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Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models. J Neurosci 2021; 41:8577-8588. [PMID: 34413204 DOI: 10.1523/jneurosci.0051-21.2021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 07/06/2021] [Accepted: 07/11/2021] [Indexed: 01/21/2023] Open
Abstract
Neuronal ensembles are groups of neurons with coordinated activity that could represent sensory, motor, or cognitive states. The study of how neuronal ensembles are built, recalled, and involved in the guiding of complex behaviors has been limited by the lack of experimental and analytical tools to reliably identify and manipulate neurons that have the ability to activate entire ensembles. Such pattern completion neurons have also been proposed as key elements of artificial and biological neural networks. Indeed, the relevance of pattern completion neurons is highlighted by growing evidence that targeting them can activate neuronal ensembles and trigger behavior. As a method to reliably detect pattern completion neurons, we use conditional random fields (CRFs), a type of probabilistic graphical model. We apply CRFs to identify pattern completion neurons in ensembles in experiments using in vivo two-photon calcium imaging from primary visual cortex of male mice and confirm the CRFs predictions with two-photon optogenetics. To test the broader applicability of CRFs we also analyze publicly available calcium imaging data (Allen Institute Brain Observatory dataset) and demonstrate that CRFs can reliably identify neurons that predict specific features of visual stimuli. Finally, to explore the scalability of CRFs we apply them to in silico network simulations and show that CRFs-identified pattern completion neurons have increased functional connectivity. These results demonstrate the potential of CRFs to characterize and selectively manipulate neural circuits.SIGNIFICANCE STATEMENT We describe a graph theory method to identify and optically manipulate neurons with pattern completion capability in mouse cortical circuits. Using calcium imaging and two-photon optogenetics in vivo we confirm that key neurons identified by this method can recall entire neuronal ensembles. This method could be broadly applied to manipulate neuronal ensemble activity to trigger behavior or for therapeutic applications in brain prostheses.
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2
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Curto C, Morrison K. Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience. Curr Opin Neurobiol 2019; 58:11-20. [PMID: 31319287 DOI: 10.1016/j.conb.2019.06.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 06/22/2019] [Indexed: 11/29/2022]
Abstract
We review recent work relating network connectivity to the dynamics of neural activity. While concepts stemming from network science provide a valuable starting point, the interpretation of graph-theoretic structures and measures can be highly dependent on the dynamics associated to the network. Properties that are quite meaningful for linear dynamics, such as random walk and network flow models, may be of limited relevance in the neuroscience setting. Theoretical and computational neuroscience are playing a vital role in understanding the relationship between network connectivity and the nonlinear dynamics associated to neural networks.
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Affiliation(s)
- Carina Curto
- The Pennsylvania State University, PA 16802, United States.
| | - Katherine Morrison
- School of Mathematical Sciences, University of Northern Colorado, Greeley, CO 80639, USA
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3
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Sizemore AE, Bassett DS. Dynamic graph metrics: Tutorial, toolbox, and tale. Neuroimage 2018; 180:417-427. [PMID: 28698107 PMCID: PMC5758445 DOI: 10.1016/j.neuroimage.2017.06.081] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/24/2017] [Accepted: 06/29/2017] [Indexed: 11/23/2022] Open
Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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4
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Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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5
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Chambers B, Levy M, Dechery JB, MacLean JN. Ensemble stacking mitigates biases in inference of synaptic connectivity. Netw Neurosci 2018; 2:60-85. [PMID: 29911678 PMCID: PMC5989998 DOI: 10.1162/netn_a_00032] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 10/11/2017] [Indexed: 01/26/2023] Open
Abstract
A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.
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Affiliation(s)
- Brendan Chambers
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Maayan Levy
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Joseph B Dechery
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA
| | - Jason N MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA.,Department of Neurobiology, University of Chicago, Chicago, IL, USA
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6
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See JZ, Atencio CA, Sohal VS, Schreiner CE. Coordinated neuronal ensembles in primary auditory cortical columns. eLife 2018; 7:e35587. [PMID: 29869986 PMCID: PMC6017807 DOI: 10.7554/elife.35587] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 06/03/2018] [Indexed: 12/15/2022] Open
Abstract
The synchronous activity of groups of neurons is increasingly thought to be important in cortical information processing and transmission. However, most studies of processing in the primary auditory cortex (AI) have viewed neurons as independent filters; little is known about how coordinated AI neuronal activity is expressed throughout cortical columns and how it might enhance the processing of auditory information. To address this, we recorded from populations of neurons in AI cortical columns of anesthetized rats and, using dimensionality reduction techniques, identified multiple coordinated neuronal ensembles (cNEs), which are groups of neurons with reliable synchronous activity. We show that cNEs reflect local network configurations with enhanced information encoding properties that cannot be accounted for by stimulus-driven synchronization alone. Furthermore, similar cNEs were identified in both spontaneous and evoked activity, indicating that columnar cNEs are stable functional constructs that may represent principal units of information processing in AI.
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Affiliation(s)
- Jermyn Z See
- UCSF Center for Integrative NeuroscienceUniversity of California, San FranciscoSan FranciscoUnited States
- Coleman Memorial LaboratoryUniversity of California, San FranciscoSan FranciscoUnited States
- Department of Otolaryngology – Head and Neck SurgeryUniversity of California, San FranciscoSan FranciscoUnited States
- Department of PsychiatryUniversity of CaliforniaSan FranciscoUnited States
| | - Craig A Atencio
- UCSF Center for Integrative NeuroscienceUniversity of California, San FranciscoSan FranciscoUnited States
- Coleman Memorial LaboratoryUniversity of California, San FranciscoSan FranciscoUnited States
- Department of Otolaryngology – Head and Neck SurgeryUniversity of California, San FranciscoSan FranciscoUnited States
| | - Vikaas S Sohal
- UCSF Center for Integrative NeuroscienceUniversity of California, San FranciscoSan FranciscoUnited States
- Department of PsychiatryUniversity of CaliforniaSan FranciscoUnited States
| | - Christoph E Schreiner
- UCSF Center for Integrative NeuroscienceUniversity of California, San FranciscoSan FranciscoUnited States
- Coleman Memorial LaboratoryUniversity of California, San FranciscoSan FranciscoUnited States
- Department of Otolaryngology – Head and Neck SurgeryUniversity of California, San FranciscoSan FranciscoUnited States
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7
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Blackwell JM, Geffen MN. Progress and challenges for understanding the function of cortical microcircuits in auditory processing. Nat Commun 2017; 8:2165. [PMID: 29255268 PMCID: PMC5735136 DOI: 10.1038/s41467-017-01755-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 10/12/2017] [Indexed: 12/21/2022] Open
Abstract
An important outstanding question in auditory neuroscience is to identify the mechanisms by which specific motifs within inter-connected neural circuits affect auditory processing and, ultimately, behavior. In the auditory cortex, a combination of large-scale electrophysiological recordings and concurrent optogenetic manipulations are improving our understanding of the role of inhibitory–excitatory interactions. At the same time, computational approaches have grown to incorporate diverse neuronal types and connectivity patterns. However, we are still far from understanding how cortical microcircuits encode and transmit information about complex acoustic scenes. In this review, we focus on recent results identifying the special function of different cortical neurons in the auditory cortex and discuss a computational framework for future work that incorporates ideas from network science and network dynamics toward the coding of complex auditory scenes. Advances in multi-neuron recordings and optogenetic manipulation have resulted in an interrogation of the function of specific cortical cell types in auditory cortex during sound processing. Here, the authors review this literature and discuss the merits of integrating computational approaches from dynamic network science.
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Affiliation(s)
- Jennifer M Blackwell
- Department of Otorhinolaryngology: HNS, Department of Neuroscience, Neuroscience Graduate Group, Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maria N Geffen
- Department of Otorhinolaryngology: HNS, Department of Neuroscience, Neuroscience Graduate Group, Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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8
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Doiron B, Litwin-Kumar A, Rosenbaum R, Ocker GK, Josić K. The mechanics of state-dependent neural correlations. Nat Neurosci 2016; 19:383-93. [PMID: 26906505 DOI: 10.1038/nn.4242] [Citation(s) in RCA: 173] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Accepted: 01/12/2016] [Indexed: 12/12/2022]
Abstract
Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.
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Affiliation(s)
- Brent Doiron
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA
| | - Ashok Litwin-Kumar
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.,Center for Theoretical Neuroscience, Columbia University, New York, New York, USA
| | - Robert Rosenbaum
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.,Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.,Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, USA
| | - Gabriel K Ocker
- Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, USA.,Allen Institute for Brain Science, Seattle, Washington, USA
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas, USA.,Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA
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9
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Frye CG, MacLean JN. Spontaneous activations follow a common developmental course across primary sensory areas in mouse neocortex. J Neurophysiol 2016; 116:431-7. [PMID: 27146981 DOI: 10.1152/jn.00172.2016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 05/01/2016] [Indexed: 11/22/2022] Open
Abstract
Spontaneous propagation of spiking within the local neocortical circuits of mature primary sensory areas is highly nonrandom, engaging specific sets of interconnected and functionally related neurons. These spontaneous activations promise insight into neocortical structure and function, but their properties in the first 2 wk of perinatal development are incompletely characterized. Previously, we have found that there is a minimal numerical sample, on the order of 400 cells, necessary to fully capture mature neocortical circuit dynamics. Therefore we maximized our numerical sample by using two-photon calcium imaging to observe spontaneous activity in populations of up to 1,062 neurons spanning multiple columns and layers in 52 acute coronal slices of mouse neocortex at each day from postnatal day (PND) 3 to PND 15. Slices contained either primary auditory cortex (A1) or somatosensory barrel field (S1BF), which allowed us to compare sensory modalities with markedly different developmental timelines. Between PND 3 and PND 8, populations in both areas exhibited activations of anatomically compact subgroups on the order of dozens of cells. Between PND 9 and PND 13, the spatiotemporal structure of the activity diversified to include spatially distributed activations encompassing hundreds of cells. Sparse activations covering the entire field of view dominated in slices taken on or after PND 14. These and other findings demonstrate that the developmental progression of spontaneous activations from active local modules in the first postnatal week to sparse, intermingled groups of neurons at the beginning of the third postnatal week generalizes across primary sensory areas, consistent with an intrinsic developmental trajectory independent of sensory input.
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Affiliation(s)
- Charles G Frye
- Helen Wills Neuroscience Institute, University of California, Berkeley, California; and Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Jason N MacLean
- Department of Neurobiology, University of Chicago, Chicago, Illinois
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10
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Pyle R, Rosenbaum R. Highly connected neurons spike less frequently in balanced networks. Phys Rev E 2016; 93:040302. [PMID: 27176240 DOI: 10.1103/physreve.93.040302] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Indexed: 11/07/2022]
Abstract
Biological neuronal networks exhibit highly variable spiking activity. Balanced networks offer a parsimonious model of this variability in which strong excitatory synaptic inputs are canceled by strong inhibitory inputs on average, and irregular spiking activity is driven by fluctuating synaptic currents. Most previous studies of balanced networks assume a homogeneous or distance-dependent connectivity structure, but connectivity in biological cortical networks is more intricate. We use a heterogeneous mean-field theory of balanced networks to show that heterogeneous in-degrees can break balance. Moreover, heterogeneous architectures that achieve balance promote lower firing rates in neurons with larger in-degrees, consistent with some recent experimental observations.
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Affiliation(s)
- Ryan Pyle
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA
| | - Robert Rosenbaum
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA.,Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana 46556, USA
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11
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Luongo FJ, Zimmerman CA, Horn ME, Sohal VS. Correlations between prefrontal neurons form a small-world network that optimizes the generation of multineuron sequences of activity. J Neurophysiol 2016; 115:2359-75. [PMID: 26888108 DOI: 10.1152/jn.01043.2015] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 02/15/2016] [Indexed: 12/11/2022] Open
Abstract
Sequential patterns of prefrontal activity are believed to mediate important behaviors, e.g., working memory, but it remains unclear exactly how they are generated. In accordance with previous studies of cortical circuits, we found that prefrontal microcircuits in young adult mice spontaneously generate many more stereotyped sequences of activity than expected by chance. However, the key question of whether these sequences depend on a specific functional organization within the cortical microcircuit, or emerge simply as a by-product of random interactions between neurons, remains unanswered. We observed that correlations between prefrontal neurons do follow a specific functional organization-they have a small-world topology. However, until now it has not been possible to directly link small-world topologies to specific circuit functions, e.g., sequence generation. Therefore, we developed a novel analysis to address this issue. Specifically, we constructed surrogate data sets that have identical levels of network activity at every point in time but nevertheless represent various network topologies. We call this method shuffling activity to rearrange correlations (SHARC). We found that only surrogate data sets based on the actual small-world functional organization of prefrontal microcircuits were able to reproduce the levels of sequences observed in actual data. As expected, small-world data sets contained many more sequences than surrogate data sets with randomly arranged correlations. Surprisingly, small-world data sets also outperformed data sets in which correlations were maximally clustered. Thus the small-world functional organization of cortical microcircuits, which effectively balances the random and maximally clustered regimes, is optimal for producing stereotyped sequential patterns of activity.
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Affiliation(s)
- Francisco J Luongo
- Department of Psychiatry, University of California, San Francisco, California; Center for Integrative Neuroscience, University of California, San Francisco, California; Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, California; and Neuroscience Graduate Program, University of California, San Francisco, California
| | - Chris A Zimmerman
- Neuroscience Graduate Program, University of California, San Francisco, California
| | - Meryl E Horn
- Neuroscience Graduate Program, University of California, San Francisco, California
| | - Vikaas S Sohal
- Department of Psychiatry, University of California, San Francisco, California; Center for Integrative Neuroscience, University of California, San Francisco, California; Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, California; and
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12
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Kim S, Callier T, Tabot GA, Gaunt RA, Tenore FV, Bensmaia SJ. Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proc Natl Acad Sci U S A 2015; 112:15202-7. [PMID: 26504211 PMCID: PMC4679002 DOI: 10.1073/pnas.1509265112] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Intracortical microstimulation (ICMS) is a powerful tool to investigate the functional role of neural circuits and may provide a means to restore sensation for patients for whom peripheral stimulation is not an option. In a series of psychophysical experiments with nonhuman primates, we investigate how stimulation parameters affect behavioral sensitivity to ICMS. Specifically, we deliver ICMS to primary somatosensory cortex through chronically implanted electrode arrays across a wide range of stimulation regimes. First, we investigate how the detectability of ICMS depends on stimulation parameters, including pulse width, frequency, amplitude, and pulse train duration. Then, we characterize the degree to which ICMS pulse trains that differ in amplitude lead to discriminable percepts across the range of perceptible and safe amplitudes. We also investigate how discriminability of pulse amplitude is modulated by other stimulation parameters-namely, frequency and duration. Perceptual judgments obtained across these various conditions will inform the design of stimulation regimes for neuroscience and neuroengineering applications.
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Affiliation(s)
- Sungshin Kim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637
| | - Thierri Callier
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637
| | - Gregg A Tabot
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637
| | - Robert A Gaunt
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, PA 15213
| | - Francesco V Tenore
- Research and Exploratory Development Department, Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723
| | - Sliman J Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637; Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637;
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13
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Chambers B, MacLean JN. Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints. J Neurophysiol 2015. [PMID: 26203109 DOI: 10.1152/jn.00429.2015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Structured multineuronal activity patterns within local neocortical circuitry are strongly linked to sensory input, motor output, and behavioral choice. These reliable patterns of pairwise lagged firing are the consequence of connectivity since they are not present in rate-matched but unconnected Poisson nulls. It is important to relate multineuronal patterns to their synaptic underpinnings, but it is unclear how effectively statistical dependencies in spiking between neurons identify causal synaptic connections. To assess the feasibility of mapping function onto structure we used a network model that showed a diversity of multineuronal activity patterns and replicated experimental constraints on data acquisition. Using an iterative Bayesian inference algorithm, we detected a select subset of monosynaptic connections substantially more precisely than correlation-based inference, a common alternative approach. We found that precise inference of synaptic connections improved with increasing numbers of diverse multineuronal activity patterns in contrast to increased observations of a single pattern. Surprisingly, neuronal spiking was most effective and precise at revealing causal synaptic connectivity when the lags considered by the iterative Bayesian algorithm encompassed the timescale of synaptic conductance and integration (∼10 ms), rather than synaptic transmission time (∼2 ms), highlighting the importance of synaptic integration in driving postsynaptic spiking. Last, strong synaptic connections were detected preferentially, underscoring their special importance in cortical computation. Even after simulating experimental constraints, top down approaches to cortical connectivity, from function to structure, identify synaptic connections underlying multineuronal activity. These select connections are closely tied to cortical processing.
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Affiliation(s)
- Brendan Chambers
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and
| | - Jason N MacLean
- Committee on Computational Neuroscience, University of Chicago, Chicago, Illinois; and Department of Neurobiology, University of Chicago, Chicago, Illinois
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