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Yan C, Mercaldo V, Jacob AD, Kramer E, Mocle A, Ramsaran AI, Tran L, Rashid AJ, Park S, Insel N, Redish AD, Frankland PW, Josselyn SA. Higher-order interactions between hippocampal CA1 neurons are disrupted in amnestic mice. Nat Neurosci 2024; 27:1794-1804. [PMID: 39030342 DOI: 10.1038/s41593-024-01713-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 06/18/2024] [Indexed: 07/21/2024]
Abstract
Across systems, higher-order interactions between components govern emergent dynamics. Here we tested whether contextual threat memory retrieval in mice relies on higher-order interactions between dorsal CA1 hippocampal neurons requiring learning-induced dendritic spine plasticity. We compared population-level Ca2+ transients as wild-type mice (with intact learning-induced spine plasticity and memory) and amnestic mice (TgCRND8 mice with high levels of amyloid-β and deficits in learning-induced spine plasticity and memory) were tested for memory. Using machine-learning classifiers with different capacities to use input data with complex interactions, our findings indicate complex neuronal interactions in the memory representation of wild-type, but not amnestic, mice. Moreover, a peptide that partially restored learning-induced spine plasticity also restored the statistical complexity of the memory representation and memory behavior in Tg mice. These findings provide a previously missing bridge between levels of analysis in memory research, linking receptors, spines, higher-order neuronal dynamics and behavior.
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Affiliation(s)
- Chen Yan
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- DeepMind, London, UK
| | - Valentina Mercaldo
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Alexander D Jacob
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Emily Kramer
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Andrew Mocle
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Adam I Ramsaran
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Lina Tran
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Asim J Rashid
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Sungmo Park
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Nathan Insel
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Psychology, University of Montana, Missoula, MT, USA
- Department of Psychology, Wilfrid Laurier University, Waterloo, Ontario, Canada
| | - A David Redish
- Dept. of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Paul W Frankland
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada
- Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada
- Child & Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada
| | - Sheena A Josselyn
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada.
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada.
- Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada.
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2
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Domanski APF, Kucewicz MT, Russo E, Tricklebank MD, Robinson ESJ, Durstewitz D, Jones MW. Distinct hippocampal-prefrontal neural assemblies coordinate memory encoding, maintenance, and recall. Curr Biol 2023; 33:1220-1236.e4. [PMID: 36898372 PMCID: PMC10728550 DOI: 10.1016/j.cub.2023.02.029] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/05/2023] [Accepted: 02/08/2023] [Indexed: 03/11/2023]
Abstract
Short-term memory enables incorporation of recent experience into subsequent decision-making. This processing recruits both the prefrontal cortex and hippocampus, where neurons encode task cues, rules, and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC populations lead in maintaining sample information across delays of an operant non-match to sample task, despite individual neurons firing only transiently. During sample encoding, distinct mPFC subpopulations joined distributed CA1-mPFC cell assemblies hallmarked by 4-5 Hz rhythmic modulation; CA1-mPFC assemblies re-emerged during choice episodes but were not 4-5 Hz modulated. Delay-dependent errors arose when attenuated rhythmic assembly activity heralded collapse of sustained mPFC encoding. Our results map component processes of memory-guided decisions onto heterogeneous CA1-mPFC subpopulations and the dynamics of physiologically distinct, distributed cell assemblies.
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Affiliation(s)
- Aleksander P F Domanski
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK; The Alan Turing Institute, British Library, 96 Euston Rd, London, UK; The Francis Crick Institute, 1 Midland Road, London, UK
| | - Michal T Kucewicz
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK; BioTechMed Center, Brain & Mind Electrophysiology Laboratory, Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland.
| | - Eleonora Russo
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany; Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany
| | - Mark D Tricklebank
- Centre for Neuroimaging Science, King's College London, Denmark Hill, London SE5 8AF, UK
| | - Emma S J Robinson
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Matt W Jones
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK
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3
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Shomali SR, Rasuli SN, Ahmadabadi MN, Shimazaki H. Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons. Commun Biol 2023; 6:169. [PMID: 36792689 PMCID: PMC9932086 DOI: 10.1038/s42003-023-04511-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 01/20/2023] [Indexed: 02/17/2023] Open
Abstract
Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.
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Affiliation(s)
- Safura Rashid Shomali
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5746, Iran.
| | - Seyyed Nader Rasuli
- grid.418744.a0000 0000 8841 7951School of Physics, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5531 Iran ,grid.411872.90000 0001 2087 2250Department of Physics, University of Guilan, Rasht, 41335-1914 Iran
| | - Majid Nili Ahmadabadi
- grid.46072.370000 0004 0612 7950Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14395-515 Iran
| | - Hideaki Shimazaki
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan. .,Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Hokkaido, 060-0812, Japan.
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Koren V, Bondanelli G, Panzeri S. Computational methods to study information processing in neural circuits. Comput Struct Biotechnol J 2023; 21:910-922. [PMID: 36698970 PMCID: PMC9851868 DOI: 10.1016/j.csbj.2023.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 01/09/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
The brain is an information processing machine and thus naturally lends itself to be studied using computational tools based on the principles of information theory. For this reason, computational methods based on or inspired by information theory have been a cornerstone of practical and conceptual progress in neuroscience. In this Review, we address how concepts and computational tools related to information theory are spurring the development of principled theories of information processing in neural circuits and the development of influential mathematical methods for the analyses of neural population recordings. We review how these computational approaches reveal mechanisms of essential functions performed by neural circuits. These functions include efficiently encoding sensory information and facilitating the transmission of information to downstream brain areas to inform and guide behavior. Finally, we discuss how further progress and insights can be achieved, in particular by studying how competing requirements of neural encoding and readout may be optimally traded off to optimize neural information processing.
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Affiliation(s)
- Veronika Koren
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, Hamburg 20251, Germany
| | | | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, Hamburg 20251, Germany
- Istituto Italiano di Tecnologia, Via Melen 83, Genova 16152, Italy
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5
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Panzeri S, Moroni M, Safaai H, Harvey CD. The structures and functions of correlations in neural population codes. Nat Rev Neurosci 2022; 23:551-567. [PMID: 35732917 DOI: 10.1038/s41583-022-00606-4] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2022] [Indexed: 12/17/2022]
Abstract
The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.
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Affiliation(s)
- Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany. .,Istituto Italiano di Tecnologia, Rovereto, Italy.
| | | | - Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
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Balaguer-Ballester E, Nogueira R, Abolafia JM, Moreno-Bote R, Sanchez-Vives MV. Correction: Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions. PLoS Comput Biol 2021; 17:e1009710. [PMID: 34962923 PMCID: PMC8714014 DOI: 10.1371/journal.pcbi.1009710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pcbi.1007862.].
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7
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Baravalle R, Montani F. Heterogeneity across neural populations: Its significance for the dynamics and functions of neural circuits. Phys Rev E 2021; 103:042308. [PMID: 34005927 DOI: 10.1103/physreve.103.042308] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 03/18/2021] [Indexed: 11/07/2022]
Abstract
Neural populations show patterns of synchronous activity, as they share common correlated inputs. Neurons in the cortex that are connected by strong synapses cause rapid firing explosions. In addition, areas that are connected by weaker synapses have a slower dynamics and they can contribute to asymmetries in the input distributions. The aim of this work is to develop a neural model to investigate how the heterogeneities in the synaptic input distributions affect different levels of organizational activity in the brain dynamics. We analytically show how small changes in the correlation inputs can cause large changes in the interactions of the outputs that lead to a phase transition, demonstrating that a simple variation in the direction of a biased skewed distribution in the neuronal inputs can generate a transition of states in the firing rate, passing from spontaneous silence ("down state") to an absolute spiking activity ("up state"). We present an exact quantification of the dynamics of the output variables, showing that when considering a biased skewed distribution in the inputs of neuronal population, the critical point is not in an asynchronous or synchronous state but rather at an intermediate value.
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Affiliation(s)
- Roman Baravalle
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata (1900) La Plata, Argentina
| | - Fernando Montani
- Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata (1900) La Plata, Argentina
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