produced by STP, primarily through shifts in the relative latency of Pyr and inhibitory neurons. Transition between the three response profiles was possible by changing the strength of the inhibitory PV→Pyr and SST→Pyr synapses. The model predicted that a unit's latency would be related to its temporal profile. Consistent with this prediction, the latency of stable units was significantly shorter than that of adapting and facilitating units. Furthermore, because of the history-dependence of STP the model generated a paradoxical prediction: that inactivation of inhibitory neurons during one tone would decrease the response of A1 neurons to a subsequent tone. Indeed, we observed that optogenetic inactivation of PV neurons during one tone counterintuitively decreased the spiking of Pyr neurons to a subsequent tone 400 ms later. These results provide evidence that STP is critical to temporal context-dependent responses in the sensory cortex.SIGNIFICANCE STATEMENT Our perception of speech and music depends strongly on temporal context, i.e., the significance of a stimulus depends on the preceding stimuli. Complementary neural mechanisms are needed to sometimes ignore repetitive stimuli (e.g., the tic of a clock) or detect meaningful repetition (e.g., consecutive tones in Morse code). We modeled a neural circuit that accounts for diverse experimentally-observed response profiles in auditory cortex (A1) neurons, based on known forms of short-term synaptic plasticity (STP). Whether the simulated circuit reduced, maintained, or enhanced its response to repeated tones depended on the relative dominance of two different types of inhibitory cells. The model made novel predictions that were experimentally validated. Results define an important role for STP in temporal context-dependent perception.
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