1
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Shah S, Hembrook-Short J, Mock V, Briggs F. Correlated variability and its attentional modulation depend on anatomical connectivity. Proc Natl Acad Sci U S A 2024; 121:e2318841121. [PMID: 39172780 PMCID: PMC11363273 DOI: 10.1073/pnas.2318841121] [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: 10/27/2023] [Accepted: 07/23/2024] [Indexed: 08/24/2024] Open
Abstract
Visual cortical neurons show variability in their responses to repeated presentations of a stimulus and a portion of this variability is shared across neurons. Attention may enhance visual perception by reducing shared spiking variability. However, shared variability and its attentional modulation are not consistent within or across cortical areas, and depend on additional factors such as neuronal type. A critical factor that has not been tested is actual anatomical connectivity. We measured spike count correlations among pairs of simultaneously recorded neurons in the primary visual cortex (V1) for which anatomical connectivity was inferred from spiking cross-correlations. Neurons were recorded in monkeys performing a contrast-change discrimination task requiring covert shifts in visual spatial attention. Accordingly, spike count correlations were compared across trials in which attention was directed toward or away from the visual stimulus overlapping recorded neuronal receptive fields. Consistent with prior findings, attention did not significantly alter spike count correlations among random pairings of unconnected V1 neurons. However, V1 neurons connected via excitatory synapses showed a significant reduction in spike count correlations with attention. Interestingly, V1 neurons connected via inhibitory synapses demonstrated high spike count correlations overall that were not modulated by attention. Correlated variability in excitatory circuits also depended upon neuronal tuning for contrast, the task-relevant stimulus feature. These results indicate that shared variability depends on the type of connectivity in neuronal circuits. Also, attention significantly reduces shared variability in excitatory circuits, even when attention effects on randomly sampled neurons within the same area are weak.
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Affiliation(s)
- Shraddha Shah
- Neuroscience Graduate Program, University of Rochester, Rochester, NY14627
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX77030
| | | | - Vanessa Mock
- Department of Neuroscience, University of Rochester School of Medicine, Rochester, NY14642
| | - Farran Briggs
- Department of Neuroscience, University of Rochester School of Medicine, Rochester, NY14642
- Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine, Rochester, NY14642
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY14627
- Center for Visual Science, University of Rochester, Rochester, NY14627
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2
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Srinivasan K, Ribeiro TL, Kells P, Plenz D. The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality. Sci Rep 2024; 14:19329. [PMID: 39164334 PMCID: PMC11335857 DOI: 10.1038/s41598-024-70014-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 08/12/2024] [Indexed: 08/22/2024] Open
Abstract
Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches-scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common 'crackling noise' approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.
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Affiliation(s)
- Keshav Srinivasan
- Section on Critical Brain Dynamics, National Institute of Mental Health, Porter Neuroscience Research Center, Rm 3A-1000, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Tiago L Ribeiro
- Section on Critical Brain Dynamics, National Institute of Mental Health, Porter Neuroscience Research Center, Rm 3A-1000, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Patrick Kells
- Section on Critical Brain Dynamics, National Institute of Mental Health, Porter Neuroscience Research Center, Rm 3A-1000, 35 Convent Drive, Bethesda, MD, 20892, USA
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, National Institute of Mental Health, Porter Neuroscience Research Center, Rm 3A-1000, 35 Convent Drive, Bethesda, MD, 20892, USA.
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3
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Xia J, Jasper A, Kohn A, Miller KD. Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability. iScience 2024; 27:110512. [PMID: 39156642 PMCID: PMC11328009 DOI: 10.1016/j.isci.2024.110512] [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: 12/19/2023] [Revised: 04/01/2024] [Accepted: 07/12/2024] [Indexed: 08/20/2024] Open
Abstract
Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a multiplicative factor and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations. However, whether the affine model could be extended to explain modulations by other stimulus variables or variability shared between two brain areas is unknown. Motivated by a simple neural circuit mechanism, we modified the affine model to better explain the contrast dependence of neural variability shared within either primary or secondary visual cortex (V1 or V2) as well as the orientation dependence of neural variability shared between V1 and V2. Our results bridge neural circuit mechanisms and statistical models and provide a parsimonious explanation for the stimulus dependence of correlated variability within and between visual areas.
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Affiliation(s)
- Ji Xia
- Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Anna Jasper
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kenneth D. Miller
- Center for Theoretical Neuroscience and Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Neuroscience, Swartz Program in Theoretical Neuroscience, Kavli Institute for Brain Science, College of Physicians and Surgeons and Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York City, NY 10027, USA
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4
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Horrocks EAB, Rodrigues FR, Saleem AB. Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex. Nat Commun 2024; 15:6415. [PMID: 39080254 PMCID: PMC11289260 DOI: 10.1038/s41467-024-50563-y] [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: 12/11/2023] [Accepted: 07/15/2024] [Indexed: 08/02/2024] Open
Abstract
Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding.
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Affiliation(s)
- Edward A B Horrocks
- Institute of Behavioural Neuroscience, University College London, London, WC1V 0AP, UK.
| | - Fabio R Rodrigues
- Institute of Behavioural Neuroscience, University College London, London, WC1V 0AP, UK
| | - Aman B Saleem
- Institute of Behavioural Neuroscience, University College London, London, WC1V 0AP, UK.
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5
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Toth J, Sidleck B, Lombardi O, Hou T, Eldo A, Kerlin M, Zeng X, Saeed D, Agarwal P, Leonard D, Andrino L, Inbar T, Malina M, Insanally MN. Dynamic gating of perceptual flexibility by non-classically responsive cortical neurons. RESEARCH SQUARE 2024:rs.3.rs-4650869. [PMID: 39108496 PMCID: PMC11302693 DOI: 10.21203/rs.3.rs-4650869/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The ability to flexibly respond to sensory cues in dynamic environments is essential to adaptive auditory-guided behaviors. Cortical spiking responses during behavior are highly diverse, ranging from reliable trial-averaged responses to seemingly random firing patterns. While the reliable responses of 'classically responsive' cells have been extensively studied for decades, the contribution of irregular spiking 'non-classically responsive' cells to behavior has remained underexplored despite their prevalence. Here, we show that flexible auditory behavior results from interactions between local auditory cortical circuits comprised of heterogeneous responses and inputs from secondary motor cortex. Strikingly, non-classically responsive neurons in auditory cortex were preferentially recruited during learning, specifically during rapid learning phases when the greatest gains in behavioral performance occur. Population-level decoding revealed that during rapid learning mixed ensembles comprised of both classically and non-classically responsive cells encode significantly more task information than homogenous ensembles of either type and emerge as a functional unit critical for learning. Optogenetically silencing inputs from secondary motor cortex selectively modulated non-classically responsive cells in the auditory cortex and impaired reversal learning by preventing the remapping of a previously learned stimulus-reward association. Top-down inputs orchestrated highly correlated non-classically responsive ensembles in sensory cortex providing a unique task-relevant manifold for learning. Thus, non-classically responsive cells in sensory cortex are preferentially recruited by top-down inputs to enable neural and behavioral flexibility.
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Affiliation(s)
- Jade Toth
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Blake Sidleck
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Olivia Lombardi
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Tiange Hou
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Abraham Eldo
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Madelyn Kerlin
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Xiangjian Zeng
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Danyall Saeed
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Priya Agarwal
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Dylan Leonard
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
| | - Luz Andrino
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Tal Inbar
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Michael Malina
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Michele N. Insanally
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213
- Pittsburgh Hearing Research Center, University of Pittsburgh, Pittsburgh, PA 15213
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213
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6
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Tang D, Zylberberg J, Jia X, Choi H. Stimulus type shapes the topology of cellular functional networks in mouse visual cortex. Nat Commun 2024; 15:5753. [PMID: 38982078 PMCID: PMC11233648 DOI: 10.1038/s41467-024-49704-0] [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: 07/03/2023] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
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Affiliation(s)
- Disheng Tang
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
| | - Joel Zylberberg
- Department of Physics and Astronomy, and Centre for Vision Research, York University, Toronto, ON M3J 1P3, ON, Canada.
- Learning in Machines and Brains Program, CIFAR, Toronto, ON M5G 1M1, ON, Canada.
| | - Xiaoxuan Jia
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, PR China.
| | - Hannah Choi
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- School of Mathematics, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
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7
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Srinivasan K, Ribeiro TL, Kells P, Plenz D. The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582056. [PMID: 38464324 PMCID: PMC10925085 DOI: 10.1101/2024.02.26.582056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches-scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2 , reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2 , even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common 'crackling noise' approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.
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Affiliation(s)
- Keshav Srinivasan
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Tiago L. Ribeiro
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Patrick Kells
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD 20892, USA
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8
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Poley L, Galla T, Baron JW. Eigenvalue spectra of finely structured random matrices. Phys Rev E 2024; 109:064301. [PMID: 39020998 DOI: 10.1103/physreve.109.064301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 04/12/2024] [Indexed: 07/20/2024]
Abstract
Random matrix theory allows for the deduction of stability criteria for complex systems using only a summary knowledge of the statistics of the interactions between components. As such, results like the well-known elliptical law are applicable in a myriad of different contexts. However, it is often assumed that all components of the complex system in question are statistically equivalent, which is unrealistic in many applications. Here we introduce the concept of a finely structured random matrix. These are random matrices with element-specific statistics, which can be used to model systems in which the individual components are statistically distinct. By supposing that the degree of "fine structure" in the matrix is small, we arrive at a succinct "modified" elliptical law. We demonstrate the direct applicability of our results to the niche and cascade models in theoretical ecology, as well as a model of a neural network, and a directed network with arbitrary degree distribution. The simple closed form of our central results allow us to draw broad qualitative conclusions about the effect of fine structure on stability.
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9
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Bae AJ, Ferger R, Peña JL. Auditory Competition and Coding of Relative Stimulus Strength across Midbrain Space Maps of Barn Owls. J Neurosci 2024; 44:e2081232024. [PMID: 38664010 PMCID: PMC11112643 DOI: 10.1523/jneurosci.2081-23.2024] [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: 11/06/2023] [Revised: 03/06/2024] [Accepted: 04/05/2024] [Indexed: 05/24/2024] Open
Abstract
The natural environment challenges the brain to prioritize the processing of salient stimuli. The barn owl, a sound localization specialist, exhibits a circuit called the midbrain stimulus selection network, dedicated to representing locations of the most salient stimulus in circumstances of concurrent stimuli. Previous competition studies using unimodal (visual) and bimodal (visual and auditory) stimuli have shown that relative strength is encoded in spike response rates. However, open questions remain concerning auditory-auditory competition on coding. To this end, we present diverse auditory competitors (concurrent flat noise and amplitude-modulated noise) and record neural responses of awake barn owls of both sexes in subsequent midbrain space maps, the external nucleus of the inferior colliculus (ICx) and optic tectum (OT). While both ICx and OT exhibit a topographic map of auditory space, OT also integrates visual input and is part of the global-inhibitory midbrain stimulus selection network. Through comparative investigation of these regions, we show that while increasing strength of a competitor sound decreases spike response rates of spatially distant neurons in both regions, relative strength determines spike train synchrony of nearby units only in the OT. Furthermore, changes in synchrony by sound competition in the OT are correlated to gamma range oscillations of local field potentials associated with input from the midbrain stimulus selection network. The results of this investigation suggest that modulations in spiking synchrony between units by gamma oscillations are an emergent coding scheme representing relative strength of concurrent stimuli, which may have relevant implications for downstream readout.
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Affiliation(s)
- Andrea J Bae
- Dominick P Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461
| | - Roland Ferger
- Dominick P Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461
| | - José L Peña
- Dominick P Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York 10461
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10
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Maddaluno O, Della Penna S, Pizzuti A, Spezialetti M, Corbetta M, de Pasquale F, Betti V. Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity. J Neurosci 2024; 44:e1766232024. [PMID: 38538141 PMCID: PMC11097277 DOI: 10.1523/jneurosci.1766-23.2024] [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: 09/18/2023] [Revised: 01/21/2024] [Accepted: 02/20/2024] [Indexed: 05/18/2024] Open
Abstract
The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.
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Affiliation(s)
- Ottavia Maddaluno
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences and ITAB - Institute of Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti and Pescara, Chieti 66013, Italy
| | - Alessandra Pizzuti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Matteo Spezialetti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua 35131, Italy
- Veneto Institute of Molecular Medicine (VIMM), Padova 35129, Italy
| | | | - Viviana Betti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
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11
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Wei 魏赣超 G, Tajik Mansouri زینب تاجیک منصوری Z, Wang 王晓婧 X, Stevenson IH. Calibrating Bayesian Decoders of Neural Spiking Activity. J Neurosci 2024; 44:e2158232024. [PMID: 38538143 PMCID: PMC11063820 DOI: 10.1523/jneurosci.2158-23.2024] [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: 11/17/2023] [Revised: 01/29/2024] [Accepted: 03/11/2024] [Indexed: 05/03/2024] Open
Abstract
Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion recordings in mice, and (4) decoding position from hippocampal recordings in rats. For each setting, we characterize the overconfidence, and we describe a possible method to correct miscalibration post hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain-machine interfaces that more accurately reflect confidence levels when identifying external variables.
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Affiliation(s)
- Ganchao Wei 魏赣超
- Department of Statistical Science, Duke University, Durham, North Carolina 27708
| | | | | | - Ian H Stevenson
- Departments of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269
- Psychological Sciences, University of Connecticut, Storrs, Connecticut 06269
- Connecticut Institute for Brain and Cognitive Science, University of Connecticut, Storrs, Connecticut 06269
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12
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Fortunato C, Bennasar-Vázquez J, Park J, Chang JC, Miller LE, Dudman JT, Perich MG, Gallego JA. Nonlinear manifolds underlie neural population activity during behaviour. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.18.549575. [PMID: 37503015 PMCID: PMC10370078 DOI: 10.1101/2023.07.18.549575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey, mouse, and human motor cortex, and mouse striatum, we show that: 1) neural manifolds are intrinsically nonlinear; 2) their nonlinearity becomes more evident during complex tasks that require more varied activity patterns; and 3) manifold nonlinearity varies across architecturally distinct brain regions. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.
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Affiliation(s)
- Cátia Fortunato
- Department of Bioengineering, Imperial College London, London UK
| | | | - Junchol Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Joanna C. Chang
- Department of Bioengineering, Imperial College London, London UK
| | - Lee E. Miller
- Department of Neurosciences, Northwestern University, Chicago IL, USA
- Department of Biomedical Engineering, Northwestern University, Chicago IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago IL, USA, and Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Joshua T. Dudman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn VA, USA
| | - Matthew G. Perich
- Department of Neurosciences, Faculté de médecine, Université de Montréal, Montréal, Québec, Canada
- Québec Artificial Intelligence Institute (MILA), Montréal, Québec, Canada
| | - Juan A. Gallego
- Department of Bioengineering, Imperial College London, London UK
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13
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Zhu S, Oh YJ, Trepka E, Chen X, Moore T. Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590176. [PMID: 38659877 PMCID: PMC11042257 DOI: 10.1101/2024.04.18.590176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In visual cortex, neural correlates of subjective perception can be generated by modulation of activity from beyond the classical receptive field (CRF). In macaque V1, activity generated by nonclassical receptive field (nCRF) stimulation involves different intracortical circuitry than activity generated by CRF stimulation, suggesting that interactions between neurons across V1 layers differ under CRF and nCRF stimulus conditions. We measured border ownership modulation within large populations of V1 neurons. We found that neurons in single columns preferred the same side of objects located outside of the CRF. In addition, we found that interactions between pairs of neurons situated across feedback/horizontal and input layers differed between CRF and nCRF stimulation. Furthermore, the magnitude of border ownership modulation was predicted by greater information flow from feedback/horizontal to input layers. These results demonstrate that the flow of signals between layers covaries with the degree to which neurons integrate information from beyond the CRF.
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14
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Pan X, Coen-Cagli R, Schwartz O. Probing the Structure and Functional Properties of the Dropout-Induced Correlated Variability in Convolutional Neural Networks. Neural Comput 2024; 36:621-644. [PMID: 38457752 PMCID: PMC11164410 DOI: 10.1162/neco_a_01652] [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: 05/16/2023] [Accepted: 12/04/2023] [Indexed: 03/10/2024]
Abstract
Computational neuroscience studies have shown that the structure of neural variability to an unchanged stimulus affects the amount of information encoded. Some artificial deep neural networks, such as those with Monte Carlo dropout layers, also have variable responses when the input is fixed. However, the structure of the trial-by-trial neural covariance in neural networks with dropout has not been studied, and its role in decoding accuracy is unknown. We studied the above questions in a convolutional neural network model with dropout in both the training and testing phases. We found that trial-by-trial correlation between neurons (i.e., noise correlation) is positive and low dimensional. Neurons that are close in a feature map have larger noise correlation. These properties are surprisingly similar to the findings in the visual cortex. We further analyzed the alignment of the main axes of the covariance matrix. We found that different images share a common trial-by-trial noise covariance subspace, and they are aligned with the global signal covariance. This evidence that the noise covariance is aligned with signal covariance suggests that noise covariance in dropout neural networks reduces network accuracy, which we further verified directly with a trial-shuffling procedure commonly used in neuroscience. These findings highlight a previously overlooked aspect of dropout layers that can affect network performance. Such dropout networks could also potentially be a computational model of neural variability.
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Affiliation(s)
- Xu Pan
- Department of Computer Science, University of Miami, Coral Gables, FL 33146, U.S.A.
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Dominick Purpura Department of Neuroscience, and Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY 10461, U.S.A.
| | - Odelia Schwartz
- Department of Computer Science, University of Miami, Coral Gables, FL 33146, U.S.A.
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15
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Contemori G, Maniglia M, Guénot J, Soler V, Cherubini M, Cottereau BR, Trotter Y. tRNS boosts visual perceptual learning in participants with bilateral macular degeneration. Front Aging Neurosci 2024; 16:1326435. [PMID: 38450381 PMCID: PMC10914974 DOI: 10.3389/fnagi.2024.1326435] [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: 10/23/2023] [Accepted: 02/02/2024] [Indexed: 03/08/2024] Open
Abstract
Perceptual learning (PL) has shown promise in enhancing residual visual functions in patients with age-related macular degeneration (MD), however it requires prolonged training and evidence of generalization to untrained visual functions is limited. Recent studies suggest that combining transcranial random noise stimulation (tRNS) with perceptual learning produces faster and larger visual improvements in participants with normal vision. Thus, this approach might hold the key to improve PL effects in MD. To test this, we trained two groups of MD participants on a contrast detection task with (n = 5) or without (n = 7) concomitant occipital tRNS. The training consisted of a lateral masking paradigm in which the participant had to detect a central low contrast Gabor target. Transfer tasks, including contrast sensitivity, near and far visual acuity, and visual crowding, were measured at pre-, mid and post-tests. Combining tRNS and perceptual learning led to greater improvements in the trained task, evidenced by a larger increment in contrast sensitivity and reduced inhibition at the shortest target to flankers' distance. The overall amount of transfer was similar between the two groups. These results suggest that coupling tRNS and perceptual learning has promising potential applications as a clinical rehabilitation strategy to improve vision in MD patients.
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Affiliation(s)
- Giulio Contemori
- Department of General Psychology, University of Padova, Padua, Italy
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Toulouse, France
| | - Marcello Maniglia
- Department of Psychology, University of California, Riverside, Riverside, CA, United States
| | - Jade Guénot
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Toulouse, France
- Centre National de la Recherche Scientifique, Toulouse, France
| | - Vincent Soler
- Service d’Ophtalmologie Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | - Marta Cherubini
- Centre National de la Recherche Scientifique, Toulouse, France
- Department of Psychology and Cognitive Science, University of Trento, Rovereto, Italy
| | - Benoit R. Cottereau
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Toulouse, France
- Centre National de la Recherche Scientifique, Toulouse, France
| | - Yves Trotter
- Centre de Recherche Cerveau et Cognition, Université de Toulouse, Toulouse, France
- Centre National de la Recherche Scientifique, Toulouse, France
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16
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Davis ZW, Busch A, Stewerd C, Muller L, Reynolds J. Horizontal cortical connections shape intrinsic traveling waves into feature-selective motifs that regulate perceptual sensitivity. RESEARCH SQUARE 2024:rs.3.rs-3830199. [PMID: 38260448 PMCID: PMC10802692 DOI: 10.21203/rs.3.rs-3830199/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Intrinsic, ongoing fluctuations of cortical activity form traveling waves that modulate the gain of sensory-evoked responses and perceptual sensitivity. Several lines of evidence suggest that intrinsic traveling waves (iTWs) may arise, in part, from the coordination of synaptic activity through the recurrent horizontal connectivity within cortical areas, which include long range patchy connections that link neurons with shared feature preferences. In a spiking network model with anatomical topology that incorporates feature-selective patchy connections, which we call the Balanced Patchy Network (BPN), we observe repeated iTWs, which we refer to as motifs. In the model, motifs stem from fluctuations in the excitability of like-tuned neurons that result from shifts in E/I balance as action potentials traverse these patchy connections. To test if feature-selective motifs occur in vivo, we examined data previously recorded using multielectrode arrays in Area MT of marmosets trained to perform a threshold visual detection task. Using a newly developed method for comparing the similarity of wave patterns we found that some iTWs can be grouped into motifs. As predicted by the BPN, many of these motifs are feature selective, exhibiting direction-selective modulations in ongoing spiking activity. Further, motifs modulate the gain of the response evoked by a target and perceptual sensitivity to the target if the target matches the preference of the motif. These results provide evidence that iTWs are shaped by the patterns of horizontal fiber projections in the cortex and that patchy connections enable iTWs to regulate neural and perceptual sensitivity in a feature selective manner.
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Affiliation(s)
- Zachary W Davis
- The Salk Institute for Biological Studies, La Jolla, CA, USA. 92037
- Department of Ophthalmology and Visual Science, University of Utah, SLC, UT, USA 84112
| | - Alexandria Busch
- Department of Applied Mathematics, Western University, London, ON, Canada. N6A 3K7
- Brain and Mind Institute, Western University, London, ON, Canada. N6A 3K7
| | - Christopher Stewerd
- Department of Applied Mathematics, Western University, London, ON, Canada. N6A 3K7
- Brain and Mind Institute, Western University, London, ON, Canada. N6A 3K7
| | - Lyle Muller
- Department of Applied Mathematics, Western University, London, ON, Canada. N6A 3K7
- Brain and Mind Institute, Western University, London, ON, Canada. N6A 3K7
| | - John Reynolds
- The Salk Institute for Biological Studies, La Jolla, CA, USA. 92037
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17
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Becker LA, Li B, Priebe NJ, Seidemann E, Taillefumier T. Exact Analysis of the Subthreshold Variability for Conductance-Based Neuronal Models with Synchronous Synaptic Inputs. PHYSICAL REVIEW. X 2024; 14:011021. [PMID: 38911939 PMCID: PMC11194039 DOI: 10.1103/physrevx.14.011021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state, neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically, we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects postspiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime yields realistic subthreshold variability (voltage variance ≃4-9 mV2) only when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations. We also show that, without synchrony, the neural variability averages out to zero for all scaling limits with vanishing synaptic weights, independent of any balanced state hypothesis. This result challenges the theoretical basis for mean-field theories of the asynchronous state.
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Affiliation(s)
- Logan A. Becker
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Baowang Li
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Learning and Memory, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Psychology, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Nicholas J. Priebe
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Learning and Memory, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Eyal Seidemann
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Center for Perceptual Systems, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Psychology, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Thibaud Taillefumier
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712, USA
- Department of Mathematics, The University of Texas at Austin, Austin, Texas 78712, USA
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18
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Cowley BR, Stan PL, Pillow JW, Smith MA. Compact deep neural network models of visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.22.568315. [PMID: 38045255 PMCID: PMC10690296 DOI: 10.1101/2023.11.22.568315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
A powerful approach to understanding the computations carried out in visual cortex is to develop models that predict neural responses to arbitrary images. Deep neural network (DNN) models have worked remarkably well at predicting neural responses [1, 2, 3], yet their underlying computations remain buried in millions of parameters. Have we simply replaced one complicated system in vivo with another in silico ? Here, we train a data-driven deep ensemble model that predicts macaque V4 responses ∼50% more accurately than currently-used task-driven DNN models. We then compress this deep ensemble to identify compact models that have 5,000x fewer parameters yet equivalent accuracy as the deep ensemble. We verified that the stimulus preferences of the compact models matched those of the real V4 neurons by measuring V4 responses to both 'maximizing' and adversarial images generated using compact models. We then analyzed the inner workings of the compact models and discovered a common circuit motif: Compact models share a similar set of filters in early stages of processing but then specialize by heavily consolidating this shared representation with a precise readout. This suggests that a V4 neuron's stimulus preference is determined entirely by its consolidation step. To demonstrate this, we investigated the compression step of a dot-detecting compact model and found a set of simple computations that may be carried out by dot-selective V4 neurons. Overall, our work demonstrates that the DNN models currently used in computational neuroscience are needlessly large; our approach provides a new way forward for obtaining explainable, high-accuracy models of visual cortical neurons.
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19
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Munn BR, Müller EJ, Aru J, Whyte CJ, Gidon A, Larkum ME, Shine JM. A thalamocortical substrate for integrated information via critical synchronous bursting. Proc Natl Acad Sci U S A 2023; 120:e2308670120. [PMID: 37939085 PMCID: PMC10655573 DOI: 10.1073/pnas.2308670120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/21/2023] [Indexed: 11/10/2023] Open
Abstract
Understanding the neurobiological mechanisms underlying consciousness remains a significant challenge. Recent evidence suggests that the coupling between distal-apical and basal-somatic dendrites in thick-tufted layer 5 pyramidal neurons (L5PN), regulated by the nonspecific-projecting thalamus, is crucial for consciousness. Yet, it is uncertain whether this thalamocortical mechanism can support emergent signatures of consciousness, such as integrated information. To address this question, we constructed a biophysical network of dual-compartment thick-tufted L5PN, with dendrosomatic coupling controlled by thalamic inputs. Our findings demonstrate that integrated information is maximized when nonspecific thalamic inputs drive the system into a regime of time-varying synchronous bursting. Here, the system exhibits variable spiking dynamics with broad pairwise correlations, supporting the enhanced integrated information. Further, the observed peak in integrated information aligns with criticality signatures and empirically observed layer 5 pyramidal bursting rates. These results suggest that the thalamocortical core of the mammalian brain may be evolutionarily configured to optimize effective information processing, providing a potential neuronal mechanism that integrates microscale theories with macroscale signatures of consciousness.
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Affiliation(s)
- Brandon R. Munn
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney2050, Australia
- Complex Systems, School of Physics, Faculty of Science, University of Sydney, Sydney2050, Australia
| | - Eli J. Müller
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney2050, Australia
- Complex Systems, School of Physics, Faculty of Science, University of Sydney, Sydney2050, Australia
| | - Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu51009, Estonia
| | - Christopher J. Whyte
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney2050, Australia
- Complex Systems, School of Physics, Faculty of Science, University of Sydney, Sydney2050, Australia
| | - Albert Gidon
- Institute of Biology, Humboldt University of Berlin, Berlin10099, Germany
- NeuroCure Center of Excellence, Charité Universitätsmedizin Berlin, Berlin10099, Germany
| | - Matthew E. Larkum
- Institute of Biology, Humboldt University of Berlin, Berlin10099, Germany
- NeuroCure Center of Excellence, Charité Universitätsmedizin Berlin, Berlin10099, Germany
| | - James M. Shine
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney2050, Australia
- Complex Systems, School of Physics, Faculty of Science, University of Sydney, Sydney2050, Australia
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20
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Andrei AR, Akil AE, Kharas N, Rosenbaum R, Josić K, Dragoi V. Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo. Nat Neurosci 2023; 26:1960-1969. [PMID: 37828225 DOI: 10.1038/s41593-023-01446-w] [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: 03/10/2022] [Accepted: 09/01/2023] [Indexed: 10/14/2023]
Abstract
To produce adaptive behavior, neural networks must balance between plasticity and stability. Computational work has demonstrated that network stability requires plasticity mechanisms to be counterbalanced by rapid compensatory processes. However, such processes have yet to be experimentally observed. Here we demonstrate that repeated optogenetic activation of excitatory neurons in monkey visual cortex (area V1) induces a population-wide dynamic reduction in the strength of neuronal interactions over the timescale of minutes during the awake state, but not during rest. This new form of rapid plasticity was observed only in the correlation structure, with firing rates remaining stable across trials. A computational network model operating in the balanced regime confirmed experimental findings and revealed that inhibitory plasticity is responsible for the decrease in correlated activity in response to repeated light stimulation. These results provide the first experimental evidence for rapid homeostatic plasticity that primarily operates during wakefulness, which stabilizes neuronal interactions during strong network co-activation.
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Affiliation(s)
- Ariana R Andrei
- Department of Neurobiology and Anatomy, University of Texas, Houston, TX, USA.
| | - Alan E Akil
- Departments of Mathematics, Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Natasha Kharas
- Department of Neurobiology and Anatomy, University of Texas, Houston, TX, USA
| | - Robert Rosenbaum
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Krešimir Josić
- Departments of Mathematics, Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy, University of Texas, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
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21
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Tian Y, Yin J, Wang C, He Z, Xie J, Feng X, Zhou Y, Ma T, Xie Y, Li X, Yang T, Ren C, Li C, Zhao Z. An Ultraflexible Electrode Array for Large-Scale Chronic Recording in the Nonhuman Primate Brain. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302333. [PMID: 37870175 PMCID: PMC10667845 DOI: 10.1002/advs.202302333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 09/08/2023] [Indexed: 10/24/2023]
Abstract
Single-unit (SU) recording in nonhuman primates (NHPs) is indispensible in the quest of how the brain works, yet electrodes currently used for the NHP brain are limited in signal longevity, stability, and spatial coverage. Using new structural materials, microfabrication, and penetration techniques, we develop a mechanically robust ultraflexible, 1 µm thin electrode array (MERF) that enables pial penetration and high-density, large-scale, and chronic recording of neurons along both vertical and horizontal cortical axes in the nonhuman primate brain. Recording from three monkeys yields 2,913 SUs from 1,065 functional recording channels (up to 240 days), with some SUs tracked for up to 2 months. Recording from the primary visual cortex (V1) reveals that neurons with similar orientation preferences for visual stimuli exhibited higher spike correlation. Furthermore, simultaneously recorded neurons in different cortical layers of the primary motor cortex (M1) show preferential firing for hand movements of different directions. Finally, it is shown that a linear decoder trained with neuronal spiking activity across M1 layers during monkey's hand movements can be used to achieve on-line control of cursor movement. Thus, the MERF electrode array offers a new tool for basic neuroscience studies and brain-machine interface (BMI) applications in the primate brain.
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Affiliation(s)
- Yixin Tian
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Jiapeng Yin
- Shanghai Center for Brain Science and Brain‐Inspired TechnologyShanghai201602China
- Lingang LaboratoryShanghai200031China
| | - Chengyao Wang
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Zhenliang He
- Lingang LaboratoryShanghai200031China
- Institute of NeuroscienceState Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Jingyi Xie
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
| | - Xiaoshan Feng
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Yang Zhou
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Tianyu Ma
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yang Xie
- Lingang LaboratoryShanghai200031China
- Institute of NeuroscienceKey Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Xue Li
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Tianming Yang
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
| | - Chi Ren
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Chengyu Li
- Lingang LaboratoryShanghai200031China
- Institute of NeuroscienceState Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
| | - Zhengtuo Zhao
- Institute of NeuroscienceCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
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22
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Banaie Boroujeni K, Womelsdorf T. Routing states transition during oscillatory bursts and attentional selection. Neuron 2023; 111:2929-2944.e11. [PMID: 37463578 PMCID: PMC10529654 DOI: 10.1016/j.neuron.2023.06.012] [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: 03/26/2023] [Revised: 05/22/2023] [Accepted: 06/20/2023] [Indexed: 07/20/2023]
Abstract
Brain-wide information routing relies on the spatio-temporal dynamics of neural activity, but it remains unclear how routing states emerge at fast spiking timescales and relate to slower activity dynamics during cognitive processes. Here, we show that localized spiking events participate in directional routing states with spiking activity in distant brain areas that dynamically switch or amplify states during oscillatory bursts, attentional selection, and decision-making. Modeling and neural recordings from lateral prefrontal cortex (LPFC), anterior cingulate cortex (ACC), and striatum of nonhuman primates revealed that cross-regional routing states arise within 20 ms following individual neuron spikes, with LPFC spikes leading the activity in ACC and striatum. The baseline routing state amplified during LPFC beta bursts between LPFC and striatum and switched direction during ACC theta/alpha bursts between ACC and LPFC. Selective attention amplified theta-/alpha-band-specific lead ensembles in ACC, while decision-making increased the lead of ACC and LPFC spikes to the striatum.
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Affiliation(s)
- Kianoush Banaie Boroujeni
- Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA.
| | - Thilo Womelsdorf
- Department of Psychology, Vanderbilt University, Nashville, TN 37240, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37240, USA
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23
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Gehr C, Sibille J, Kremkow J. Retinal input integration in excitatory and inhibitory neurons in the mouse superior colliculus in vivo. eLife 2023; 12:RP88289. [PMID: 37682267 PMCID: PMC10491433 DOI: 10.7554/elife.88289] [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: 09/09/2023] Open
Abstract
The superior colliculus (SC) is a midbrain structure that receives inputs from retinal ganglion cells (RGCs). The SC contains one of the highest densities of inhibitory neurons in the brain but whether excitatory and inhibitory SC neurons differentially integrate retinal activity in vivo is still largely unknown. We recently established a recording approach to measure the activity of RGCs simultaneously with their postsynaptic SC targets in vivo, to study how SC neurons integrate RGC activity. Here, we employ this method to investigate the functional properties that govern retinocollicular signaling in a cell type-specific manner by identifying GABAergic SC neurons using optotagging in VGAT-ChR2 mice. Our results demonstrate that both excitatory and inhibitory SC neurons receive comparably strong RGC inputs and similar wiring rules apply for RGCs innervation of both SC cell types, unlike the cell type-specific connectivity in the thalamocortical system. Moreover, retinal activity contributed more to the spiking activity of postsynaptic excitatory compared to inhibitory SC neurons. This study deepens our understanding of cell type-specific retinocollicular functional connectivity and emphasizes that the two major brain areas for visual processing, the visual cortex and the SC, differently integrate sensory afferent inputs.
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Affiliation(s)
- Carolin Gehr
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
| | - Jeremie Sibille
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
| | - Jens Kremkow
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
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24
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Kass RE, Bong H, Olarinre M, Xin Q, Urban KN. Identification of interacting neural populations: methods and statistical considerations. J Neurophysiol 2023; 130:475-496. [PMID: 37465897 PMCID: PMC10642974 DOI: 10.1152/jn.00131.2023] [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: 03/29/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 07/20/2023] Open
Abstract
As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods can achieve and the circumstances under which they are likely to succeed. Toward this end, we include a discussion of four cross-cutting issues: the definition of neural populations, trial-to-trial variability and Poisson-like noise, time-varying dynamics, and causality.
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Affiliation(s)
- Robert E Kass
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Heejong Bong
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Motolani Olarinre
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Qi Xin
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Konrad N Urban
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
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25
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Rowland JM, van der Plas TL, Loidolt M, Lees RM, Keeling J, Dehning J, Akam T, Priesemann V, Packer AM. Propagation of activity through the cortical hierarchy and perception are determined by neural variability. Nat Neurosci 2023; 26:1584-1594. [PMID: 37640911 PMCID: PMC10471496 DOI: 10.1038/s41593-023-01413-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/18/2023] [Indexed: 08/31/2023]
Abstract
Brains are composed of anatomically and functionally distinct regions performing specialized tasks, but regions do not operate in isolation. Orchestration of complex behaviors requires communication between brain regions, but how neural dynamics are organized to facilitate reliable transmission is not well understood. Here we studied this process directly by generating neural activity that propagates between brain regions and drives behavior, assessing how neural populations in sensory cortex cooperate to transmit information. We achieved this by imaging two densely interconnected regions-the primary and secondary somatosensory cortex (S1 and S2)-in mice while performing two-photon photostimulation of S1 neurons and assigning behavioral salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation but also by the variability of S1 neural activity. Therefore, maximizing the signal-to-noise ratio of the stimulus representation in cortex relative to the noise or variability is critical to facilitate activity propagation and perception.
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Affiliation(s)
- James M Rowland
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Thijs L van der Plas
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Matthias Loidolt
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Laboratory for Molecular Cell Biology, University College London, London, UK
| | - Robert M Lees
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
- Science and Technology Facilities Council, Octopus Imaging Facility, Research Complex at Harwell, Harwell Campus, Oxfordshire, UK
| | - Joshua Keeling
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Jonas Dehning
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Thomas Akam
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Adam M Packer
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK.
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26
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Herzfeld DJ, Joshua M, Lisberger SG. Rate versus synchrony codes for cerebellar control of motor behavior. Neuron 2023; 111:2448-2460.e6. [PMID: 37536289 PMCID: PMC10424531 DOI: 10.1016/j.neuron.2023.07.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/24/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023]
Abstract
Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the code for information transmission from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellum of rhesus macaques reveal how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and insufficient to impact the firing of downstream vestibular nucleus neurons. Analysis of previous metrics that purported to reveal Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate rather than a synchrony code to drive the activity of downstream neurons and thus control motor behavior.
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Affiliation(s)
- David J Herzfeld
- Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710, USA.
| | - Mati Joshua
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Stephen G Lisberger
- Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710, USA
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27
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Sachdeva P, Bak JH, Livezey J, Kirst C, Frank L, Bhattacharyya S, Bouchard KE. Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.11.548615. [PMID: 37503030 PMCID: PMC10370036 DOI: 10.1101/2023.07.11.548615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets.
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Affiliation(s)
- Pratik Sachdeva
- Physics Department, UC Berkeley
- Redwood Center for Theoretical Neuroscience, UC Berkeley
| | - Ji Hyun Bak
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab
| | - Jesse Livezey
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab
| | - Christoph Kirst
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Scientific Data Division, Lawrence Berkeley National Lab
- Deptartment of Anatomy, UC San Francisco
| | - Loren Frank
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Departments of Physiology and Psychiatry, UC San Francisco
- Howard Hughes Medical Institute
| | | | - Kristofer E. Bouchard
- Redwood Center for Theoretical Neuroscience, UC Berkeley
- Kavli Institute for Fundamental Neuroscience, UC San Francisco
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab
- Scientific Data Division, Lawrence Berkeley National Lab
- Helen Wills Neuroscience Institute, UC Berkeley
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28
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Tang D, Zylberberg J, Jia X, Choi H. Stimulus-dependent functional network topology in mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.03.547364. [PMID: 37461471 PMCID: PMC10349950 DOI: 10.1101/2023.07.03.547364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
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Affiliation(s)
- Disheng Tang
- School of Life Sciences, Tsinghua University
- Quantitative Biosciences Program, Georgia Institute of Technology
- IDG/McGovern Institute for Brain Research, Tsinghua University
| | - Joel Zylberberg
- Department of Physics and Astronomy, and Centre for Vision Research, York University
- Learning in Machines and Brains Program, CIFAR
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
| | - Xiaoxuan Jia
- School of Life Sciences, Tsinghua University
- IDG/McGovern Institute for Brain Research, Tsinghua University
- Tsinghua–Peking Center for Life Sciences
- Allen Institute for Brain Science
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
| | - Hannah Choi
- Quantitative Biosciences Program, Georgia Institute of Technology
- School of Mathematics, Georgia Institute of Technology
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
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29
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Katz LN, Yu G, Herman JP, Krauzlis RJ. Correlated variability in primate superior colliculus depends on functional class. Commun Biol 2023; 6:540. [PMID: 37202508 DOI: 10.1038/s42003-023-04912-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 05/04/2023] [Indexed: 05/20/2023] Open
Abstract
Correlated variability in neuronal activity (spike count correlations, rSC) can constrain how information is read out from populations of neurons. Traditionally, rSC is reported as a single value summarizing a brain area. However, single values, like summary statistics, stand to obscure underlying features of the constituent elements. We predict that in brain areas containing distinct neuronal subpopulations, different subpopulations will exhibit distinct levels of rSC that are not captured by the population rSC. We tested this idea in macaque superior colliculus (SC), a structure containing several functional classes (i.e., subpopulations) of neurons. We found that during saccade tasks, different functional classes exhibited differing degrees of rSC. "Delay class" neurons displayed the highest rSC, especially during saccades that relied on working memory. Such dependence of rSC on functional class and cognitive demand underscores the importance of taking functional subpopulations into account when attempting to model or infer population coding principles.
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Affiliation(s)
- Leor N Katz
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA.
| | - Gongchen Yu
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
| | - James P Herman
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, 15219, USA
| | - Richard J Krauzlis
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
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30
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Zhu RJB, Wei XX. Unsupervised approach to decomposing neural tuning variability. Nat Commun 2023; 14:2298. [PMID: 37085524 PMCID: PMC10121715 DOI: 10.1038/s41467-023-37982-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/07/2023] [Indexed: 04/23/2023] Open
Abstract
Neural representation is often described by the tuning curves of individual neurons with respect to certain stimulus variables. Despite this tradition, it has become increasingly clear that neural tuning can vary substantially in accordance with a collection of internal and external factors. A challenge we are facing is the lack of appropriate methods to accurately capture the moment-to-moment tuning variability directly from the noisy neural responses. Here we introduce an unsupervised statistical approach, Poisson functional principal component analysis (Pf-PCA), which identifies different sources of systematic tuning fluctuations, moreover encompassing several current models (e.g.,multiplicative gain models) as special cases. Applying this method to neural data recorded from macaque primary visual cortex- a paradigmatic case for which the tuning curve approach has been scientifically essential- we discovered a simple relationship governing the variability of orientation tuning, which unifies different types of gain changes proposed previously. By decomposing the neural tuning variability into interpretable components, our method enables discovery of unexpected structure of the neural code, capturing the influence of the external stimulus drive and internal states simultaneously.
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Affiliation(s)
- Rong J B Zhu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Shanghai, China.
| | - Xue-Xin Wei
- Department of Neuroscience, The University of Texas at Austin, Austin, USA.
- Department of Psychology, The University of Texas at Austin, Austin, USA.
- Center for Perceptual Systems, The University of Texas at Austin, Austin, USA.
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin, Austin, USA.
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31
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Zylbertal A, Bianco IH. Recurrent network interactions explain tectal response variability and experience-dependent behavior. eLife 2023; 12:78381. [PMID: 36943029 PMCID: PMC10030118 DOI: 10.7554/elife.78381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 03/09/2023] [Indexed: 03/23/2023] Open
Abstract
Response variability is an essential and universal feature of sensory processing and behavior. It arises from fluctuations in the internal state of the brain, which modulate how sensory information is represented and transformed to guide behavioral actions. In part, brain state is shaped by recent network activity, fed back through recurrent connections to modulate neuronal excitability. However, the degree to which these interactions influence response variability and the spatial and temporal scales across which they operate, are poorly understood. Here, we combined population recordings and modeling to gain insights into how neuronal activity modulates network state and thereby impacts visually evoked activity and behavior. First, we performed cellular-resolution calcium imaging of the optic tectum to monitor ongoing activity, the pattern of which is both a cause and consequence of changes in network state. We developed a minimal network model incorporating fast, short range, recurrent excitation and long-lasting, activity-dependent suppression that reproduced a hallmark property of tectal activity - intermittent bursting. We next used the model to estimate the excitability state of tectal neurons based on recent activity history and found that this explained a portion of the trial-to-trial variability in visually evoked responses, as well as spatially selective response adaptation. Moreover, these dynamics also predicted behavioral trends such as selective habituation of visually evoked prey-catching. Overall, we demonstrate that a simple recurrent interaction motif can be used to estimate the effect of activity upon the incidental state of a neural network and account for experience-dependent effects on sensory encoding and visually guided behavior.
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Affiliation(s)
- Asaph Zylbertal
- Department of Neuroscience, Physiology & Pharmacology, University College London, London, United Kingdom
| | - Isaac H Bianco
- Department of Neuroscience, Physiology & Pharmacology, University College London, London, United Kingdom
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32
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Sachse EM, Snyder AC. Dynamic attention signalling in V4: Relation to fast-spiking/non-fast-spiking cell class and population coupling. Eur J Neurosci 2023; 57:918-939. [PMID: 36732934 DOI: 10.1111/ejn.15928] [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: 08/03/2022] [Revised: 01/09/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023]
Abstract
The computational role of a neuron during attention depends on its firing properties, neurotransmitter expression and functional connectivity. Neurons in the visual cortical area V4 are reliably engaged by selective attention but exhibit diversity in the effect of attention on firing rates and correlated variability. It remains unclear what specific neuronal properties shape these attention effects. In this study, we quantitatively characterised the distribution of attention modulation of firing rates across populations of V4 neurons. Neurons exhibited a continuum of time-varying attention effects. At one end of the continuum, neurons' spontaneous firing rates were slightly depressed with attention (compared to when unattended), whereas their stimulus responses were enhanced with attention. The other end of the continuum showed the converse pattern: attention depressed stimulus responses but increased spontaneous activity. We tested whether the particular pattern of time-varying attention effects that a neuron exhibited was related to the shape of their actions potentials (so-called 'fast-spiking' [FS] neurons have been linked to inhibition) and the strength of their coupling to the overall population. We found an interdependence among neural attention effects, neuron type and population coupling. In particular, we found neurons for which attention enhanced spontaneous activity but suppressed stimulus responses were less likely to be fast-spiking (more likely to be non-fast-spiking) and tended to have stronger population coupling, compared to neurons with other types of attention effects. These results add important information to our understanding of visual attention circuits at the cellular level.
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Affiliation(s)
- Elizabeth M Sachse
- Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA.,Neuroscience, University of Minnesota, Minneapolis, Minnesota, USA
| | - Adam C Snyder
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, USA.,Neuroscience, University of Rochester, Rochester, New York, USA.,Center for Visual Sciences, University of Rochester, Rochester, New York, USA
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33
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Herzfeld DJ, Joshua M, Lisberger SG. Rate versus synchrony codes for cerebellar control of motor behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.17.529019. [PMID: 36824885 PMCID: PMC9949136 DOI: 10.1101/2023.02.17.529019] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
UNLABELLED Control of movement requires the coordination of multiple brain areas, each containing populations of neurons that receive inputs, process these inputs via recurrent dynamics, and then relay the processed information to downstream populations. Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the nature of the code for transmission of signals to downstream areas from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellar flocculus of rhesus macaques revealed how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and likely insufficient to impact the firing of downstream neurons in the vestibular nucleus. Further, analysis of previous metrics for assaying Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate code rather than synchrony code to drive activity of downstream neurons and thus control motor behavior. IMPACT STATEMENT Information transmission in the brain can occur via changes in firing rate or via the precise timing of spikes. Simultaneous recordings from pairs of Purkinje cells in the floccular complex reveals that information transmission out of the cerebellar cortex relies almost exclusively on changes in firing rates rather than millisecond-scale coordination of spike timing across the Purkinje cell population.
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Affiliation(s)
- David J. Herzfeld
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Mati Joshua
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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34
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West J, Dasht Bozorgi Z, Herron J, Chizeck HJ, Chambers JD, Li L. Machine learning seizure prediction: one problematic but accepted practice. J Neural Eng 2023; 20. [PMID: 36548993 DOI: 10.1088/1741-2552/acae09] [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: 10/06/2022] [Accepted: 12/22/2022] [Indexed: 12/24/2022]
Abstract
Objective.Epilepsy is one of the most common neurological disorders and can have a devastating effect on a person's quality of life. As such, the search for markers which indicate an upcoming seizure is a critically important area of research which would allow either on-demand treatment or early warning for people suffering with these disorders. There is a growing body of work which uses machine learning methods to detect pre-seizure biomarkers from electroencephalography (EEG), however the high prediction rates published do not translate into the clinical setting. Our objective is to investigate a potential reason for this.Approach.We conduct an empirical study of a commonly used data labelling method for EEG seizure prediction which relies on labelling small windows of EEG data in temporal groups then selecting randomly from those windows to validate results. We investigate a confound for this approach for seizure prediction and demonstrate the ease at which it can be inadvertently learned by a machine learning system.Main results.We find that non-seizure signals can create decision surfaces for machine learning approaches which can result in false high prediction accuracy on validation datasets. We prove this by training an artificial neural network to learn fake seizures (fully decoupled from biology) in real EEG.Significance.The significance of our findings is that many existing works may be reporting results based on this confound and that future work should adhere to stricter requirements in mitigating this confound. The problematic, but commonly accepted approach in the literature for seizure prediction labelling is potentially preventing real advances in developing solutions for these sufferers. By adhering to the guidelines in this paper future work in machine learning seizure prediction is more likely to be clinically relevant.
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Affiliation(s)
- Joseph West
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia.,Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Zahra Dasht Bozorgi
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Jeffrey Herron
- Department of Neurological Surgery, University of Washington, Seattle, Washington, United States of America
| | - Howard J Chizeck
- Department of Electrical and Computer Engineering, University of Washington, Seattle, Washington, United States of America
| | - Jordan D Chambers
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Lyra Li
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
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35
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Nigam S, Milton R, Pojoga S, Dragoi V. Adaptive coding across visual features during free-viewing and fixation conditions. Nat Commun 2023; 14:87. [PMID: 36604422 PMCID: PMC9816177 DOI: 10.1038/s41467-022-35656-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 12/15/2022] [Indexed: 01/07/2023] Open
Abstract
Theoretical studies have long proposed that adaptation allows the brain to effectively use the limited response range of sensory neurons to encode widely varying natural inputs. However, despite this influential view, experimental studies have exclusively focused on how the neural code adapts to a range of stimuli lying along a single feature axis, such as orientation or contrast. Here, we performed electrical recordings in macaque visual cortex (area V4) to reveal significant adaptive changes in the neural code of single cells and populations across multiple feature axes. Both during free viewing and passive fixation, populations of cells improved their ability to encode image features after rapid exposure to stimuli lying on orthogonal feature axes even in the absence of initial tuning to these stimuli. These results reveal a remarkable adaptive capacity of visual cortical populations to improve network computations relevant for natural viewing despite the modularity of the functional cortical architecture.
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Affiliation(s)
- Sunny Nigam
- Department of Neurobiology and Anatomy McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, US.
| | - Russell Milton
- Department of Neurobiology and Anatomy McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, US
| | - Sorin Pojoga
- Department of Neurobiology and Anatomy McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, US
| | - Valentin Dragoi
- Department of Neurobiology and Anatomy McGovern Medical School, University of Texas at Houston, Houston, TX, 77030, US.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, US.
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36
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Shi YL, Zeraati R, Levina A, Engel TA. Spatial and temporal correlations in neural networks with structured connectivity. PHYSICAL REVIEW RESEARCH 2023; 5:013005. [PMID: 38938692 PMCID: PMC11210526 DOI: 10.1103/physrevresearch.5.013005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
Correlated fluctuations in the activity of neural populations reflect the network's dynamics and connectivity. The temporal and spatial dimensions of neural correlations are interdependent. However, prior theoretical work mainly analyzed correlations in either spatial or temporal domains, oblivious to their interplay. We show that the network dynamics and connectivity jointly define the spatiotemporal profile of neural correlations. We derive analytical expressions for pairwise correlations in networks of binary units with spatially arranged connectivity in one and two dimensions. We find that spatial interactions among units generate multiple timescales in auto- and cross-correlations. Each timescale is associated with fluctuations at a particular spatial frequency, making a hierarchical contribution to the correlations. External inputs can modulate the correlation timescales when spatial interactions are nonlinear, and the modulation effect depends on the operating regime of network dynamics. These theoretical results open new ways to relate connectivity and dynamics in cortical networks via measurements of spatiotemporal neural correlations.
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Affiliation(s)
- Yan-Liang Shi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| | - Roxana Zeraati
- International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, Tübingen, Germany
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anna Levina
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience Tübingen, Tübingen, Germany
| | - Tatiana A Engel
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
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37
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Vila‐Vidal M, Khawaja M, Carreño M, Roldán P, Rumià J, Donaire A, Deco G, Tauste Campo A. Assessing the coupling between local neural activity and global connectivity fluctuations: Application to human intracranial electroencephalography during a cognitive task. Hum Brain Mapp 2022; 44:1173-1192. [PMID: 36437716 PMCID: PMC9875936 DOI: 10.1002/hbm.26150] [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: 06/20/2022] [Revised: 10/17/2022] [Accepted: 10/20/2022] [Indexed: 11/29/2022] Open
Abstract
Cognitive-relevant information is processed by different brain areas that cooperate to eventually produce a response. The relationship between local activity and global brain states during such processes, however, remains for the most part unexplored. To address this question, we designed a simple face-recognition task performed in patients with drug-resistant epilepsy and monitored with intracranial electroencephalography (EEG). Based on our observations, we developed a novel analytical framework (named "local-global" framework) to statistically correlate the brain activity in every recorded gray-matter region with the widespread connectivity fluctuations as proxy to identify concurrent local activations and global brain phenomena that may plausibly reflect a common functional network during cognition. The application of the local-global framework to the data from three subjects showed that similar connectivity fluctuations found across patients were mainly coupled to the local activity of brain areas involved in face information processing. In particular, our findings provide preliminary evidence that the reported global measures might be a novel signature of functional brain activity reorganization when a stimulus is processed in a task context regardless of the specific recorded areas.
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Affiliation(s)
- Manel Vila‐Vidal
- Center for Brain and Cognition, Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain,Computational Biology and Complex Systems Group, Department of PhysicsUniversitat Politècnica de CatalunyaBarcelonaSpain
| | | | - Mar Carreño
- Epilepsy ProgramHospital ClínicBarcelonaSpain,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - Pedro Roldán
- Epilepsy Program, NeurosurgeryHospital ClínicBarcelonaSpain
| | - Jordi Rumià
- Epilepsy Program, NeurosurgeryHospital ClínicBarcelonaSpain
| | - Antonio Donaire
- Epilepsy ProgramHospital ClínicBarcelonaSpain,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain,CIBERBBN, Networking Centre on Bioengineering, Biomaterials and NanomedicineBarcelonaSpain
| | - Gustavo Deco
- Computational Biology and Complex Systems Group, Department of PhysicsUniversitat Politècnica de CatalunyaBarcelonaSpain,Institució Catalana de Recerca i Estudis AvançatsBarcelonaSpain
| | - Adrià Tauste Campo
- Center for Brain and Cognition, Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain,Computational Biology and Complex Systems Group, Department of PhysicsUniversitat Politècnica de CatalunyaBarcelonaSpain
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38
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Wagatsuma N, Shimomura H, Nobukawa S. Disinhibitory circuit mediated by connections from vasoactive intestinal polypeptide to somatostatin interneurons underlies the paradoxical decrease in spike synchrony with increased border ownership selective neuron firing rate. Front Comput Neurosci 2022; 16:988715. [PMID: 36405781 PMCID: PMC9672816 DOI: 10.3389/fncom.2022.988715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
The activity of border ownership selective (BOS) neurons in intermediate-level visual areas indicates which side of a contour owns a border relative to its classical receptive field and provides a fundamental component of figure-ground segregation. A physiological study reported that selective attention facilitates the activity of BOS neurons with a consistent border ownership preference, defined as two neurons tuned to respond to the same visual object. However, spike synchrony between this pair is significantly suppressed by selective attention. These neurophysiological findings are derived from a biologically-plausible microcircuit model consisting of spiking neurons including two subtypes of inhibitory interneurons, somatostatin (SOM) and vasoactive intestinal polypeptide (VIP) interneurons, and excitatory BOS model neurons. In our proposed model, BOS neurons and SOM interneurons cooperate and interact with each other. VIP interneurons not only suppress SOM interneuron responses but also are activated by feedback signals mediating selective attention, which leads to disinhibition of BOS neurons when they are directing selective attention toward an object. Our results suggest that disinhibition arising from the synaptic connections from VIP to SOM interneurons plays a critical role in attentional modulation of neurons in intermediate-level visual areas.
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Affiliation(s)
- Nobuhiko Wagatsuma
- Department of Information Science, Faculty of Science, Toho University, Funabashi, Japan
- *Correspondence: Nobuhiko Wagatsuma,
| | - Haruka Shimomura
- Department of Information Science, Faculty of Science, Toho University, Funabashi, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
- Department of Preventive Intervention for Psychiatric Disorders, National Center of Neurology and Psychiatry, Kodaira, Japan
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39
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Trepka EB, Zhu S, Xia R, Chen X, Moore T. Functional interactions among neurons within single columns of macaque V1. eLife 2022; 11:e79322. [PMID: 36321687 PMCID: PMC9662816 DOI: 10.7554/elife.79322] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022] Open
Abstract
Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.
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Affiliation(s)
- Ethan B Trepka
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
- Neurosciences Program, Stanford UniversityStanfordUnited States
| | - Shude Zhu
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Ruobing Xia
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Xiaomo Chen
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
- Center for Neuroscience, Department of Neurobiology, Physiology, and Behavior, University of California, DavisDavisUnited States
| | - Tirin Moore
- Department of Neurobiology, Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
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40
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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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41
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Leung D, Sun W. ZAP: Z$$ Z $$‐value adaptive procedures for false discovery rate control with side information. J R Stat Soc Series B Stat Methodol 2022. [DOI: 10.1111/rssb.12557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Affiliation(s)
- Dennis Leung
- School of Mathematics and Statistics University of Melbourne Parkville Victoria Australia
| | - Wenguang Sun
- Center for Data Science Zhejiang University Hangzhou China
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42
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Gansel KS. Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding. Front Integr Neurosci 2022; 16:900715. [PMID: 36262373 PMCID: PMC9574343 DOI: 10.3389/fnint.2022.900715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 09/13/2022] [Indexed: 11/13/2022] Open
Abstract
Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.
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43
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Khoury CF, Fala NG, Runyan CA. The spatial scale of somatostatin subnetworks increases from sensory to association cortex. Cell Rep 2022; 40:111319. [PMID: 36070697 DOI: 10.1016/j.celrep.2022.111319] [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: 03/20/2022] [Revised: 07/01/2022] [Accepted: 08/15/2022] [Indexed: 11/24/2022] Open
Abstract
Incoming signals interact with rich, ongoing population activity dynamics in cortical circuits. These intrinsic dynamics are the consequence of interactions among local excitatory and inhibitory neurons and affect inter-region communication and information coding. It is unclear whether specializations in the patterns of interactions among excitatory and inhibitory neurons underlie systematic differences in activity dynamics across the cortex. Here, in mice, we compare the functional interactions among somatostatin (SOM)-expressing inhibitory interneurons and the rest of the neural population in auditory cortex (AC), a sensory region of the cortex, and posterior parietal cortex (PPC), an association region. The spatial structure of shared variability among SOM and non-SOM neurons differs across regions: correlations decay rapidly with distance in AC but not in PPC. However, in both regions, activity of SOM neurons is more highly correlated than non-SOM neurons' activity. Our results imply both generalization and specialization in the functional structure of inhibitory subnetworks across the cortex.
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Affiliation(s)
- Christine F Khoury
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Noelle G Fala
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Caroline A Runyan
- Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA; Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA 15260, USA.
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44
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Sibille J, Gehr C, Benichov JI, Balasubramanian H, Teh KL, Lupashina T, Vallentin D, Kremkow J. High-density electrode recordings reveal strong and specific connections between retinal ganglion cells and midbrain neurons. Nat Commun 2022; 13:5218. [PMID: 36064789 PMCID: PMC9445019 DOI: 10.1038/s41467-022-32775-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 08/16/2022] [Indexed: 11/29/2022] Open
Abstract
The superior colliculus is a midbrain structure that plays important roles in visually guided behaviors in mammals. Neurons in the superior colliculus receive inputs from retinal ganglion cells but how these inputs are integrated in vivo is unknown. Here, we discovered that high-density electrodes simultaneously capture the activity of retinal axons and their postsynaptic target neurons in the superior colliculus, in vivo. We show that retinal ganglion cell axons in the mouse provide a single cell precise representation of the retina as input to superior colliculus. This isomorphic mapping builds the scaffold for precise retinotopic wiring and functionally specific connection strength. Our methods are broadly applicable, which we demonstrate by recording retinal inputs in the optic tectum in zebra finches. We find common wiring rules in mice and zebra finches that provide a precise representation of the visual world encoded in retinal ganglion cells connections to neurons in retinorecipient areas.
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Affiliation(s)
- Jérémie Sibille
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, 10115, Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Philippstraße 13, 10115, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Carolin Gehr
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, 10115, Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Philippstraße 13, 10115, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Jonathan I Benichov
- Max Planck Institute for Ornithology, Eberhard-Gwinner Straße, 82319, Seewiesen, Germany
- Max Planck Institute for Biological Intelligence (in foundation), Eberhard-Gwinner Straße, 82319, Seewiesen, Germany
| | - Hymavathy Balasubramanian
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, 10115, Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Philippstraße 13, 10115, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Kai Lun Teh
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, 10115, Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Philippstraße 13, 10115, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Tatiana Lupashina
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, 10115, Berlin, Germany
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Philippstraße 13, 10115, Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany
| | - Daniela Vallentin
- Max Planck Institute for Ornithology, Eberhard-Gwinner Straße, 82319, Seewiesen, Germany
- Max Planck Institute for Biological Intelligence (in foundation), Eberhard-Gwinner Straße, 82319, Seewiesen, Germany
| | - Jens Kremkow
- Neuroscience Research Center, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Bernstein Center for Computational Neuroscience Berlin, Philippstraße 13, 10115, Berlin, Germany.
- Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Philippstraße 13, 10115, Berlin, Germany.
- Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany.
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45
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Chini M, Pfeffer T, Hanganu-Opatz I. An increase of inhibition drives the developmental decorrelation of neural activity. eLife 2022; 11:78811. [PMID: 35975980 PMCID: PMC9448324 DOI: 10.7554/elife.78811] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/16/2022] [Indexed: 11/23/2022] Open
Abstract
Throughout development, the brain transits from early highly synchronous activity patterns to a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying this process are poorly understood. The developmental transition has important functional consequences, as the latter state is thought to allow for more efficient storage, retrieval, and processing of information. Here, we show that, in the mouse medial prefrontal cortex (mPFC), neural activity during the first two postnatal weeks decorrelates following specific spatial patterns. This process is accompanied by a concomitant tilting of excitation-inhibition (E-I) ratio toward inhibition. Using optogenetic manipulations and neural network modeling, we show that the two phenomena are mechanistically linked, and that a relative increase of inhibition drives the decorrelation of neural activity. Accordingly, in mice mimicking the etiology of neurodevelopmental disorders, subtle alterations in E-I ratio are associated with specific impairments in the correlational structure of spike trains. Finally, capitalizing on EEG data from newborn babies, we show that an analogous developmental transition takes place also in the human brain. Thus, changes in E-I ratio control the (de)correlation of neural activity and, by these means, its developmental imbalance might contribute to the pathogenesis of neurodevelopmental disorders.
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Affiliation(s)
- Mattia Chini
- Institute of Developmental Neurophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thomas Pfeffer
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Ileana Hanganu-Opatz
- Institute of Developmental Neurophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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46
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Gokcen E, Jasper AI, Semedo JD, Zandvakili A, Kohn A, Machens CK, Yu BM. Disentangling the flow of signals between populations of neurons. NATURE COMPUTATIONAL SCIENCE 2022; 2:512-525. [PMID: 38177794 DOI: 10.1038/s43588-022-00282-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 06/21/2022] [Indexed: 01/06/2024]
Abstract
Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: how do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We propose here a dimensionality reduction framework, delayed latents across groups (DLAG), that disentangles signals relayed in each direction, identifies how these signals are represented by each population and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signalling contributes to cortical computation.
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Affiliation(s)
- Evren Gokcen
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Anna I Jasper
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
| | - João D Semedo
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Amin Zandvakili
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
| | - Adam Kohn
- Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, New York, NY, USA
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, New York, NY, USA
| | - Christian K Machens
- Champalimaud Neuroscience Programme, Champalimaud Foundation, Lisbon, Portugal
| | - Byron M Yu
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
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47
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Hu Q, Zheng Z, Sui X, Li L, Chai X, Chen Y. Spatial Attention Modulates Spike Count Correlations and Granger Causality in the Primary Visual Cortex. Front Cell Neurosci 2022; 16:838049. [PMID: 35783091 PMCID: PMC9246483 DOI: 10.3389/fncel.2022.838049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 05/23/2022] [Indexed: 11/16/2022] Open
Abstract
The influence of spatial attention on neural interactions has been revealed even in early visual information processing stages. It resolves the process of competing for sensory information about objects perceived as targets and distractors. However, the attentional modulation of the interaction between pairs of neurons with non-overlapping receptive fields (RFs) is not well known. Here, we investigated the activity of anatomically distant neurons in two behaving monkeys’ primary visual cortex (V1), when they performed a spatial attention task detecting color change. We compared attentional modulation from the perspective of spike count correlations and Granger causality among simple and complex cells. An attention-related increase in spike count correlations and a decrease in Granger causality were found. The results showed that spatial attention significantly influenced only the interactions between rather than within simple and complex cells. Furthermore, we found that the attentional modulation of neuronal interactions changed with neuronal pairs’ preferred directions differences. Thus, we found that spatial attention increased the functional communications and competing connectivities when attending to the neurons’ RFs, which impacts the interactions only between simple and complex cells. Our findings enrich the model of simple and complex cells and further understand the way that attention influences the neurons’ activities.
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Affiliation(s)
- Qiyi Hu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhiyan Zheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaohong Sui
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Liming Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xinyu Chai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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48
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Ray S. Spike-Gamma Phase Relationship in the Visual Cortex. Annu Rev Vis Sci 2022; 8:361-381. [PMID: 35667158 DOI: 10.1146/annurev-vision-100419-104530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Gamma oscillations (30-70 Hz) have been hypothesized to play a role in cortical function. Most of the proposed mechanisms involve rhythmic modulation of neuronal excitability at gamma frequencies, leading to modulation of spike timing relative to the rhythm. I first show that the gamma band could be more privileged than other frequencies in observing spike-field interactions even in the absence of genuine gamma rhythmicity and discuss several biases in spike-gamma phase estimation. I then discuss the expected spike-gamma phase according to several hypotheses. Inconsistent with the phase-coding hypothesis (but not with others), the spike-gamma phase does not change with changes in stimulus intensity or attentional state, with spikes preferentially occurring 2-4 ms before the trough, but with substantial variability. However, this phase relationship is expected even when gamma is a byproduct of excitatory-inhibitory interactions. Given that gamma occurs in short bursts, I argue that the debate over the role of gamma is a matter of semantics. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India 560012;
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49
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Chung JE, Sellers KK, Leonard MK, Gwilliams L, Xu D, Dougherty ME, Kharazia V, Metzger SL, Welkenhuysen M, Dutta B, Chang EF. High-density single-unit human cortical recordings using the Neuropixels probe. Neuron 2022; 110:2409-2421.e3. [PMID: 35679860 DOI: 10.1016/j.neuron.2022.05.007] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/10/2022] [Accepted: 05/10/2022] [Indexed: 10/18/2022]
Abstract
The action potential is a fundamental unit of neural computation. Even though significant advances have been made in recording large numbers of individual neurons in animal models, translation of these methodologies to humans has been limited because of clinical constraints and electrode reliability. Here, we present a reliable method for intraoperative recording of dozens of neurons in humans using the Neuropixels probe, yielding up to ∼100 simultaneously recorded single units. Most single units were active within 1 min of reaching target depth. The motion of the electrode array had a strong inverse correlation with yield, identifying a major challenge and opportunity to further increase the probe utility. Cell pairs active close in time were spatially closer in most recordings, demonstrating the power to resolve complex cortical dynamics. Altogether, this approach provides access to population single-unit activity across the depth of human neocortex at scales previously only accessible in animal models.
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Affiliation(s)
- Jason E Chung
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Matthew K Leonard
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Laura Gwilliams
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Duo Xu
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Maximilian E Dougherty
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Viktor Kharazia
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Sean L Metzger
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA; University of California Berkeley, University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA 94720, USA
| | | | | | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94158, USA.
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Hirano K, Morishita Y, Minami M, Nomura H. The impact of pitolisant, an H 3 receptor antagonist/inverse agonist, on perirhinal cortex activity in individual neuron and neuronal population levels. Sci Rep 2022; 12:7015. [PMID: 35551460 PMCID: PMC9098477 DOI: 10.1038/s41598-022-11032-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 04/18/2022] [Indexed: 11/09/2022] Open
Abstract
Histamine is a neurotransmitter that modulates neuronal activity and regulates various brain functions. Histamine H3 receptor (H3R) antagonists/inverse agonists enhance its release in most brain regions, including the cerebral cortex, which improves learning and memory and exerts an antiepileptic effect. However, the mechanism underlying the effect of H3R antagonists/inverse agonists on cortical neuronal activity in vivo remains unclear. Here, we show the mechanism by which pitolisant, an H3R antagonist/inverse agonist, influenced perirhinal cortex (PRh) activity in individual neuron and neuronal population levels. We monitored neuronal activity in the PRh of freely moving mice using in vivo Ca2+ imaging through a miniaturized one-photon microscope. Pitolisant increased the activity of some PRh neurons while decreasing the activity of others without affecting the mean neuronal activity across neurons. Moreover, it increases neuron pairs with synchronous activity in excitatory-responsive neuronal populations. Furthermore, machine learning analysis revealed that pitolisant altered the neuronal population activity. The changes in the population activity were dependent on the neurons that were excited and inhibited by pitolisant treatment. These findings indicate that pitolisant influences the activity of a subset of PRh neurons by increasing the synchronous activity and modifying the population activity.
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Affiliation(s)
- Kyosuke Hirano
- Endowed Department of Cognitive Function and Pathology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, 467-8601, Japan.,Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo, 060-0812, Japan
| | - Yoshikazu Morishita
- Endowed Department of Cognitive Function and Pathology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, 467-8601, Japan
| | - Masabumi Minami
- Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo, 060-0812, Japan
| | - Hiroshi Nomura
- Endowed Department of Cognitive Function and Pathology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, 467-8601, Japan. .,Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo, 060-0812, Japan.
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