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Hoffmann H. Computational model of layer 2/3 in mouse primary visual cortex explains observed visuomotor mismatch response. J Comput Neurosci 2024:10.1007/s10827-024-00882-2. [PMID: 39340618 DOI: 10.1007/s10827-024-00882-2] [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: 12/10/2023] [Revised: 07/29/2024] [Accepted: 09/13/2024] [Indexed: 09/30/2024]
Abstract
Activity in layer 2/3 of the mouse primary visual cortex has been shown to depend both on visual input and the mouse's locomotion. Moreover, this activity is altered by a mismatch between the observed visual flow and the predicted visual flow from locomotion. Here, I present a simple computational model that explains previously reported recordings from layer 2/3 neurons in mice. In my model, layer 2/3 encodes the velocity difference between the estimate from visual flow and the prediction from locomotion using a neural population code. Moreover, I describe a hypothesized mechanism for how the brain may carry out computations of variables encoded in population codes. This mechanism may point to a general principle for computing any mathematical function in the brain.
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Affiliation(s)
- Heiko Hoffmann
- Magimine, LLC, P.O. Box 941154, Simi Valley, CA, 93094, USA.
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2
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O'Reilly JA, Zhu JD, Sowman PF. Localized estimation of event-related neural source activity from simultaneous MEG-EEG with a recurrent neural network. Neural Netw 2024; 180:106731. [PMID: 39303603 DOI: 10.1016/j.neunet.2024.106731] [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: 03/13/2024] [Revised: 09/05/2024] [Accepted: 09/10/2024] [Indexed: 09/22/2024]
Abstract
Estimating intracranial current sources underlying the electromagnetic signals observed from extracranial sensors is a perennial challenge in non-invasive neuroimaging. Established solutions to this inverse problem treat time samples independently without considering the temporal dynamics of event-related brain processes. This paper describes current source estimation from simultaneously recorded magneto- and electro-encephalography (MEEG) using a recurrent neural network (RNN) that learns sequential relationships from neural data. The RNN was trained in two phases: (1) pre-training and (2) transfer learning with L1 regularization applied to the source estimation layer. Performance of using scaled labels derived from MEEG, magnetoencephalography (MEG), or electroencephalography (EEG) were compared, as were results from volumetric source space with free dipole orientation and surface source space with fixed dipole orientation. Exact low-resolution electromagnetic tomography (eLORETA) and mixed-norm L1/L2 (MxNE) source estimation methods were also applied to these data for comparison with the RNN method. The RNN approach outperformed other methods in terms of output signal-to-noise ratio, correlation and mean-squared error metrics evaluated against reference event-related field (ERF) and event-related potential (ERP) waveforms. Using MEEG labels with fixed-orientation surface sources produced the most consistent estimates. To estimate sources of ERF and ERP waveforms, the RNN generates temporal dynamics within its internal computational units, driven by sequential structure in neural data used as training labels. It thus provides a data-driven model of computational transformations from psychophysiological events into corresponding event-related neural signals, which is unique among MEEG source reconstruction solutions.
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Affiliation(s)
- Jamie A O'Reilly
- School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand.
| | - Judy D Zhu
- School of Psychological Sciences, Macquarie University, New South Wales, 2109, Australia
| | - Paul F Sowman
- School of Psychological Sciences, Macquarie University, New South Wales, 2109, Australia; School of Clinical Sciences, Auckland University of Technology, Auckland, 1142, New Zealand
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3
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Jiang HJ, Qi G, Duarte R, Feldmeyer D, van Albada SJ. A layered microcircuit model of somatosensory cortex with three interneuron types and cell-type-specific short-term plasticity. Cereb Cortex 2024; 34:bhae378. [PMID: 39344196 PMCID: PMC11439972 DOI: 10.1093/cercor/bhae378] [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: 11/03/2023] [Revised: 07/17/2024] [Accepted: 09/04/2024] [Indexed: 10/01/2024] Open
Abstract
Three major types of GABAergic interneurons, parvalbumin-, somatostatin-, and vasoactive intestinal peptide-expressing (PV, SOM, VIP) cells, play critical but distinct roles in the cortical microcircuitry. Their specific electrophysiology and connectivity shape their inhibitory functions. To study the network dynamics and signal processing specific to these cell types in the cerebral cortex, we developed a multi-layer model incorporating biologically realistic interneuron parameters from rodent somatosensory cortex. The model is fitted to in vivo data on cell-type-specific population firing rates. With a protocol of cell-type-specific stimulation, network responses when activating different neuron types are examined. The model reproduces the experimentally observed inhibitory effects of PV and SOM cells and disinhibitory effect of VIP cells on excitatory cells. We further create a version of the model incorporating cell-type-specific short-term synaptic plasticity (STP). While the ongoing activity with and without STP is similar, STP modulates the responses of Exc, SOM, and VIP cells to cell-type-specific stimulation, presumably by changing the dominant inhibitory pathways. With slight adjustments, the model also reproduces sensory responses of specific interneuron types recorded in vivo. Our model provides predictions on network dynamics involving cell-type-specific short-term plasticity and can serve to explore the computational roles of inhibitory interneurons in sensory functions.
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Affiliation(s)
- Han-Jia Jiang
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
- Institute of Zoology, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany
| | - Guanxiao Qi
- JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
| | - Renato Duarte
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
- Center for Neuroscience and Cell Biology (CNC-UC), University of Coimbra, Palace of Schools, 3004-531 Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Palace of Schools, 3004-531 Coimbra, Portugal
| | - Dirk Feldmeyer
- JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Sacha J van Albada
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
- Institute of Zoology, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany
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4
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Cole N, Harvey M, Myers-Joseph D, Gilra A, Khan AG. Prediction-error signals in anterior cingulate cortex drive task-switching. Nat Commun 2024; 15:7088. [PMID: 39154045 PMCID: PMC11330528 DOI: 10.1038/s41467-024-51368-9] [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: 04/30/2024] [Accepted: 08/05/2024] [Indexed: 08/19/2024] Open
Abstract
Task-switching is a fundamental cognitive ability that allows animals to update their knowledge of current rules or contexts. Detecting discrepancies between predicted and observed events is essential for this process. However, little is known about how the brain computes cognitive prediction-errors and whether neural prediction-error signals are causally related to task-switching behaviours. Here we trained mice to use a prediction-error to switch, in a single trial, between responding to the same stimuli using two distinct rules. Optogenetic silencing and un-silencing, together with widefield and two-photon calcium imaging revealed that the anterior cingulate cortex (ACC) was specifically required for this rapid task-switching, but only when it exhibited neural prediction-error signals. These prediction-error signals were projection-target dependent and were larger preceding successful behavioural transitions. An all-optical approach revealed a disinhibitory interneuron circuit required for successful prediction-error computation. These results reveal a circuit mechanism for computing prediction-errors and transitioning between distinct cognitive states.
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Affiliation(s)
- Nicholas Cole
- Centre for Developmental Neurobiology, King's College London, London, UK
| | - Matthew Harvey
- Centre for Developmental Neurobiology, King's College London, London, UK
| | - Dylan Myers-Joseph
- Centre for Developmental Neurobiology, King's College London, London, UK
| | - Aditya Gilra
- Machine Learning Group, Centrum Wiskunde & Informatica, Amsterdam, the Netherlands
- Department of Computer Science, The University of Sheffield, Sheffield, UK
| | - Adil G Khan
- Centre for Developmental Neurobiology, King's College London, London, UK.
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5
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Wang B, Audette NJ, Schneider DM, Aljadeff J. Desegregation of neuronal predictive processing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.05.606684. [PMID: 39149380 PMCID: PMC11326200 DOI: 10.1101/2024.08.05.606684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Neural circuits construct internal 'world-models' to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors is a signature of those internal models. Here, to understand how the brain generates predictions for complex sensorimotor signals, we investigate the emergence of high-dimensional, multi-modal predictive representations in recurrent networks. We find that robust predictive processing arises in a network with loose excitatory/inhibitory balance. Contrary to previous proposals of functionally specialized cell-types, the network exhibits desegregation of stimulus and prediction-error representations. We confirmed these model predictions by experimentally probing predictive-coding circuits using a rich stimulus-set to violate learned expectations. When constrained by data, our model further reveals and makes concrete testable experimental predictions for the distinct functional roles of excitatory and inhibitory neurons, and of neurons in different layers along a laminar hierarchy, in computing multi-modal predictions. These results together imply that in natural conditions, neural representations of internal models are highly distributed, yet structured to allow flexible readout of behaviorally-relevant information. The generality of our model advances the understanding of computation of internal models across species, by incorporating different types of predictive computations into a unified framework.
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Affiliation(s)
- Bin Wang
- Department of Physics, University of California San Diego, La Jolla, CA, 92093, USA
| | | | - David M Schneider
- Center for Neural Science, New York University, New York, NY 10003, USA
| | - Johnatan Aljadeff
- Department of Neurobiology, University of California San Diego, La Jolla, CA, 92093, USA
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6
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Klug S, Murgaš M, Godbersen GM, Hacker M, Lanzenberger R, Hahn A. Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain. Neuroimage 2024; 295:120658. [PMID: 38810891 DOI: 10.1016/j.neuroimage.2024.120658] [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: 01/15/2024] [Revised: 04/22/2024] [Accepted: 05/27/2024] [Indexed: 05/31/2024] Open
Abstract
PURPOSE The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.
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Affiliation(s)
- Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria.
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7
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Teckentrup V, Kroemer NB. Mechanisms for survival: vagal control of goal-directed behavior. Trends Cogn Sci 2024; 28:237-251. [PMID: 38036309 DOI: 10.1016/j.tics.2023.11.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023]
Abstract
Survival is a fundamental physiological drive, and neural circuits have evolved to prioritize actions that meet the energy demands of the body. This fine-tuning of goal-directed actions based on metabolic states ('allostasis') is deeply rooted in our brain, and hindbrain nuclei orchestrate the vital communication between the brain and body through the vagus nerve. Despite mounting evidence for vagal control of allostatic behavior in animals, its broader function in humans is still contested. Based on stimulation studies, we propose that the vagal afferent pathway supports transitions between survival modes by gating the integration of ascending bodily signals, thereby regulating reward-seeking. By reconceptualizing vagal signals as catalysts for goal-directed behavior, our perspective opens new avenues for theory-driven translational work in mental disorders.
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Affiliation(s)
- Vanessa Teckentrup
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72076 Tübingen, Germany; School of Psychology and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Nils B Kroemer
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72076 Tübingen, Germany; Section of Medical Psychology, Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Bonn, 53127 Bonn, Germany; German Center for Mental Health (DZPG), 72076 Tübingen, Germany.
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8
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Galván Fraile J, Scherr F, Ramasco JJ, Arkhipov A, Maass W, Mirasso CR. Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations. PLoS Comput Biol 2024; 20:e1011921. [PMID: 38452057 PMCID: PMC10950248 DOI: 10.1371/journal.pcbi.1011921] [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: 10/12/2023] [Revised: 03/19/2024] [Accepted: 02/18/2024] [Indexed: 03/09/2024] Open
Abstract
In an ever-changing visual world, animals' survival depends on their ability to perceive and respond to rapidly changing motion cues. The primary visual cortex (V1) is at the forefront of this sensory processing, orchestrating neural responses to perturbations in visual flow. However, the underlying neural mechanisms that lead to distinct cortical responses to such perturbations remain enigmatic. In this study, our objective was to uncover the neural dynamics that govern V1 neurons' responses to visual flow perturbations using a biologically realistic computational model. By subjecting the model to sudden changes in visual input, we observed opposing cortical responses in excitatory layer 2/3 (L2/3) neurons, namely, depolarizing and hyperpolarizing responses. We found that this segregation was primarily driven by the competition between external visual input and recurrent inhibition, particularly within L2/3 and L4. This division was not observed in excitatory L5/6 neurons, suggesting a more prominent role for inhibitory mechanisms in the visual processing of the upper cortical layers. Our findings share similarities with recent experimental studies focusing on the opposing influence of top-down and bottom-up inputs in the mouse primary visual cortex during visual flow perturbations.
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Affiliation(s)
- J. Galván Fraile
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), UIB-CSIC, Palma de Mallorca, Spain
| | - Franz Scherr
- Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - José J. Ramasco
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), UIB-CSIC, Palma de Mallorca, Spain
| | - Anton Arkhipov
- Allen Institute, Seattle, Washington, United States of America
| | - Wolfgang Maass
- Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - Claudio R. Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC), UIB-CSIC, Palma de Mallorca, Spain
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9
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O'Toole SM, Oyibo HK, Keller GB. Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses. Neuron 2023; 111:2918-2928.e8. [PMID: 37708892 DOI: 10.1016/j.neuron.2023.08.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 04/19/2023] [Accepted: 08/14/2023] [Indexed: 09/16/2023]
Abstract
Predictive processing postulates the existence of prediction error neurons in cortex. Neurons with both negative and positive prediction error response properties have been identified in layer 2/3 of visual cortex, but whether they correspond to transcriptionally defined subpopulations is unclear. Here we used the activity-dependent, photoconvertible marker CaMPARI2 to tag neurons in layer 2/3 of mouse visual cortex during stimuli and behaviors designed to evoke prediction errors. We performed single-cell RNA-sequencing on these populations and found that previously annotated Adamts2 and Rrad layer 2/3 transcriptional cell types were enriched when photolabeling during stimuli that drive negative or positive prediction error responses, respectively. Finally, we validated these results functionally by designing artificial promoters for use in AAV vectors to express genetically encoded calcium indicators. Thus, transcriptionally distinct cell types in layer 2/3 that can be targeted using AAV vectors exhibit distinguishable negative and positive prediction error responses.
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Affiliation(s)
- Sean M O'Toole
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Hassana K Oyibo
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Georg B Keller
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland; Faculty of Science, University of Basel, Basel, Switzerland.
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10
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Keijser J, Sprekeler H. Cortical interneurons: fit for function and fit to function? Evidence from development and evolution. Front Neural Circuits 2023; 17:1172464. [PMID: 37215503 PMCID: PMC10192557 DOI: 10.3389/fncir.2023.1172464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 03/30/2023] [Indexed: 05/24/2023] Open
Abstract
Cortical inhibitory interneurons form a broad spectrum of subtypes. This diversity suggests a division of labor, in which each cell type supports a distinct function. In the present era of optimisation-based algorithms, it is tempting to speculate that these functions were the evolutionary or developmental driving force for the spectrum of interneurons we see in the mature mammalian brain. In this study, we evaluated this hypothesis using the two most common interneuron types, parvalbumin (PV) and somatostatin (SST) expressing cells, as examples. PV and SST interneurons control the activity in the cell bodies and the apical dendrites of excitatory pyramidal cells, respectively, due to a combination of anatomical and synaptic properties. But was this compartment-specific inhibition indeed the function for which PV and SST cells originally evolved? Does the compartmental structure of pyramidal cells shape the diversification of PV and SST interneurons over development? To address these questions, we reviewed and reanalyzed publicly available data on the development and evolution of PV and SST interneurons on one hand, and pyramidal cell morphology on the other. These data speak against the idea that the compartment structure of pyramidal cells drove the diversification into PV and SST interneurons. In particular, pyramidal cells mature late, while interneurons are likely committed to a particular fate (PV vs. SST) during early development. Moreover, comparative anatomy and single cell RNA-sequencing data indicate that PV and SST cells, but not the compartment structure of pyramidal cells, existed in the last common ancestor of mammals and reptiles. Specifically, turtle and songbird SST cells also express the Elfn1 and Cbln4 genes that are thought to play a role in compartment-specific inhibition in mammals. PV and SST cells therefore evolved and developed the properties that allow them to provide compartment-specific inhibition before there was selective pressure for this function. This suggest that interneuron diversity originally resulted from a different evolutionary driving force and was only later co-opted for the compartment-specific inhibition it seems to serve in mammals today. Future experiments could further test this idea using our computational reconstruction of ancestral Elfn1 protein sequences.
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Affiliation(s)
- Joram Keijser
- Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany
- Einstein Center for Neurosciences, Charité University Medicine Berlin, Berlin, Germany
| | - Henning Sprekeler
- Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt University of Berlin, Berlin, Germany
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11
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Abdeladim L, Shin H, Jagadisan UK, Ogando MB, Adesnik H. Probing inter-areal computations with a cellular resolution two-photon holographic mesoscope. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.02.530875. [PMID: 37090604 PMCID: PMC10120651 DOI: 10.1101/2023.03.02.530875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Brain computation depends on intricately connected yet highly distributed neural networks. Due to the absence of the requisite technologies, causally testing fundamental hypotheses on the nature of inter-areal processing have remained largely out-of-each. Here we developed the first two photon holographic mesoscope, a system capable of simultaneously reading and writing neural activity patterns with single cell resolution across large regions of the brain. We demonstrate the precise photo-activation of spatial and temporal sequences of neurons in one brain area while reading out the downstream effect in several other regions. Investigators can use this new platform to understand feed-forward and feed-back processing in distributed neural circuits with single cell precision for the first time.
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12
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Fang C, Aronov D, Abbott LF, Mackevicius EL. Neural learning rules for generating flexible predictions and computing the successor representation. eLife 2023; 12:e80680. [PMID: 36928104 PMCID: PMC10019889 DOI: 10.7554/elife.80680] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 10/26/2022] [Indexed: 03/18/2023] Open
Abstract
The predictive nature of the hippocampus is thought to be useful for memory-guided cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been formalized as a predictive map called the successor representation (SR). The SR captures a number of observations about hippocampal activity. However, the algorithm does not provide a neural mechanism for how such representations arise. Here, we show the dynamics of a recurrent neural network naturally calculate the SR when the synaptic weights match the transition probability matrix. Interestingly, the predictive horizon can be flexibly modulated simply by changing the network gain. We derive simple, biologically plausible learning rules to learn the SR in a recurrent network. We test our model with realistic inputs and match hippocampal data recorded during random foraging. Taken together, our results suggest that the SR is more accessible in neural circuits than previously thought and can support a broad range of cognitive functions.
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Affiliation(s)
- Ching Fang
- Zuckerman Institute, Department of Neuroscience, Columbia UniversityNew YorkUnited States
| | - Dmitriy Aronov
- Zuckerman Institute, Department of Neuroscience, Columbia UniversityNew YorkUnited States
| | - LF Abbott
- Zuckerman Institute, Department of Neuroscience, Columbia UniversityNew YorkUnited States
| | - Emily L Mackevicius
- Zuckerman Institute, Department of Neuroscience, Columbia UniversityNew YorkUnited States
- Basis Research InstituteNew YorkUnited States
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13
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Wu YK, Miehl C, Gjorgjieva J. Regulation of circuit organization and function through inhibitory synaptic plasticity. Trends Neurosci 2022; 45:884-898. [PMID: 36404455 DOI: 10.1016/j.tins.2022.10.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/02/2022] [Accepted: 10/04/2022] [Indexed: 11/15/2022]
Abstract
Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning.
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Affiliation(s)
- Yue Kris Wu
- School of Life Sciences, Technical University of Munich, Freising, Germany; Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Christoph Miehl
- School of Life Sciences, Technical University of Munich, Freising, Germany; Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Julijana Gjorgjieva
- School of Life Sciences, Technical University of Munich, Freising, Germany; Max Planck Institute for Brain Research, Frankfurt, Germany.
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14
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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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O'Reilly JA. Recurrent Neural Network Model of Human Event-related Potentials in Response to Intensity Oddball Stimulation. Neuroscience 2022; 504:63-74. [DOI: 10.1016/j.neuroscience.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/27/2022] [Accepted: 10/03/2022] [Indexed: 10/31/2022]
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O'Reilly JA, Angsuwatanakul T, Wehrman J. Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate 'danger' and 'safety' units. Eur J Neurosci 2022; 56:4154-4175. [PMID: 35695993 PMCID: PMC9545291 DOI: 10.1111/ejn.15736] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 12/27/2022]
Abstract
The ability to respond appropriately to sensory information received from the external environment is among the most fundamental capabilities of central nervous systems. In the auditory domain, processes underlying this behaviour are studied by measuring auditory‐evoked electrophysiology during sequences of sounds with predetermined regularities. Identifying neural correlates of ensuing auditory novelty responses is supported by research in experimental animals. In the present study, we reanalysed epidural field potential recordings from the auditory cortex of anaesthetised mice during frequency and intensity oddball stimulation. Multivariate pattern analysis (MVPA) and hierarchical recurrent neural network (RNN) modelling were adopted to explore these data with greater resolution than previously considered using conventional methods. Time‐wise and generalised temporal decoding MVPA approaches revealed previously underestimated asymmetry between responses to sound‐level transitions in the intensity oddball paradigm, in contrast with tone frequency changes. After training, the cross‐validated RNN model architecture with four hidden layers produced output waveforms in response to simulated auditory inputs that were strongly correlated with grand‐average auditory‐evoked potential waveforms (r2 > .9). Units in hidden layers were classified based on their temporal response properties and characterised using principal component analysis and sample entropy. These demonstrated spontaneous alpha rhythms, sound onset and offset responses and putative ‘safety’ and ‘danger’ units activated by relatively inconspicuous and salient changes in auditory inputs, respectively. The hypothesised existence of corresponding biological neural sources is naturally derived from this model. If proven, this could have significant implications for prevailing theories of auditory processing.
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Affiliation(s)
- Jamie A O'Reilly
- College of Biomedical Engineering, Rangsit University, Lak Hok, Thailand
| | | | - Jordan Wehrman
- Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
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