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Li Y, Zhu X, Qi Y, Wang Y. Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals. eLife 2024; 12:RP87881. [PMID: 39120996 PMCID: PMC11315449 DOI: 10.7554/elife.87881] [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] [Indexed: 08/11/2024] Open
Abstract
In motor cortex, behaviorally relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally relevant and irrelevant signals at both single-neuron and single-trial levels, but this approach remains elusive due to the unknown ground truth of behaviorally relevant signals. Therefore, we propose a framework to define, extract, and validate behaviorally relevant signals. Analyzing separated signals in three monkeys performing different reaching tasks, we found neural responses previously considered to contain little information actually encode rich behavioral information in complex nonlinear ways. These responses are critical for neuronal redundancy and reveal movement behaviors occupy a higher-dimensional neural space than previously expected. Surprisingly, when incorporating often-ignored neural dimensions, behaviorally relevant signals can be decoded linearly with comparable performance to nonlinear decoding, suggesting linear readout may be performed in motor cortex. Our findings prompt that separating behaviorally relevant signals may help uncover more hidden cortical mechanisms.
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Affiliation(s)
- Yangang Li
- Qiushi Academy for Advanced Studies, Zhejiang UniversityHangzhouChina
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
| | - Xinyun Zhu
- Qiushi Academy for Advanced Studies, Zhejiang UniversityHangzhouChina
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
| | - Yu Qi
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
- Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital and the MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of MedicineHangzhouChina
| | - Yueming Wang
- Qiushi Academy for Advanced Studies, Zhejiang UniversityHangzhouChina
- Nanhu Brain-Computer Interface InstituteHangzhouChina
- College of Computer Science and Technology, Zhejiang UniversityHangzhouChina
- The State Key Lab of Brain-Machine Intelligence, Zhejiang UniversityHangzhouChina
- Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital and the MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of MedicineHangzhouChina
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Chang YJ, Chen YI, Yeh HC, Santacruz SR. Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics. Sci Rep 2024; 14:5145. [PMID: 38429297 PMCID: PMC10907713 DOI: 10.1038/s41598-024-54593-w] [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: 08/02/2023] [Accepted: 02/14/2024] [Indexed: 03/03/2024] Open
Abstract
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.
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Affiliation(s)
- Yin-Jui Chang
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Yuan-I Chen
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Hsin-Chih Yeh
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, USA
| | - Samantha R Santacruz
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX, USA.
- Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
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Zhao B, Wang R, Zhu Z, Yang Q, Chen A. The computational rules of cross-modality suppression in the visual posterior sylvian area. iScience 2023; 26:106973. [PMID: 37378331 PMCID: PMC10291470 DOI: 10.1016/j.isci.2023.106973] [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: 09/06/2022] [Revised: 03/13/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
The macaque visual posterior sylvian area (VPS) is an area with neurons responding selectively to heading direction in both visual and vestibular modalities, but how VPS neurons combined these two sensory signals is still unknown. In contrast to the subadditive characteristics in the medial superior temporal area (MSTd), responses in VPS were dominated by vestibular signals, with approximately a winner-take-all competition. The conditional Fisher information analysis shows that VPS neural population encodes information from distinct sensory modalities under large and small offset conditions, which differs from MSTd whose neural population contains more information about visual stimuli in both conditions. However, the combined responses of single neurons in both areas can be well fit by weighted linear sums of unimodal responses. Furthermore, a normalization model captured most vestibular and visual interaction characteristics for both VPS and MSTd, indicating the divisive normalization mechanism widely exists in the cortex.
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Affiliation(s)
- Bin Zhao
- Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai 200062, China
| | - Rong Wang
- Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai 200062, China
| | - Zhihua Zhu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qianli Yang
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Aihua Chen
- Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai 200062, China
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Kira S, Safaai H, Morcos AS, Panzeri S, Harvey CD. A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions. Nat Commun 2023; 14:2121. [PMID: 37055431 PMCID: PMC10102117 DOI: 10.1038/s41467-023-37804-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/30/2023] [Indexed: 04/15/2023] Open
Abstract
Decision-making requires flexibility to rapidly switch one's actions in response to sensory stimuli depending on information stored in memory. We identified cortical areas and neural activity patterns underlying this flexibility during virtual navigation, where mice switched navigation toward or away from a visual cue depending on its match to a remembered cue. Optogenetics screening identified V1, posterior parietal cortex (PPC), and retrosplenial cortex (RSC) as necessary for accurate decisions. Calcium imaging revealed neurons that can mediate rapid navigation switches by encoding a mixture of a current and remembered visual cue. These mixed selectivity neurons emerged through task learning and predicted the mouse's choices by forming efficient population codes before correct, but not incorrect, choices. They were distributed across posterior cortex, even V1, and were densest in RSC and sparsest in PPC. We propose flexibility in navigation decisions arises from neurons that mix visual and memory information within a visual-parietal-retrosplenial network.
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Affiliation(s)
- Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- 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
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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Chadwick A, Khan AG, Poort J, Blot A, Hofer SB, Mrsic-Flogel TD, Sahani M. Learning shapes cortical dynamics to enhance integration of relevant sensory input. Neuron 2023; 111:106-120.e10. [PMID: 36283408 PMCID: PMC7614688 DOI: 10.1016/j.neuron.2022.10.001] [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: 08/01/2021] [Revised: 07/14/2022] [Accepted: 09/30/2022] [Indexed: 11/05/2022]
Abstract
Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity among neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input.
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Affiliation(s)
- Angus Chadwick
- Gatsby Computational Neuroscience Unit, University College London, London, UK; Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK; Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Adil G Khan
- Centre for Developmental Neurobiology, King's College London, London, UK
| | - Jasper Poort
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Antonin Blot
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Sonja B Hofer
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Thomas D Mrsic-Flogel
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Maneesh Sahani
- Gatsby Computational Neuroscience Unit, University College London, London, UK.
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