1
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Deister CA, Moore AI, Voigts J, Bechek S, Lichtin R, Brown TC, Moore CI. Neocortical inhibitory imbalance predicts successful sensory detection. Cell Rep 2024; 43:114233. [PMID: 38905102 DOI: 10.1016/j.celrep.2024.114233] [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: 07/16/2021] [Revised: 07/17/2023] [Accepted: 04/26/2024] [Indexed: 06/23/2024] Open
Abstract
Perceptual success depends on fast-spiking, parvalbumin-positive interneurons (FS/PVs). However, competing theories of optimal rate and correlation in pyramidal (PYR) firing make opposing predictions regarding the underlying FS/PV dynamics. We addressed this with population calcium imaging of FS/PVs and putative PYR neurons during threshold detection. In primary somatosensory and visual neocortex, a distinct PYR subset shows increased rate and spike-count correlations on detected trials ("hits"), while most show no rate change and decreased correlations. A larger fraction of FS/PVs predicts hits with either rate increases or decreases. Using computational modeling, we found that inhibitory imbalance, created by excitatory "feedback" and interactions between FS/PV pools, can account for the data. Rate-decreasing FS/PVs increase rate and correlation in a PYR subset, while rate-increasing FS/PVs reduce correlations and offset enhanced excitation in PYR neurons. These findings indicate that selection of informative PYR ensembles, through transient inhibitory imbalance, is a common motif of optimal neocortical processing.
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Affiliation(s)
- Christopher A Deister
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Alexander I Moore
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Jakob Voigts
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA; Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Sophia Bechek
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Rebecca Lichtin
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA
| | - Tyler C Brown
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christopher I Moore
- Department of Neuroscience and Carney Institute for Brain Sciences, Brown University, Providence, RI, USA.
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2
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de Brito Van Velze M, Dhanasobhon D, Martinez M, Morabito A, Berthaux E, Pinho CM, Zerlaut Y, Rebola N. Feedforward and disinhibitory circuits differentially control activity of cortical somatostatin interneurons during behavioral state transitions. Cell Rep 2024; 43:114197. [PMID: 38733587 DOI: 10.1016/j.celrep.2024.114197] [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: 10/05/2023] [Revised: 03/26/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024] Open
Abstract
Interneurons (INs), specifically those in disinhibitory circuits like somatostatin (SST) and vasoactive intestinal peptide (VIP)-INs, are strongly modulated by the behavioral context. Yet, the mechanisms by which these INs are recruited during active states and whether their activity is consistent across sensory cortices remain unclear. We now report that in mice, locomotor activity strongly recruits SST-INs in the primary somatosensory (S1) but not the visual (V1) cortex. This diverse engagement of SST-INs cannot be explained by differences in VIP-IN function but is absent in the presence of visual input, suggesting the involvement of feedforward sensory pathways. Accordingly, inactivating the somatosensory thalamus, but not decreasing VIP-IN activity, significantly reduces the modulation of SST-INs by locomotion. Model simulations suggest that the differences in SST-INs across behavioral states can be explained by varying ratios of VIP- and thalamus-driven activity. By integrating feedforward activity with neuromodulation, SST-INs are anticipated to be crucial for adapting sensory processing to behavioral states.
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Affiliation(s)
- Marcel de Brito Van Velze
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, INSERM, CNRS, 75013 Paris, France
| | - Dhanasak Dhanasobhon
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, INSERM, CNRS, 75013 Paris, France
| | - Marie Martinez
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, INSERM, CNRS, 75013 Paris, France
| | - Annunziato Morabito
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, INSERM, CNRS, 75013 Paris, France
| | - Emmanuelle Berthaux
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, INSERM, CNRS, 75013 Paris, France
| | - Cibele Martins Pinho
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, INSERM, CNRS, 75013 Paris, France
| | - Yann Zerlaut
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, INSERM, CNRS, 75013 Paris, France.
| | - Nelson Rebola
- ICM, Paris Brain Institute, Hôpital de la Pitié-Salpêtrière, Sorbonne Université, INSERM, CNRS, 75013 Paris, France.
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3
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Waitzmann F, Wu YK, Gjorgjieva J. Top-down modulation in canonical cortical circuits with short-term plasticity. Proc Natl Acad Sci U S A 2024; 121:e2311040121. [PMID: 38593083 PMCID: PMC11032497 DOI: 10.1073/pnas.2311040121] [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: 06/30/2023] [Accepted: 02/14/2024] [Indexed: 04/11/2024] Open
Abstract
Cortical dynamics and computations are strongly influenced by diverse GABAergic interneurons, including those expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP). Together with excitatory (E) neurons, they form a canonical microcircuit and exhibit counterintuitive nonlinear phenomena. One instance of such phenomena is response reversal, whereby SST neurons show opposite responses to top-down modulation via VIP depending on the presence of bottom-up sensory input, indicating that the network may function in different regimes under different stimulation conditions. Combining analytical and computational approaches, we demonstrate that model networks with multiple interneuron subtypes and experimentally identified short-term plasticity mechanisms can implement response reversal. Surprisingly, despite not directly affecting SST and VIP activity, PV-to-E short-term depression has a decisive impact on SST response reversal. We show how response reversal relates to inhibition stabilization and the paradoxical effect in the presence of several short-term plasticity mechanisms demonstrating that response reversal coincides with a change in the indispensability of SST for network stabilization. In summary, our work suggests a role of short-term plasticity mechanisms in generating nonlinear phenomena in networks with multiple interneuron subtypes and makes several experimentally testable predictions.
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Affiliation(s)
- Felix Waitzmann
- School of Life Sciences, Technical University of Munich, 85354Freising, Germany
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438Frankfurt, Germany
| | - Yue Kris Wu
- School of Life Sciences, Technical University of Munich, 85354Freising, Germany
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438Frankfurt, Germany
| | - Julijana Gjorgjieva
- School of Life Sciences, Technical University of Munich, 85354Freising, Germany
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, 60438Frankfurt, Germany
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4
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Beerendonk L, Mejías JF, Nuiten SA, de Gee JW, Fahrenfort JJ, van Gaal S. A disinhibitory circuit mechanism explains a general principle of peak performance during mid-level arousal. Proc Natl Acad Sci U S A 2024; 121:e2312898121. [PMID: 38277436 PMCID: PMC10835062 DOI: 10.1073/pnas.2312898121] [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: 07/30/2023] [Accepted: 12/04/2023] [Indexed: 01/28/2024] Open
Abstract
Perceptual decision-making is highly dependent on the momentary arousal state of the brain, which fluctuates over time on a scale of hours, minutes, and even seconds. The textbook relationship between momentary arousal and task performance is captured by an inverted U-shape, as put forward in the Yerkes-Dodson law. This law suggests optimal performance at moderate levels of arousal and impaired performance at low or high arousal levels. However, despite its popularity, the evidence for this relationship in humans is mixed at best. Here, we use pupil-indexed arousal and performance data from various perceptual decision-making tasks to provide converging evidence for the inverted U-shaped relationship between spontaneous arousal fluctuations and performance across different decision types (discrimination, detection) and sensory modalities (visual, auditory). To further understand this relationship, we built a neurobiologically plausible mechanistic model and show that it is possible to reproduce our findings by incorporating two types of interneurons that are both modulated by an arousal signal. The model architecture produces two dynamical regimes under the influence of arousal: one regime in which performance increases with arousal and another regime in which performance decreases with arousal, together forming an inverted U-shaped arousal-performance relationship. We conclude that the inverted U-shaped arousal-performance relationship is a general and robust property of sensory processing. It might be brought about by the influence of arousal on two types of interneurons that together act as a disinhibitory pathway for the neural populations that encode the available sensory evidence used for the decision.
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Affiliation(s)
- Lola Beerendonk
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam1001NK, The Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam1001NK, The Netherlands
| | - Jorge F. Mejías
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam1001NK, The Netherlands
- Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam1098XH, The Netherlands
| | - Stijn A. Nuiten
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam1001NK, The Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam1001NK, The Netherlands
- Universitäre Psychiatrische Kliniken Basel, Wilhelm Klein-Strasse 27, Basel4002, Switzerland
| | - Jan Willem de Gee
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam1001NK, The Netherlands
- Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam1098XH, The Netherlands
| | - Johannes J. Fahrenfort
- Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam1081HV, The Netherlands
- Department of Applied and Experimental Psychology, Vrije Universiteit Amsterdam, Amsterdam1081HV, The Netherlands
| | - Simon van Gaal
- Research Priority Area Brain and Cognition, University of Amsterdam, Amsterdam1001NK, The Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam1001NK, The Netherlands
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5
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Xie Y, Sadeh S. Computational assessment of visual coding across mouse brain areas and behavioural states. Front Comput Neurosci 2023; 17:1269019. [PMID: 37899886 PMCID: PMC10613063 DOI: 10.3389/fncom.2023.1269019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
Introduction Our brain is bombarded by a diverse range of visual stimuli, which are converted into corresponding neuronal responses and processed throughout the visual system. The neural activity patterns that result from these external stimuli vary depending on the object or scene being observed, but they also change as a result of internal or behavioural states. This raises the question of to what extent it is possible to predict the presented visual stimuli from neural activity across behavioural states, and how this varies in different brain regions. Methods To address this question, we assessed the computational capacity of decoders to extract visual information in awake behaving mice, by analysing publicly available standardised datasets from the Allen Brain Institute. We evaluated how natural movie frames can be distinguished based on the activity of units recorded in distinct brain regions and under different behavioural states. This analysis revealed the spectrum of visual information present in different brain regions in response to binary and multiclass classification tasks. Results Visual cortical areas showed highest classification accuracies, followed by thalamic and midbrain regions, with hippocampal regions showing close to chance accuracy. In addition, we found that behavioural variability led to a decrease in decoding accuracy, whereby large behavioural changes between train and test sessions reduced the classification performance of the decoders. A generalised linear model analysis suggested that this deterioration in classification might be due to an independent modulation of neural activity by stimulus and behaviour. Finally, we reconstructed the natural movie frames from optimal linear classifiers, and observed a strong similarity between reconstructed and actual movie frames. However, the similarity was significantly higher when the decoders were trained and tested on sessions with similar behavioural states. Conclusion Our analysis provides a systematic assessment of visual coding in the mouse brain, and sheds light on the spectrum of visual information present across brain areas and behavioural states.
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Affiliation(s)
| | - Sadra Sadeh
- Department of Brain Sciences, Imperial College London, London, United Kingdom
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6
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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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7
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Aussel A, Fiebelkorn IC, Kastner S, Kopell NJ, Pittman-Polletta BR. Interacting rhythms enhance sensitivity of target detection in a fronto-parietal computational model of visual attention. eLife 2023; 12:e67684. [PMID: 36718998 PMCID: PMC10129332 DOI: 10.7554/elife.67684] [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: 02/19/2021] [Accepted: 01/12/2023] [Indexed: 02/01/2023] Open
Abstract
Even during sustained attention, enhanced processing of attended stimuli waxes and wanes rhythmically, with periods of enhanced and relatively diminished visual processing (and subsequent target detection) alternating at 4 or 8 Hz in a sustained visual attention task. These alternating attentional states occur alongside alternating dynamical states, in which lateral intraparietal cortex (LIP), the frontal eye field (FEF), and the mediodorsal pulvinar (mdPul) exhibit different activity and functional connectivity at α, β, and γ frequencies-rhythms associated with visual processing, working memory, and motor suppression. To assess whether and how these multiple interacting rhythms contribute to periodicity in attention, we propose a detailed computational model of FEF and LIP. When driven by θ-rhythmic inputs simulating experimentally-observed mdPul activity, this model reproduced the rhythmic dynamics and behavioral consequences of observed attentional states, revealing that the frequencies and mechanisms of the observed rhythms allow for peak sensitivity in visual target detection while maintaining functional flexibility.
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Affiliation(s)
- Amélie Aussel
- Cognitive Rhythms Collaborative, Boston UniversityBostonUnited States
- Department of Mathematics and Statistics, Boston UniversityRochesterUnited States
| | - Ian C Fiebelkorn
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester Medical Center, University of RochesterRochesterUnited States
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Sabine Kastner
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Department of Psychology, Princeton UniversityPrincetonUnited States
| | - Nancy J Kopell
- Cognitive Rhythms Collaborative, Boston UniversityBostonUnited States
- Department of Mathematics and Statistics, Boston UniversityRochesterUnited States
| | - Benjamin Rafael Pittman-Polletta
- Cognitive Rhythms Collaborative, Boston UniversityBostonUnited States
- Department of Mathematics and Statistics, Boston UniversityRochesterUnited States
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8
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Thivierge JP, Giraud E, Lynn M, Théberge Charbonneau A. Key role of neuronal diversity in structured reservoir computing. CHAOS (WOODBURY, N.Y.) 2022; 32:113130. [PMID: 36456321 DOI: 10.1063/5.0111131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 10/24/2022] [Indexed: 06/17/2023]
Abstract
Chaotic time series have been captured by reservoir computing models composed of a recurrent neural network whose output weights are trained in a supervised manner. These models, however, are typically limited to randomly connected networks of homogeneous units. Here, we propose a new class of structured reservoir models that incorporates a diversity of cell types and their known connections. In a first version of the model, the reservoir was composed of mean-rate units separated into pyramidal, parvalbumin, and somatostatin cells. Stability analysis of this model revealed two distinct dynamical regimes, namely, (i) an inhibition-stabilized network (ISN) where strong recurrent excitation is balanced by strong inhibition and (ii) a non-ISN network with weak excitation. These results were extended to a leaky integrate-and-fire model that captured different cell types along with their network architecture. ISN and non-ISN reservoir networks were trained to relay and generate a chaotic Lorenz attractor. Despite their increased performance, ISN networks operate in a regime of activity near the limits of stability where external perturbations yield a rapid divergence in output. The proposed framework of structured reservoir computing opens avenues for exploring how neural microcircuits can balance performance and stability when representing time series through distinct dynamical regimes.
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Affiliation(s)
- Jean-Philippe Thivierge
- University of Ottawa Brain and Mind Research Institute, 451 Smyth Rd., Ottawa, Ontario K1H 8M5, Canada
| | - Eloïse Giraud
- School of Psychology, University of Ottawa, 156 Jean-Jacques Lussier, Ottawa, Ontario K1N 6N5, Canada
| | - Michael Lynn
- University of Ottawa Brain and Mind Research Institute, 451 Smyth Rd., Ottawa, Ontario K1H 8M5, Canada
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9
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Sadeh S, Clopath C. Contribution of behavioural variability to representational drift. eLife 2022; 11:77907. [PMID: 36040010 PMCID: PMC9481246 DOI: 10.7554/elife.77907] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Neuronal responses to similar stimuli change dynamically over time, raising the question of how internal representations can provide a stable substrate for neural coding. Recent work has suggested a large degree of drift in neural representations even in sensory cortices, which are believed to store stable representations of the external world. While the drift of these representations is mostly characterized in relation to external stimuli, the behavioural state of the animal (for instance, the level of arousal) is also known to strongly modulate the neural activity. We therefore asked how the variability of such modulatory mechanisms can contribute to representational changes. We analysed large-scale recording of neural activity from the Allen Brain Observatory, which was used before to document representational drift in the mouse visual cortex. We found that, within these datasets, behavioural variability significantly contributes to representational changes. This effect was broadcasted across various cortical areas in the mouse, including the primary visual cortex, higher order visual areas, and even regions not primarily linked to vision like hippocampus. Our computational modelling suggests that these results are consistent with independent modulation of neural activity by behaviour over slower timescales. Importantly, our analysis suggests that reliable but variable modulation of neural representations by behaviour can be misinterpreted as representational drift if neuronal representations are only characterized in the stimulus space and marginalized over behavioural parameters.
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Affiliation(s)
- Sadra Sadeh
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Claudia Clopath
- Department of Bioengineering, Imperial College London, London, United Kingdom
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10
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Hahn G, Kumar A, Schmidt H, Knösche TR, Deco G. Rate and oscillatory switching dynamics of a multilayer visual microcircuit model. eLife 2022; 11:77594. [PMID: 35994330 PMCID: PMC9395191 DOI: 10.7554/elife.77594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
The neocortex is organized around layered microcircuits consisting of a variety of excitatory and inhibitory neuronal types which perform rate- and oscillation-based computations. Using modeling, we show that both superficial and deep layers of the primary mouse visual cortex implement two ultrasensitive and bistable switches built on mutual inhibitory connectivity motives between somatostatin, parvalbumin, and vasoactive intestinal polypeptide cells. The switches toggle pyramidal neurons between high and low firing rate states that are synchronized across layers through translaminar connectivity. Moreover, inhibited and disinhibited states are characterized by low- and high-frequency oscillations, respectively, with layer-specific differences in frequency and power which show asymmetric changes during state transitions. These findings are consistent with a number of experimental observations and embed firing rate together with oscillatory changes within a switch interpretation of the microcircuit.
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Affiliation(s)
- Gerald Hahn
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Arvind Kumar
- Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Helmut Schmidt
- Brain Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Thomas R Knösche
- Brain Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Institute of Biomedical Engineering and Informatics, Department of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats, Barcelona, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
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11
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A circuit mechanism for independent modulation of excitatory and inhibitory firing rates after sensory deprivation. Proc Natl Acad Sci U S A 2022; 119:e2116895119. [PMID: 35925891 PMCID: PMC9371725 DOI: 10.1073/pnas.2116895119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The cortex is particularly vulnerable to perturbations during sensitive periods, such as the critical period when manipulating sensory experience can induce long-lasting changes in brain structure. Depriving rodents of vision in one eye (known as monocular deprivation [MD]) reduces network activity over two days, whereby inhibitory neurons decrease their firing rates one day after MD, while excitatory neurons are delayed by an additional day. We use spiking networks to mechanistically dissect the requirements for this independent firing-rate regulation after sensory deprivation. We find that in networks stabilized by recurrent inhibition, at least two interneuron subtypes (parvalbumin-expressing and somatostatin-expressing interneurons) are necessary to dynamically alter the circuit response after deprivation and generalize the result across sensory cortices. Diverse interneuron subtypes shape sensory processing in mature cortical circuits. During development, sensory deprivation evokes powerful synaptic plasticity that alters circuitry, but how different inhibitory subtypes modulate circuit dynamics in response to this plasticity remains unclear. We investigate how deprivation-induced synaptic changes affect excitatory and inhibitory firing rates in a microcircuit model of the sensory cortex with multiple interneuron subtypes. We find that with a single interneuron subtype (parvalbumin-expressing [PV]), excitatory and inhibitory firing rates can only be comodulated—increased or decreased together. To explain the experimentally observed independent modulation, whereby one firing rate increases and the other decreases, requires strong feedback from a second interneuron subtype (somatostatin-expressing [SST]). Our model applies to the visual and somatosensory cortex, suggesting a general mechanism across sensory cortices. Therefore, we provide a mechanistic explanation for the differential role of interneuron subtypes in regulating firing rates, contributing to the already diverse roles they serve in the cortex.
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12
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Opposite forms of adaptation in mouse visual cortex are controlled by distinct inhibitory microcircuits. Nat Commun 2022; 13:1031. [PMID: 35210417 PMCID: PMC8873261 DOI: 10.1038/s41467-022-28635-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 01/28/2022] [Indexed: 01/29/2023] Open
Abstract
Sensory processing in the cortex adapts to the history of stimulation but the mechanisms are not understood. Imaging the primary visual cortex of mice we find here that an increase in stimulus contrast is not followed by a simple decrease in gain of pyramidal cells; as many cells increase gain to improve detection of a subsequent decrease in contrast. Depressing and sensitizing forms of adaptation also occur in different types of interneurons (PV, SST and VIP) and the net effect within individual pyramidal cells reflects the balance of PV inputs, driving depression, and a subset of SST interneurons driving sensitization. Changes in internal state associated with locomotion increase gain across the population of pyramidal cells while maintaining the balance between these opposite forms of plasticity, consistent with activation of both VIP->SST and SST->PV disinhibitory pathways. These results reveal how different inhibitory microcircuits adjust the gain of pyramidal cells signalling changes in stimulus strength. The authors describe the role of inhibitory microcircuits in the visual cortex of mice in adaptation to contrast. They show how external stimuli and internal state interact to adjust processing in the visual cortex.
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13
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Ahmadian Y, Miller KD. What is the dynamical regime of cerebral cortex? Neuron 2021; 109:3373-3391. [PMID: 34464597 PMCID: PMC9129095 DOI: 10.1016/j.neuron.2021.07.031] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 07/05/2021] [Accepted: 07/30/2021] [Indexed: 01/13/2023]
Abstract
Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition. We review a wide range of evidence pointing to this cancellation occurring in a regime in which the balance is loose, meaning that the net input remaining after cancellation of excitation and inhibition is comparable in size with the factors that cancel, rather than tight, meaning that the net input is very small relative to the canceling factors. This choice of regime has important implications for cortical functional responses, as we describe: loose balance, but not tight balance, can yield many nonlinear population behaviors seen in sensory cortical neurons, allow the presence of correlated variability, and yield decrease of that variability with increasing external stimulus drive as observed across multiple cortical areas.
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Affiliation(s)
- Yashar Ahmadian
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
| | - Kenneth D Miller
- Center for Theoretical Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, and Department of Neuroscience, College of Physicians and Surgeons and Morton B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
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14
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Huang C. Modulation of the dynamical state in cortical network models. Curr Opin Neurobiol 2021; 70:43-50. [PMID: 34403890 PMCID: PMC8688204 DOI: 10.1016/j.conb.2021.07.004] [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: 02/01/2021] [Revised: 05/18/2021] [Accepted: 07/14/2021] [Indexed: 11/29/2022]
Abstract
Cortical neural responses can be modulated by various factors, such as stimulus inputs and the behavior state of the animal. Understanding the circuit mechanisms underlying modulations of network dynamics is important to understand the flexibility of circuit computations. Identifying the dynamical state of a network is an important first step to predict network responses to external stimulus and top-down modulatory inputs. Models in stable or unstable dynamical regimes require different analytic tools to estimate the network responses to inputs and the structure of neural variability. In this article, I review recent cortical models of state-dependent responses and their predictions about the underlying modulatory mechanisms.
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Affiliation(s)
- Chengcheng Huang
- Departments of Neuroscience and Mathematics, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
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15
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Kirchberger L, Mukherjee S, Schnabel UH, van Beest EH, Barsegyan A, Levelt CN, Heimel JA, Lorteije JAM, van der Togt C, Self MW, Roelfsema PR. The essential role of recurrent processing for figure-ground perception in mice. SCIENCE ADVANCES 2021; 7:eabe1833. [PMID: 34193411 PMCID: PMC8245045 DOI: 10.1126/sciadv.abe1833] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 05/17/2021] [Indexed: 05/15/2023]
Abstract
The segregation of figures from the background is an important step in visual perception. In primary visual cortex, figures evoke stronger activity than backgrounds during a delayed phase of the neuronal responses, but it is unknown how this figure-ground modulation (FGM) arises and whether it is necessary for perception. Here, we show, using optogenetic silencing in mice, that the delayed V1 response phase is necessary for figure-ground segregation. Neurons in higher visual areas also exhibit FGM and optogenetic silencing of higher areas reduced FGM in V1. In V1, figures elicited higher activity of vasoactive intestinal peptide-expressing (VIP) interneurons than the background, whereas figures suppressed somatostatin-positive interneurons, resulting in an increased activation of pyramidal cells. Optogenetic silencing of VIP neurons reduced FGM in V1, indicating that disinhibitory circuits contribute to FGM. Our results provide insight into how lower and higher areas of the visual cortex interact to shape visual perception.
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Affiliation(s)
- Lisa Kirchberger
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands
| | - Sreedeep Mukherjee
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands
| | - Ulf H Schnabel
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands
| | - Enny H van Beest
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands
| | - Areg Barsegyan
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands
| | - Christiaan N Levelt
- Molecular Visual Plasticity Group, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands
- Department of Molecular and Cellular Neuroscience, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands
| | - J Alexander Heimel
- Cortical Structure and Function Group, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands
| | - Jeannette A M Lorteije
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, 1098XH Amsterdam, Netherlands
| | - Chris van der Togt
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands
| | - Matthew W Self
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands
| | - Pieter R Roelfsema
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, Netherlands.
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, Netherlands
- Department of Psychiatry, Academic Medical Center, Amsterdam, Netherlands
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16
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Kullander K, Topolnik L. Cortical disinhibitory circuits: cell types, connectivity and function. Trends Neurosci 2021; 44:643-657. [PMID: 34006387 DOI: 10.1016/j.tins.2021.04.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 04/16/2021] [Accepted: 04/22/2021] [Indexed: 12/16/2022]
Abstract
The concept of a dynamic excitation/inhibition balance tuned by circuit disinhibition, which can shape information flow during complex behavioral tasks, has arisen as an important and conserved information-processing motif. In cortical circuits, different subtypes of GABAergic inhibitory interneurons are connected to each other, offering an anatomical foundation for disinhibitory processes. Moreover, a subpopulation of GABAergic cells that express vasoactive intestinal polypeptide (VIP) preferentially innervates inhibitory interneurons, highlighting their central role in disinhibitory modulation. We discuss inhibitory neuron subtypes involved in disinhibition, with a focus on local circuits and long-range synaptic connections that drive disinhibitory function. We highlight multiple layers of disinhibition across cortical circuits that regulate behavior and serve to maintain an excitation/inhibition balance.
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Affiliation(s)
- Klas Kullander
- Department of Neuroscience, Uppsala University, Uppsala, Sweden.
| | - Lisa Topolnik
- Department of Biochemistry, Microbiology, and Bioinformatics, Laval University, Québec, QC, Canada; Neuroscience Axis, Centre de Recherche du Centre Hospitalier Universitaire de Québec (CRCHUQ), Laval University, Québec, QC, Canada.
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17
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Flossmann T, Rochefort NL. Spatial navigation signals in rodent visual cortex. Curr Opin Neurobiol 2020; 67:163-173. [PMID: 33360769 DOI: 10.1016/j.conb.2020.11.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/04/2020] [Accepted: 11/06/2020] [Indexed: 12/14/2022]
Abstract
During navigation, animals integrate sensory information with body movements to guide actions. The impact of both navigational and movement-related signals on cortical visual information processing remains largely unknown. We review recent studies in awake rodents that have revealed navigation-related signals in the primary visual cortex (V1) including speed, distance travelled and head-orienting movements. Both cortical and subcortical inputs convey self-motion related information to V1 neurons: for example, top-down inputs from secondary motor and retrosplenial cortices convey information about head movements and spatial expectations. Within V1, subtypes of inhibitory neurons are critical for the integration of navigation-related and visual signals. We conclude with potential functional roles of navigation-related signals in V1 including gain control, motor error signals and predictive coding.
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Affiliation(s)
- Tom Flossmann
- Centre for Discovery Brain Sciences, School of Biomedical Sciences, University of Edinburgh, Edinburgh, EH8 9XD, United Kingdom
| | - Nathalie L Rochefort
- Centre for Discovery Brain Sciences, School of Biomedical Sciences, University of Edinburgh, Edinburgh, EH8 9XD, United Kingdom; Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, EH8 9XD, United Kingdom.
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18
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Zhang Y, Young LS. DNN-assisted statistical analysis of a model of local cortical circuits. Sci Rep 2020; 10:20139. [PMID: 33208805 PMCID: PMC7674455 DOI: 10.1038/s41598-020-76770-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/20/2020] [Indexed: 01/27/2023] Open
Abstract
In neuroscience, computational modeling is an effective way to gain insight into cortical mechanisms, yet the construction and analysis of large-scale network models—not to mention the extraction of underlying principles—are themselves challenging tasks, due to the absence of suitable analytical tools and the prohibitive costs of systematic numerical exploration of high-dimensional parameter spaces. In this paper, we propose a data-driven approach assisted by deep neural networks (DNN). The idea is to first discover certain input-output relations, and then to leverage this information and the superior computation speeds of the well-trained DNN to guide parameter searches and to deduce theoretical understanding. To illustrate this novel approach, we used as a test case a medium-size network of integrate-and-fire neurons intended to model local cortical circuits. With the help of an accurate yet extremely efficient DNN surrogate, we revealed the statistics of model responses, providing a detailed picture of model behavior. The information obtained is both general and of a fundamental nature, with direct application to neuroscience. Our results suggest that the methodology proposed can be scaled up to larger and more complex biological networks when used in conjunction with other techniques of biological modeling.
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Affiliation(s)
- Yaoyu Zhang
- School of Mathematical Sciences, Institute of Natural Sciences, MOE-LSC and Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Lai-Sang Young
- School of Mathematics and School of Natural Sciences, Institute for Advanced Study, Princeton, NJ, 08540, USA. .,Courant Institute of Mathematical Sciences, New York University, New York, NY, 10012, USA.
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19
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Sadeh S, Clopath C. Inhibitory stabilization and cortical computation. Nat Rev Neurosci 2020; 22:21-37. [PMID: 33177630 DOI: 10.1038/s41583-020-00390-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2020] [Indexed: 12/22/2022]
Abstract
Neuronal networks with strong recurrent connectivity provide the brain with a powerful means to perform complex computational tasks. However, high-gain excitatory networks are susceptible to instability, which can lead to runaway activity, as manifested in pathological regimes such as epilepsy. Inhibitory stabilization offers a dynamic, fast and flexible compensatory mechanism to balance otherwise unstable networks, thus enabling the brain to operate in its most efficient regimes. Here we review recent experimental evidence for the presence of such inhibition-stabilized dynamics in the brain and discuss their consequences for cortical computation. We show how the study of inhibition-stabilized networks in the brain has been facilitated by recent advances in the technological toolbox and perturbative techniques, as well as a concomitant development of biologically realistic computational models. By outlining future avenues, we suggest that inhibitory stabilization can offer an exemplary case of how experimental neuroscience can progress in tandem with technology and theory to advance our understanding of the brain.
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Affiliation(s)
- Sadra Sadeh
- Bioengineering Department, Imperial College London, London, UK
| | - Claudia Clopath
- Bioengineering Department, Imperial College London, London, UK.
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20
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Anastasiades PG, Boada C, Carter AG. Cell-Type-Specific D1 Dopamine Receptor Modulation of Projection Neurons and Interneurons in the Prefrontal Cortex. Cereb Cortex 2020; 29:3224-3242. [PMID: 30566584 DOI: 10.1093/cercor/bhy299] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 11/01/2018] [Accepted: 11/07/2018] [Indexed: 11/14/2022] Open
Abstract
Dopamine modulation in the prefrontal cortex (PFC) mediates diverse effects on neuronal physiology and function, but the expression of dopamine receptors at subpopulations of projection neurons and interneurons remains unresolved. Here, we examine D1 receptor expression and modulation at specific cell types and layers in the mouse prelimbic PFC. We first show that D1 receptors are enriched in pyramidal cells in both layers 5 and 6, and that these cells project to intratelencephalic targets including contralateral cortex, striatum, and claustrum rather than to extratelencephalic structures. We then find that D1 receptors are also present in interneurons and enriched in superficial layer VIP-positive (VIP+) interneurons that coexpresses calretinin but absent from parvalbumin-positive (PV+) and somatostatin-positive (SOM+) interneurons. Finally, we determine that D1 receptors strongly and selectively enhance action potential firing in only a subset of these corticocortical neurons and VIP+ interneurons. Our findings define several novel subpopulations of D1+ neurons, highlighting how modulation via D1 receptors can influence both excitatory and disinhibitory microcircuits in the PFC.
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Affiliation(s)
- Paul G Anastasiades
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, USA
| | - Christina Boada
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, USA
| | - Adam G Carter
- Center for Neural Science, New York University, 4 Washington Place, New York, NY, USA
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21
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Sanzeni A, Histed MH, Brunel N. Response nonlinearities in networks of spiking neurons. PLoS Comput Biol 2020; 16:e1008165. [PMID: 32941457 PMCID: PMC7524009 DOI: 10.1371/journal.pcbi.1008165] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 09/29/2020] [Accepted: 07/19/2020] [Indexed: 01/18/2023] Open
Abstract
Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities are inconsistent with the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the diversity of non-linearities observed in cortical networks.
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Affiliation(s)
- Alessandro Sanzeni
- National institute of Mental Health Intramural Program, NIH, Bethesda, MD, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - Mark H. Histed
- National institute of Mental Health Intramural Program, NIH, Bethesda, MD, USA
| | - Nicolas Brunel
- Department of Neurobiology, Duke University, Durham, NC, USA
- Department of Physics, Duke University, Durham, NC, USA
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22
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Mahrach A, Chen G, Li N, van Vreeswijk C, Hansel D. Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation. eLife 2020; 9:e49967. [PMID: 31951197 PMCID: PMC7012611 DOI: 10.7554/elife.49967] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 12/25/2019] [Indexed: 12/28/2022] Open
Abstract
GABAergic interneurons can be subdivided into three subclasses: parvalbumin positive (PV), somatostatin positive (SOM) and serotonin positive neurons. With principal cells (PCs) they form complex networks. We examine PCs and PV responses in mouse anterior lateral motor cortex (ALM) and barrel cortex (S1) upon PV photostimulation in vivo. In ALM layer five and S1, the PV response is paradoxical: photoexcitation reduces their activity. This is not the case in ALM layer 2/3. We combine analytical calculations and numerical simulations to investigate how these results constrain the architecture. Two-population models cannot explain the results. Four-population networks with V1-like architecture account for the data in ALM layer 2/3 and layer 5. Our data in S1 can be explained if SOM neurons receive inputs only from PCs and PV neurons. In both four-population models, the paradoxical effect implies not too strong recurrent excitation. It is not evidence for stabilization by inhibition.
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Affiliation(s)
- Alexandre Mahrach
- CNRS-UMR 8002, Integrative Neuroscience and Cognition CenterParisFrance
| | - Guang Chen
- Department of NeuroscienceBaylor College of MedicineHoustonUnited States
| | - Nuo Li
- Department of NeuroscienceBaylor College of MedicineHoustonUnited States
| | | | - David Hansel
- CNRS-UMR 8002, Integrative Neuroscience and Cognition CenterParisFrance
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23
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Mechanisms underlying gain modulation in the cortex. Nat Rev Neurosci 2020; 21:80-92. [PMID: 31911627 DOI: 10.1038/s41583-019-0253-y] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2019] [Indexed: 01/19/2023]
Abstract
Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.
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24
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Gleeson P, Cantarelli M, Marin B, Quintana A, Earnshaw M, Sadeh S, Piasini E, Birgiolas J, Cannon RC, Cayco-Gajic NA, Crook S, Davison AP, Dura-Bernal S, Ecker A, Hines ML, Idili G, Lanore F, Larson SD, Lytton WW, Majumdar A, McDougal RA, Sivagnanam S, Solinas S, Stanislovas R, van Albada SJ, van Geit W, Silver RA. Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits. Neuron 2019; 103:395-411.e5. [PMID: 31201122 PMCID: PMC6693896 DOI: 10.1016/j.neuron.2019.05.019] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 03/04/2019] [Accepted: 05/09/2019] [Indexed: 02/07/2023]
Abstract
Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community.
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Affiliation(s)
- Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Matteo Cantarelli
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; MetaCell Limited, Oxford, UK
| | - Boris Marin
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Santo André, Brazil
| | - Adrian Quintana
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Matt Earnshaw
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sadra Sadeh
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Eugenio Piasini
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Computational Neuroscience Initiative and Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
| | - Justas Birgiolas
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | | | - N Alex Cayco-Gajic
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sharon Crook
- School of Life Sciences, Arizona State University, Tempe, AZ, USA; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
| | - Andrew P Davison
- Unité de Neuroscience, Information et Complexité, Centre National de la Recherche Scientifique, Paris, France
| | | | - András Ecker
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael L Hines
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | | | - Frederic Lanore
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | | | - William W Lytton
- SUNY Downstate Medical Center and Kings County Hospital, Brooklyn, NY, USA
| | | | - Robert A McDougal
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA; Center for Medical Informatics, Yale University, New Haven, CT, USA
| | | | - Sergio Solinas
- Department of Biomedical Science, University of Sassari, Sassari, Italy; Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Rokas Stanislovas
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sacha J van Albada
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Werner van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - R Angus Silver
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.
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25
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Correlation Transfer by Layer 5 Cortical Neurons Under Recreated Synaptic Inputs In Vitro. J Neurosci 2019; 39:7648-7663. [PMID: 31346031 DOI: 10.1523/jneurosci.3169-18.2019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 07/06/2019] [Accepted: 07/12/2019] [Indexed: 11/21/2022] Open
Abstract
Correlated electrical activity in neurons is a prominent characteristic of cortical microcircuits. Despite a growing amount of evidence concerning both spike-count and subthreshold membrane potential pairwise correlations, little is known about how different types of cortical neurons convert correlated inputs into correlated outputs. We studied pyramidal neurons and two classes of GABAergic interneurons of layer 5 in neocortical brain slices obtained from rats of both sexes, and we stimulated them with biophysically realistic correlated inputs, generated using dynamic clamp. We found that the physiological differences between cell types manifested unique features in their capacity to transfer correlated inputs. We used linear response theory and computational modeling to gain clear insights into how cellular properties determine both the gain and timescale of correlation transfer, thus tying single-cell features with network interactions. Our results provide further ground for the functionally distinct roles played by various types of neuronal cells in the cortical microcircuit.SIGNIFICANCE STATEMENT No matter how we probe the brain, we find correlated neuronal activity over a variety of spatial and temporal scales. For the cerebral cortex, significant evidence has accumulated on trial-to-trial covariability in synaptic inputs activation, subthreshold membrane potential fluctuations, and output spike trains. Although we do not yet fully understand their origin and whether they are detrimental or beneficial for information processing, we believe that clarifying how correlations emerge is pivotal for understanding large-scale neuronal network dynamics and computation. Here, we report quantitative differences between excitatory and inhibitory cells, as they relay input correlations into output correlations. We explain this heterogeneity by simple biophysical models and provide the most experimentally validated test of a theory for the emergence of correlations.
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26
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Hertäg L, Sprekeler H. Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types. PLoS Comput Biol 2019; 15:e1006999. [PMID: 31095556 PMCID: PMC6541306 DOI: 10.1371/journal.pcbi.1006999] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 05/29/2019] [Accepted: 04/01/2019] [Indexed: 01/24/2023] Open
Abstract
GABAergic interneurons play an important role in shaping the activity of excitatory pyramidal cells (PCs). How the various inhibitory cell types contribute to neuronal information processing, however, is not resolved. Here, we propose a functional role for a widespread network motif consisting of parvalbumin- (PV), somatostatin- (SOM) and vasoactive intestinal peptide (VIP)-expressing interneurons. Following the idea that PV and SOM interneurons control the distribution of somatic and dendritic inhibition onto PCs, we suggest that mutual inhibition between VIP and SOM cells translates weak inputs to VIP interneurons into large changes of somato-dendritic inhibition of PCs. Using a computational model, we show that the neuronal and synaptic properties of the circuit support this hypothesis. Moreover, we demonstrate that the SOM-VIP motif allows transient inputs to persistently switch the circuit between two processing modes, in which top-down inputs onto apical dendrites of PCs are either integrated or cancelled. Neurons in the brain can be classified as excitatory or inhibitory based on whether they activate or deactivate the cells to whom they send signals. Compared to their excitatory counterpart, inhibitory neurons present themselves as a wild diversity of cell classes. It is broadly believed that these classes serve different purposes, but as of now, those are poorly understood. In this article, we suggest how an intricate interplay of three inhibitory cell classes can control whether internal signals—such as predictions, memory signals or motor commands—are taken into account when sensory signals are interpreted. Using a mathematical model and computer simulations, we show that such internal signals can be shut down by regulating which inhibitory cell types are active, and that the interaction of different cell classes allows weak control signals to do so.
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Affiliation(s)
- Loreen Hertäg
- Modelling of Cognitive Processes, Berlin Institute of Technology, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Henning Sprekeler
- Modelling of Cognitive Processes, Berlin Institute of Technology, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
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27
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Khan AG, Hofer SB. Contextual signals in visual cortex. Curr Opin Neurobiol 2018; 52:131-138. [PMID: 29883940 DOI: 10.1016/j.conb.2018.05.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 05/11/2018] [Indexed: 11/15/2022]
Abstract
Vision is an active process. What we perceive strongly depends on our actions, intentions and expectations. During visual processing, these internal signals therefore need to be integrated with the visual information from the retina. The mechanisms of how this is achieved by the visual system are still poorly understood. Advances in recording and manipulating neuronal activity in specific cell types and axonal projections together with tools for circuit tracing are beginning to shed light on the neuronal circuit mechanisms of how internal, contextual signals shape sensory representations. Here we review recent work, primarily in mice, that has advanced our understanding of these processes, focusing on contextual signals related to locomotion, behavioural relevance and predictions.
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Affiliation(s)
- Adil G Khan
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sonja B Hofer
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK.
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28
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Pakan JM, Francioni V, Rochefort NL. Action and learning shape the activity of neuronal circuits in the visual cortex. Curr Opin Neurobiol 2018; 52:88-97. [PMID: 29727859 PMCID: PMC6562203 DOI: 10.1016/j.conb.2018.04.020] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 04/13/2018] [Indexed: 11/25/2022]
Abstract
Arousal and locomotion modulate neuronal activity in primary visual cortex. Neurons in primary visual cortex respond to visuomotor mismatch. Experience shapes neuronal responses to familiar stimuli, reward and object location. Neuronal representations of visual stimuli are modulated according to the behavioural relevance of the stimuli. Neuromodulatory, top-down and thalamocortical inputs convey arousal-related and motor-related signals to primary visual cortex.
Nonsensory variables strongly influence neuronal activity in the adult mouse primary visual cortex. Neuronal responses to visual stimuli are modulated by behavioural state, such as arousal and motor activity, and are shaped by experience. This dynamic process leads to neural representations in the visual cortex that reflect stimulus familiarity, expectations of reward and object location, and mismatch between self-motion and visual-flow. The recent development of genetic tools and recording techniques in awake behaving mice has enabled the investigation of the circuit mechanisms underlying state-dependent and experience-dependent neuronal representations in primary visual cortex. These neuronal circuits involve neuromodulatory, top-down cortico-cortical and thalamocortical pathways. The functions of nonsensory signals at this early stage of visual information processing are now beginning to be unravelled.
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Affiliation(s)
- Janelle Mp Pakan
- Center for Behavioral Brain Sciences, Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, Magdeburg, Germany; German Center for Neurodegenerative Diseases, Magdeburg, Germany
| | - Valerio Francioni
- Centre for Discovery Brain Sciences, School of Biomedical Sciences, Edinburgh, United Kingdom
| | - Nathalie L Rochefort
- Centre for Discovery Brain Sciences, School of Biomedical Sciences, Edinburgh, United Kingdom; Simons Initiative for the Developing Brain, Edinburgh, United Kingdom.
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29
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A disinhibitory circuit motif and flexible information routing in the brain. Curr Opin Neurobiol 2018; 49:75-83. [PMID: 29414069 DOI: 10.1016/j.conb.2018.01.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 12/26/2017] [Accepted: 01/05/2018] [Indexed: 12/28/2022]
Abstract
In the mammalian neocortex, an area typically receives inputs from, and projects to, dozens of other areas. Mechanisms are needed to flexibly route information to the right place at the right time, which we term 'pathway gating'. For instance, a region in your brain that receives signals from both visual and auditory pathways may want to 'gate in' the visual pathway while 'gating out' the auditory pathway when you try to read a book surrounded by people in a noisy café. In this review, we marshall experimental and computational evidence in support of a circuit mechanism for flexible pathway gating realized by a disinhibitory motif. Moreover, recent work shows an increasing preponderance of this disinhibitory motif from sensory areas to association areas of the mammalian cortex. Pathway input gating is briefly compared with alternative or complementary gating mechanisms. Predictions and open questions for future research on this puzzle about the complex brain system will be discussed.
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