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Kuan AT, Bondanelli G, Driscoll LN, Han J, Kim M, Hildebrand DGC, Graham BJ, Wilson DE, Thomas LA, Panzeri S, Harvey CD, Lee WCA. Synaptic wiring motifs in posterior parietal cortex support decision-making. Nature 2024; 627:367-373. [PMID: 38383788 DOI: 10.1038/s41586-024-07088-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 01/17/2024] [Indexed: 02/23/2024]
Abstract
The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks1-10. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. This motif was present even between neurons with activity peaks in different task epochs. We developed neural-circuit models of the computations performed by these motifs, and found that opponent inhibition between neural populations with opposite selectivity amplifies selective inputs, thereby improving the encoding of trial-type information. The models also predict that opponent inhibition between neurons with activity peaks in different task epochs contributes to creating choice-specific sequential activity. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.
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Affiliation(s)
- Aaron T Kuan
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Giulio Bondanelli
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genoa, Italy
- Department of Excellence for Neural Information Processing, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Laura N Driscoll
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Allen Institute for Neural Dynamics, Allen Institute, Seattle, WA, USA
| | - Julie Han
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Khoury College of Computer Sciences, Northeastern University, Seattle, WA, USA
| | - Minsu Kim
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - David G C Hildebrand
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA
| | - Brett J Graham
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Space Telescope Science Institute, Baltimore, MD, USA
| | - Daniel E Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Logan A Thomas
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Biophysics Graduate Group, University of California Berkeley, Berkeley, CA, USA
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genoa, Italy.
- Department of Excellence for Neural Information Processing, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
| | | | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
- FM Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA.
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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,Corresponding author at: Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, Hamburg 20251, Germany.
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3
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Bondanelli G, Panzeri S. Neuroscience: Turbulent times for brain information processing. Curr Biol 2021; 31:R1400-R1402. [PMID: 34699808 DOI: 10.1016/j.cub.2021.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
A recent study shows that rare long-range connections between brain areas may considerably improve transmission of information between areas. The study suggests that information may propagate better through long-range connections when neural activity exhibits turbulent dynamics.
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Affiliation(s)
- Giulio Bondanelli
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy; Department of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
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4
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Valente M, Pica G, Bondanelli G, Moroni M, Runyan CA, Morcos AS, Harvey CD, Panzeri S. Correlations enhance the behavioral readout of neural population activity in association cortex. Nat Neurosci 2021; 24:975-986. [PMID: 33986549 PMCID: PMC8559600 DOI: 10.1038/s41593-021-00845-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 03/24/2021] [Indexed: 02/03/2023]
Abstract
Noise correlations (that is, trial-to-trial covariations in neural activity for a given stimulus) limit the stimulus information encoded by neural populations, leading to the widely held prediction that they impair perceptual discrimination behaviors. However, this prediction neglects the effects of correlations on information readout. We studied how correlations affect both encoding and readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations reduced the encoded stimulus information, but, seemingly paradoxically, were higher when mice made correct rather than incorrect choices. Single-trial behavioral choices depended not only on the stimulus information encoded by the whole population, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, they enhanced the conversion of sensory information into behavioral choices, overcoming their detrimental information-limiting effects. Thus, correlations in association cortex can benefit task performance even if they decrease sensory information.
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Affiliation(s)
- Martina Valente
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
- Centro Interdisciplinare Mente e Cervello (CIMeC), University of Trento, Rovereto, Italy
| | - Giuseppe Pica
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Giulio Bondanelli
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Monica Moroni
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
| | | | - Ari S Morcos
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | | | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy.
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Abstract
Across sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of recurrent interactions at the network level. To test this hypothesis, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli, and compared them to linear single-cell and network models. While the single-cell model explained some prominent features of the data, it could not capture the structure across stimuli and trials. In contrast, the network model accounted for the low-dimensional organization of population responses and their global structure across stimuli, where distinct stimuli activated mostly orthogonal dimensions in the neural state-space.
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Affiliation(s)
- Giulio Bondanelli
- Laboratoire de Neurosciences Cognitives et Computationelles, Département d’études cognitives, ENS, PSL University, INSERMParisFrance
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia (IIT)GenoaItaly
| | - Thomas Deneux
- Départment de Neurosciences Intégratives et Computationelles (ICN), Institut des Neurosciences Paris-Saclay (NeuroPSI), UMR 9197 CNRS, Université Paris SudGif-sur-YvetteFrance
| | - Brice Bathellier
- Départment de Neurosciences Intégratives et Computationelles (ICN), Institut des Neurosciences Paris-Saclay (NeuroPSI), UMR 9197 CNRS, Université Paris SudGif-sur-YvetteFrance
- Institut Pasteur, INSERM, Institut de l’AuditionParisFrance
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationelles, Département d’études cognitives, ENS, PSL University, INSERMParisFrance
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Bondanelli G, Ostojic S. Coding with transient trajectories in recurrent neural networks. PLoS Comput Biol 2020; 16:e1007655. [PMID: 32053594 PMCID: PMC7043794 DOI: 10.1371/journal.pcbi.1007655] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 02/26/2020] [Accepted: 01/14/2020] [Indexed: 01/04/2023] Open
Abstract
Following a stimulus, the neural response typically strongly varies in time and across neurons before settling to a steady-state. While classical population coding theory disregards the temporal dimension, recent works have argued that trajectories of transient activity can be particularly informative about stimulus identity and may form the basis of computations through dynamics. Yet the dynamical mechanisms needed to generate a population code based on transient trajectories have not been fully elucidated. Here we examine transient coding in a broad class of high-dimensional linear networks of recurrently connected units. We start by reviewing a well-known result that leads to a distinction between two classes of networks: networks in which all inputs lead to weak, decaying transients, and networks in which specific inputs elicit amplified transient responses and are mapped onto output states during the dynamics. Theses two classes are simply distinguished based on the spectrum of the symmetric part of the connectivity matrix. For the second class of networks, which is a sub-class of non-normal networks, we provide a procedure to identify transiently amplified inputs and the corresponding readouts. We first apply these results to standard randomly-connected and two-population networks. We then build minimal, low-rank networks that robustly implement trajectories mapping a specific input onto a specific orthogonal output state. Finally, we demonstrate that the capacity of the obtained networks increases proportionally with their size.
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Affiliation(s)
- Giulio Bondanelli
- Laboratoire de Neurosciences Cognitives et Computationelles, Département d’Études Cognitives, École Normale Supérieure, INSERM U960, PSL University, Paris, France
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationelles, Département d’Études Cognitives, École Normale Supérieure, INSERM U960, PSL University, Paris, France
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