1
|
Pi JS, Fakharian MA, Hage P, Sedaghat-Nejad E, Muller SZ, Shadmehr R. The olivary input to the cerebellum dissociates sensory events from movement plans. Proc Natl Acad Sci U S A 2024; 121:e2318849121. [PMID: 38630714 PMCID: PMC11047103 DOI: 10.1073/pnas.2318849121] [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: 03/19/2024] [Indexed: 04/19/2024] Open
Abstract
Neurons in the inferior olive are thought to anatomically organize the Purkinje cells (P-cells) of the cerebellum into computational modules, but what is computed by each module? Here, we designed a saccade task in marmosets that dissociated sensory events from motor events and then recorded the complex and simple spikes of hundreds of P-cells. We found that when a visual target was presented at a random location, the olive reported the direction of that sensory event to one group of P-cells, but not to a second group. However, just before movement onset, it reported the direction of the planned movement to both groups, even if that movement was not toward the target. At the end of the movement if the subject experienced an error but chose to withhold the corrective movement, only the first group received information about the sensory prediction error. We organized the P-cells based on the information content of their olivary input and found that in the group that received sensory information, the simple spikes were suppressed during fixation, then produced a burst before saccade onset in a direction consistent with assisting the movement. In the second group, the simple spikes were not suppressed during fixation but burst near saccade deceleration in a direction consistent with stopping the movement. Thus, the olive differentiated the P-cells based on whether they would receive sensory or motor information, and this defined their contributions to control of movements as well as holding still.
Collapse
Affiliation(s)
- Jay S. Pi
- Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MA21205
| | - Mohammad Amin Fakharian
- Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MA21205
| | - Paul Hage
- Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MA21205
| | - Ehsan Sedaghat-Nejad
- Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MA21205
| | - Salomon Z. Muller
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY10027
| | - Reza Shadmehr
- Laboratory for Computational Motor Control, Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MA21205
| |
Collapse
|
2
|
Safavi S, Panagiotaropoulos TI, Kapoor V, Ramirez-Villegas JF, Logothetis NK, Besserve M. Uncovering the organization of neural circuits with Generalized Phase Locking Analysis. PLoS Comput Biol 2023; 19:e1010983. [PMID: 37011110 PMCID: PMC10109521 DOI: 10.1371/journal.pcbi.1010983] [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: 06/22/2022] [Revised: 04/17/2023] [Accepted: 02/27/2023] [Indexed: 04/05/2023] Open
Abstract
Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.
Collapse
Affiliation(s)
- Shervin Safavi
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Tübingen, Germany
| | - Theofanis I. Panagiotaropoulos
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France
| | - Vishal Kapoor
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- International Center for Primate Brain Research (ICPBR), Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS), Shanghai 201602, China
| | - Juan F. Ramirez-Villegas
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Institute of Science and Technology Austria (IST Austria), Klosterneuburg, Austria
| | - Nikos K. Logothetis
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- International Center for Primate Brain Research (ICPBR), Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS), Shanghai 201602, China
- Centre for Imaging Sciences, Biomedical Imaging Institute, The University of Manchester, Manchester, United Kingdom
| | - Michel Besserve
- Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Department of Empirical Inference, Max Planck Institute for Intelligent Systems and MPI-ETH Center for Learning Systems, Tübingen, Germany
| |
Collapse
|
3
|
Levi A, Spivak L, Sloin HE, Someck S, Stark E. Error correction and improved precision of spike timing in converging cortical networks. Cell Rep 2022; 40:111383. [PMID: 36130516 PMCID: PMC9513803 DOI: 10.1016/j.celrep.2022.111383] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/26/2022] [Accepted: 08/28/2022] [Indexed: 11/20/2022] Open
Abstract
The brain propagates neuronal signals accurately and rapidly. Nevertheless, whether and how a pool of cortical neurons transmits an undistorted message to a target remains unclear. We apply optogenetic white noise signals to small assemblies of cortical pyramidal cells (PYRs) in freely moving mice. The directly activated PYRs exhibit a spike timing precision of several milliseconds. Instead of losing precision, interneurons driven via synaptic activation exhibit higher precision with respect to the white noise signal. Compared with directly activated PYRs, postsynaptic interneuron spike trains allow better signal reconstruction, demonstrating error correction. Data-driven modeling shows that nonlinear amplification of coincident spikes can generate error correction and improved precision. Over multiple applications of the same signal, postsynaptic interneuron spiking is most reliable at timescales ten times shorter than those of the presynaptic PYR, exhibiting temporal coding. Similar results are observed in hippocampal region CA1. Coincidence detection of convergent inputs enables messages to be precisely propagated between cortical PYRs and interneurons. PYR-to-interneuron spike transmission exhibits error correction and improved precision Interneuron precision is higher when a larger pool of presynaptic PYRs is recruited Error correction and improved precision are consistent with coincidence detection Interneurons activated by synaptic transmission act as temporal coders
Collapse
Affiliation(s)
- Amir Levi
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Lidor Spivak
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Hadas E Sloin
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Shirly Someck
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Stark
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel.
| |
Collapse
|
4
|
Spivak L, Levi A, Sloin HE, Someck S, Stark E. Deconvolution improves the detection and quantification of spike transmission gain from spike trains. Commun Biol 2022; 5:520. [PMID: 35641587 PMCID: PMC9156687 DOI: 10.1038/s42003-022-03450-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 05/04/2022] [Indexed: 12/22/2022] Open
Abstract
Accurate detection and quantification of spike transmission between neurons is essential for determining neural network mechanisms that govern cognitive functions. Using point process and conductance-based simulations, we found that existing methods for determining neuronal connectivity from spike times are highly affected by burst spiking activity, resulting in over- or underestimation of spike transmission. To improve performance, we developed a mathematical framework for decomposing the cross-correlation between two spike trains. We then devised a deconvolution-based algorithm for removing effects of second-order spike train statistics. Deconvolution removed the effect of burst spiking, improving the estimation of neuronal connectivity yielded by state-of-the-art methods. Application of deconvolution to neuronal data recorded from hippocampal region CA1 of freely-moving mice produced higher estimates of spike transmission, in particular when spike trains exhibited bursts. Deconvolution facilitates the precise construction of complex connectivity maps, opening the door to enhanced understanding of the neural mechanisms underlying brain function.
Collapse
Affiliation(s)
- Lidor Spivak
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Amir Levi
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Hadas E Sloin
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Shirly Someck
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Eran Stark
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel.
| |
Collapse
|
5
|
Monosynaptic inference via finely-timed spikes. J Comput Neurosci 2021; 49:131-157. [PMID: 33507429 DOI: 10.1007/s10827-020-00770-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 09/04/2020] [Accepted: 10/19/2020] [Indexed: 10/22/2022]
Abstract
Observations of finely-timed spike relationships in population recordings have been used to support partial reconstruction of neural microcircuit diagrams. In this approach, fine-timescale components of paired spike train interactions are isolated and subsequently attributed to synaptic parameters. Recent perturbation studies strengthen the case for such an inference, yet the complete set of measurements needed to calibrate statistical models is unavailable. To address this gap, we study features of pairwise spiking in a large-scale in vivo dataset where presynaptic neurons were explicitly decoupled from network activity by juxtacellular stimulation. We then construct biophysical models of paired spike trains to reproduce the observed phenomenology of in vivo monosynaptic interactions, including both fine-timescale spike-spike correlations and firing irregularity. A key characteristic of these models is that the paired neurons are coupled by rapidly-fluctuating background inputs. We quantify a monosynapse's causal effect by comparing the postsynaptic train with its counterfactual, when the monosynapse is removed. Subsequently, we develop statistical techniques for estimating this causal effect from the pre- and post-synaptic spike trains. A particular focus is the justification and application of a nonparametric separation of timescale principle to implement synaptic inference. Using simulated data generated from the biophysical models, we characterize the regimes in which the estimators accurately identify the monosynaptic effect. A secondary goal is to initiate a critical exploration of neurostatistical assumptions in terms of biophysical mechanisms, particularly with regards to the challenging but arguably fundamental issue of fast, unobservable nonstationarities in background dynamics.
Collapse
|
6
|
Radosevic M, Willumsen A, Petersen PC, Lindén H, Vestergaard M, Berg RW. Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord. Nat Commun 2019; 10:2937. [PMID: 31270315 PMCID: PMC6610135 DOI: 10.1038/s41467-019-10822-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Accepted: 06/04/2019] [Indexed: 12/12/2022] Open
Abstract
During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.
Collapse
Affiliation(s)
- Marija Radosevic
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
| | - Alex Willumsen
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
| | - Peter C Petersen
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
- Neuroscience Institute, New York University, New York, NY, 10016, USA
| | - Henrik Lindén
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
| | - Mikkel Vestergaard
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark
- Department of Neuroscience, Max Delbrück Center for Molecular Medicine (MDC), 13125, Berlin-Buch, Germany
| | - Rune W Berg
- Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200, Copenhagen N, Denmark.
| |
Collapse
|
7
|
Quaglio P, Rostami V, Torre E, Grün S. Methods for identification of spike patterns in massively parallel spike trains. BIOLOGICAL CYBERNETICS 2018; 112:57-80. [PMID: 29651582 PMCID: PMC5908877 DOI: 10.1007/s00422-018-0755-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 03/26/2018] [Indexed: 06/08/2023]
Abstract
Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assemblies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwise correlations in simultaneous recordings of few neurons. Increasing the number of simultaneously recorded neurons increases the chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled the recording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical tools to be analyzed for correlations, because they raise considerable combinatorial and multiple testing issues. Recently, various of such methods have started to develop. First approaches were based on population or pairwise measures of synchronization, and later led to methods for the detection of various types of higher-order synchronization and of spatio-temporal patterns. The latest techniques combine data mining with analysis of statistical significance. Here, we give a comparative overview of these methods, of their assumptions and of the types of correlations they can detect.
Collapse
Affiliation(s)
- Pietro Quaglio
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany.
| | - Vahid Rostami
- Computational Systems Neuroscience, Institute for Zoology, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany
| | - Emiliano Torre
- Chair of Risk, Safety and Uncertainty Quantification, ETH Zürich, Zurich, Switzerland
- Risk Center, ETH Zürich, Zurich, Switzerland
| | - Sonja Grün
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
- Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
| |
Collapse
|
8
|
Kass RE, Amari SI, Arai K, Brown EN, Diekman CO, Diesmann M, Doiron B, Eden UT, Fairhall AL, Fiddyment GM, Fukai T, Grün S, Harrison MT, Helias M, Nakahara H, Teramae JN, Thomas PJ, Reimers M, Rodu J, Rotstein HG, Shea-Brown E, Shimazaki H, Shinomoto S, Yu BM, Kramer MA. Computational Neuroscience: Mathematical and Statistical Perspectives. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2018; 5:183-214. [PMID: 30976604 PMCID: PMC6454918 DOI: 10.1146/annurev-statistics-041715-033733] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.
Collapse
Affiliation(s)
- Robert E Kass
- Carnegie Mellon University, Pittsburgh, PA, USA, 15213;
| | - Shun-Ichi Amari
- RIKEN Brain Science Institute, Wako, Saitama Prefecture, Japan, 351-0198
| | | | - Emery N Brown
- Massachusetts Institute of Technology, Cambridge, MA, USA, 02139
- Harvard Medical School, Boston, MA, USA, 02115
| | | | - Markus Diesmann
- Jülich Research Centre, Jülich, Germany, 52428
- RWTH Aachen University, Aachen, Germany, 52062
| | - Brent Doiron
- University of Pittsburgh, Pittsburgh, PA, USA, 15260
| | - Uri T Eden
- Boston University, Boston, MA, USA, 02215
| | | | | | - Tomoki Fukai
- RIKEN Brain Science Institute, Wako, Saitama Prefecture, Japan, 351-0198
| | - Sonja Grün
- Jülich Research Centre, Jülich, Germany, 52428
- RWTH Aachen University, Aachen, Germany, 52062
| | | | - Moritz Helias
- Jülich Research Centre, Jülich, Germany, 52428
- RWTH Aachen University, Aachen, Germany, 52062
| | - Hiroyuki Nakahara
- RIKEN Brain Science Institute, Wako, Saitama Prefecture, Japan, 351-0198
| | | | - Peter J Thomas
- Case Western Reserve University, Cleveland, OH, USA, 44106
| | - Mark Reimers
- Michigan State University, East Lansing, MI, USA, 48824
| | - Jordan Rodu
- Carnegie Mellon University, Pittsburgh, PA, USA, 15213;
| | | | | | - Hideaki Shimazaki
- Honda Research Institute Japan, Wako, Saitama Prefecture, Japan, 351-0188
- Kyoto University, Kyoto, Kyoto Prefecture, Japan, 606-8502
| | | | - Byron M Yu
- Carnegie Mellon University, Pittsburgh, PA, USA, 15213;
| | | |
Collapse
|
9
|
English DF, McKenzie S, Evans T, Kim K, Yoon E, Buzsáki G. Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks. Neuron 2017; 96:505-520.e7. [PMID: 29024669 DOI: 10.1016/j.neuron.2017.09.033] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 08/11/2017] [Accepted: 09/20/2017] [Indexed: 10/18/2022]
Abstract
Excitatory control of inhibitory neurons is poorly understood due to the difficulty of studying synaptic connectivity in vivo. We inferred such connectivity through analysis of spike timing and validated this inference using juxtacellular and optogenetic control of presynaptic spikes in behaving mice. We observed that neighboring CA1 neurons had stronger connections and that superficial pyramidal cells projected more to deep interneurons. Connection probability and strength were skewed, with a minority of highly connected hubs. Divergent presynaptic connections led to synchrony between interneurons. Synchrony of convergent presynaptic inputs boosted postsynaptic drive. Presynaptic firing frequency was read out by postsynaptic neurons through short-term depression and facilitation, with individual pyramidal cells and interneurons displaying a diversity of spike transmission filters. Additionally, spike transmission was strongly modulated by prior spike timing of the postsynaptic cell. These results bridge anatomical structure with physiological function.
Collapse
Affiliation(s)
| | - Sam McKenzie
- Neuroscience Institute, New York University, New York, NY 10016, US
| | - Talfan Evans
- Neuroscience Institute, New York University, New York, NY 10016, US
| | | | - Euisik Yoon
- University of Michigan, Ann Arbor, MI 48109, US
| | - György Buzsáki
- Neuroscience Institute, New York University, New York, NY 10016, US; Center for Neural Science, New York University, New York, NY 10016, US.
| |
Collapse
|