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Sachdeva PS, Livezey JA, Dougherty ME, Gu BM, Berke JD, Bouchard KE. Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation. J Neurosci Methods 2021; 358:109195. [PMID: 33905791 DOI: 10.1016/j.jneumeth.2021.109195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/08/2021] [Accepted: 04/10/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND A central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations, and their modulation by external factors, using high-dimensional and stochastic neural recordings. Parametric statistical models (e.g., coupling, encoding, and decoding models), play an instrumental role in accomplishing this goal. However, extracting conclusions from a parametric model requires that it is fit using an inference algorithm capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. The recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation. NEW METHOD We used algorithms based on Union of Intersections, a statistical inference framework based on stability principles, capable of improved selection and estimation. COMPARISON We fit functional coupling, encoding, and decoding models across a battery of neural datasets using both UoI and baseline inference procedures (e.g., ℓ1-penalized GLMs), and compared the structure of their fitted parameters. RESULTS Across recording modality, brain region, and task, we found that UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance. We obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that relied on fewer single-units. CONCLUSIONS Together, these results demonstrate that improved parameter inference, achieved via UoI, reshapes interpretation in diverse neuroscience contexts.
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Affiliation(s)
- Pratik S Sachdeva
- Redwood Center for Theoretical Neuroscience, University of California, Berkeley, 94720, CA, USA; Department of Physics, University of California, Berkeley, 94720, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA
| | - Jesse A Livezey
- Redwood Center for Theoretical Neuroscience, University of California, Berkeley, 94720, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA
| | - Maximilian E Dougherty
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA
| | - Bon-Mi Gu
- Department of Neurology, University of California, San Francisco, San Francisco, 94143, CA, USA
| | - Joshua D Berke
- Department of Neurology, University of California, San Francisco, San Francisco, 94143, CA, USA; Department of Psychiatry; Neuroscience Graduate Program; Kavli Institute for Fundamental Neuroscience; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, 94143, CA, USA
| | - Kristofer E Bouchard
- Redwood Center for Theoretical Neuroscience, University of California, Berkeley, 94720, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA; Computational Resources Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, 94720, CA, USA
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Kobayashi R, Kurita S, Kurth A, Kitano K, Mizuseki K, Diesmann M, Richmond BJ, Shinomoto S. Reconstructing neuronal circuitry from parallel spike trains. Nat Commun 2019; 10:4468. [PMID: 31578320 PMCID: PMC6775109 DOI: 10.1038/s41467-019-12225-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 08/27/2019] [Indexed: 11/23/2022] Open
Abstract
State-of-the-art techniques allow researchers to record large numbers of spike trains in parallel for many hours. With enough such data, we should be able to infer the connectivity among neurons. Here we develop a method for reconstructing neuronal circuitry by applying a generalized linear model (GLM) to spike cross-correlations. Our method estimates connections between neurons in units of postsynaptic potentials and the amount of spike recordings needed to verify connections. The performance of inference is optimized by counting the estimation errors using synthetic data. This method is superior to other established methods in correctly estimating connectivity. By applying our method to rat hippocampal data, we show that the types of estimated connections match the results inferred from other physiological cues. Thus our method provides the means to build a circuit diagram from recorded spike trains, thereby providing a basis for elucidating the differences in information processing in different brain regions. Current techniques have enabled the simultaneous collection of spike train data from large numbers of neurons. Here, the authors report a method to infer the underlying neural circuit connectivity diagram based on a generalized linear model applied to spike cross-correlations between neurons.
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Affiliation(s)
- Ryota Kobayashi
- National Institute of Informatics, Tokyo, 101-8430, Japan.,Department of Informatics, SOKENDAI (The Graduate University for Advanced Studies), Tokyo, 101-8430, Japan
| | - Shuhei Kurita
- Center for Advanced Intelligence Project, RIKEN, Tokyo, 103-0027, Japan
| | - Anno Kurth
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52425, Jülich, Germany.,Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany
| | - Katsunori Kitano
- Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, 525-8577, Japan
| | - Kenji Mizuseki
- Department of Physiology, Osaka City University Graduate School of Medicine, Osaka, 545-8585, Japan
| | - Markus Diesmann
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52425, Jülich, Germany.,Department of Physics, Faculty 1, RWTH Aachen University, Aachen, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, RWTH Aachen University, Aachen, Germany
| | - Barry J Richmond
- Laboratory of Neuropsychology, NIMH/NIH/DHHS, Bethesda, MD, 20814, USA
| | - Shigeru Shinomoto
- Department of Physics, Kyoto University, Kyoto, 606-8502, Japan. .,Brain Information Communication Research Laboratory Group, ATR Institute International, Kyoto, 619-0288, Japan.
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