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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.
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2
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Affective memory rehearsal with temporal sequences in amygdala neurons. Nat Neurosci 2019; 22:2050-2059. [DOI: 10.1038/s41593-019-0542-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 10/18/2019] [Indexed: 11/09/2022]
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3
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Saxena S, Cunningham JP. Towards the neural population doctrine. Curr Opin Neurobiol 2019; 55:103-111. [DOI: 10.1016/j.conb.2019.02.002] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 01/30/2019] [Accepted: 02/07/2019] [Indexed: 01/06/2023]
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4
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Paninski L, Cunningham JP. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience. Curr Opin Neurobiol 2019; 50:232-241. [PMID: 29738986 DOI: 10.1016/j.conb.2018.04.007] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 03/12/2018] [Accepted: 04/06/2018] [Indexed: 01/01/2023]
Abstract
Modern large-scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single-neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control-developed in lockstep with advances in experimental neurotechnology-promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time-series data with single-neuronal precision.
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Affiliation(s)
- L Paninski
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States; Department of Neuroscience, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States.
| | - J P Cunningham
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States
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5
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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.
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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;
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6
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Dechery JB, MacLean JN. Emergent cortical circuit dynamics contain dense, interwoven ensembles of spike sequences. J Neurophysiol 2017; 118:1914-1925. [PMID: 28724786 DOI: 10.1152/jn.00394.2017] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 07/05/2017] [Accepted: 07/14/2017] [Indexed: 01/30/2023] Open
Abstract
Temporal codes are theoretically powerful encoding schemes, but their precise form in the neocortex remains unknown in part because of the large number of possible codes and the difficulty in disambiguating informative spikes from statistical noise. A biologically plausible and computationally powerful temporal coding scheme is the Hebbian assembly phase sequence (APS), which predicts reliable propagation of spikes between functionally related assemblies of neurons. Here, we sought to measure the inherent capacity of neocortical networks to produce reliable sequences of spikes, as would be predicted by an APS code. To record microcircuit activity, the scale at which computation is implemented, we used two-photon calcium imaging to densely sample spontaneous activity in murine neocortical networks ex vivo. We show that the population spike histogram is sufficient to produce a spatiotemporal progression of activity across the population. To more comprehensively evaluate the capacity for sequential spiking that cannot be explained by the overall population spiking, we identify statistically significant spike sequences. We found a large repertoire of sequence spikes that collectively comprise the majority of spiking in the circuit. Sequences manifest probabilistically and share neuron membership, resulting in unique ensembles of interwoven sequences characterizing individual spatiotemporal progressions of activity. Distillation of population dynamics into its constituent sequences provides a way to capture trial-to-trial variability and may prove to be a powerful decoding substrate in vivo. Informed by these data, we suggest that the Hebbian APS be reformulated as interwoven sequences with flexible assembly membership due to shared overlapping neurons.NEW & NOTEWORTHY Neocortical computation occurs largely within microcircuits comprised of individual neurons and their connections within small volumes (<500 μm3). We found evidence for a long-postulated temporal code, the Hebbian assembly phase sequence, by identifying repeated and co-occurring sequences of spikes. Variance in population activity across trials was explained in part by the ensemble of active sequences. The presence of interwoven sequences suggests that neuronal assembly structure can be variable and is determined by previous activity.
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Affiliation(s)
- Joseph B Dechery
- 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, Illinois
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Platkiewicz J, Stark E, Amarasingham A. Spike-Centered Jitter Can Mistake Temporal Structure. Neural Comput 2017; 29:783-803. [PMID: 28095192 DOI: 10.1162/neco_a_00927] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Jitter-type spike resampling methods are routinely applied in neurophysiology for detecting temporal structure in spike trains (point processes). Several variations have been proposed. The concern has been raised, based on numerical experiments involving Poisson spike processes, that such procedures can be conservative. We study the issue and find it can be resolved by reemphasizing the distinction between spike-centered (basic) jitter and interval jitter. Focusing on spiking processes with no temporal structure, interval jitter generates an exact hypothesis test, guaranteeing valid conclusions. In contrast, such a guarantee is not available for spike-centered jitter. We construct explicit examples in which spike-centered jitter hallucinates temporal structure, in the sense of exaggerated false-positive rates. Finally, we illustrate numerically that Poisson approximations to jitter computations, while computationally efficient, can also result in inaccurate hypothesis tests. We highlight the value of classical statistical frameworks for guiding the design and interpretation of spike resampling methods.
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Affiliation(s)
- Jonathan Platkiewicz
- Department of Mathematics, City College of New York, City University of New York, NY 10031, U.S.A., and NYU Neuroscience Institute, School of Medicine, New York University, New York, NY 10016, U.S.A.
| | - Eran Stark
- Department of Physiology and Pharmacology, Sackler Faculty of Medicine, and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Asohan Amarasingham
- Department of Mathematics, City University of New York, New York, NY 10031, U.S.A., and Departments of Biology, Computer Science, and Psychology, Graduate Center, City University of New York, New York, NY 10016, U.S.A.
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Truccolo W. From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining. JOURNAL OF PHYSIOLOGY, PARIS 2016; 110:336-347. [PMID: 28336305 PMCID: PMC5610574 DOI: 10.1016/j.jphysparis.2017.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 02/20/2017] [Accepted: 02/26/2017] [Indexed: 01/15/2023]
Abstract
This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles.
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Affiliation(s)
- Wilson Truccolo
- Department of Neuroscience and Institute for Brain Science, Brown University, Providence, USA; Center for Neurorestoration and Neurotechnology, U.S. Department of Veterans Affairs, Providence, USA.
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Tal I, Abeles M. Temporal accuracy of human cortico-cortical interactions. J Neurophysiol 2016; 115:1810-20. [PMID: 26843604 PMCID: PMC4869482 DOI: 10.1152/jn.00956.2015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 01/29/2016] [Indexed: 11/22/2022] Open
Abstract
The precision in space and time of interactions among multiple cortical sites was evaluated by examining repeating precise spatiotemporal patterns of instances in which cortical currents showed brief amplitude undulations. The amplitudes of the cortical current dipoles were estimated by applying a variant of synthetic aperture magnetometry to magnetoencephalographic (MEG) recordings of subjects tapping to metric auditory rhythms of drum beats. Brief amplitude undulations were detected in the currents by template matching at a rate of 2–3 per second. Their timing was treated as point processes, and precise spatiotemporal patterns were searched for. By randomly teetering these point processes within a time window W, we estimated the accuracy of the timing of these brief amplitude undulations and compared the results with those obtained by applying the same analysis to traces composed of random numbers. The results demonstrated that the timing accuracy of patterns was better than 3 ms. Successful classification of two different cognitive processes based on these patterns suggests that at least some of the repeating patterns are specific to a cognitive process.
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Affiliation(s)
- Idan Tal
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel; and
| | - Moshe Abeles
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel; and The Hebrew University of Jerusalem, Jerusalem, Israel
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10
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Ambiguity and nonidentifiability in the statistical analysis of neural codes. Proc Natl Acad Sci U S A 2015; 112:6455-60. [PMID: 25934918 DOI: 10.1073/pnas.1506400112] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, "Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?" For another example, "How much of a neuron's observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?" However, a neuron's theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature.
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11
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Abstract
Seizures are classically characterized as the expression of hypersynchronous neural activity, yet the true degree of synchrony in neuronal spiking (action potentials) during human seizures remains a fundamental question. We quantified the temporal precision of spike synchrony in ensembles of neocortical neurons during seizures in people with pharmacologically intractable epilepsy. Two seizure types were analyzed: those characterized by sustained gamma (∼40-60 Hz) local field potential (LFP) oscillations or by spike-wave complexes (SWCs; ∼3 Hz). Fine (<10 ms) temporal synchrony was rarely present during gamma-band seizures, where neuronal spiking remained highly irregular and asynchronous. In SWC seizures, phase locking of neuronal spiking to the SWC spike phase induced synchrony at a coarse 50-100 ms level. In addition, transient fine synchrony occurred primarily during the initial ∼20 ms period of the SWC spike phase and varied across subjects and seizures. Sporadic coherence events between neuronal population spike counts and LFPs were observed during SWC seizures in high (∼80 Hz) gamma-band and during high-frequency oscillations (∼130 Hz). Maximum entropy models of the joint neuronal spiking probability, constrained only on single neurons' nonstationary coarse spiking rates and local network activation, explained most of the fine synchrony in both seizure types. Our findings indicate that fine neuronal ensemble synchrony occurs mostly during SWC, not gamma-band, seizures, and primarily during the initial phase of SWC spikes. Furthermore, these fine synchrony events result mostly from transient increases in overall neuronal network spiking rates, rather than changes in precise spiking correlations between specific pairs of neurons.
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