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Soldado-Magraner S, Buonomano DV. Neural Sequences and the Encoding of Time. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1455:81-93. [PMID: 38918347 DOI: 10.1007/978-3-031-60183-5_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Converging experimental and computational evidence indicate that on the scale of seconds the brain encodes time through changing patterns of neural activity. Experimentally, two general forms of neural dynamic regimes that can encode time have been observed: neural population clocks and ramping activity. Neural population clocks provide a high-dimensional code to generate complex spatiotemporal output patterns, in which each neuron exhibits a nonlinear temporal profile. A prototypical example of neural population clocks are neural sequences, which have been observed across species, brain areas, and behavioral paradigms. Additionally, neural sequences emerge in artificial neural networks trained to solve time-dependent tasks. Here, we examine the role of neural sequences in the encoding of time, and how they may emerge in a biologically plausible manner. We conclude that neural sequences may represent a canonical computational regime to perform temporal computations.
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Affiliation(s)
| | - Dean V Buonomano
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.
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Cook JR, Li H, Nguyen B, Huang HH, Mahdavian P, Kirchgessner MA, Strassmann P, Engelhardt M, Callaway EM, Jin X. Secondary auditory cortex mediates a sensorimotor mechanism for action timing. Nat Neurosci 2022; 25:330-344. [PMID: 35260862 DOI: 10.1038/s41593-022-01025-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 01/26/2022] [Indexed: 01/08/2023]
Abstract
The ability to accurately determine when to perform an action is a fundamental brain function and vital to adaptive behavior. The behavioral mechanism and neural circuit for action timing, however, remain largely unknown. Using a new, self-paced action timing task in mice, we found that deprivation of auditory, but not somatosensory or visual input, disrupts learned action timing. The hearing effect was dependent on the auditory feedback derived from the animal's own actions, rather than passive environmental cues. Neuronal activity in the secondary auditory cortex was found to be both correlated with and necessary for the proper execution of learned action timing. Closed-loop, action-dependent optogenetic stimulation of the specific task-related neuronal population within the secondary auditory cortex rescued the key features of learned action timing under auditory deprivation. These results unveil a previously underappreciated sensorimotor mechanism in which the secondary auditory cortex transduces self-generated audiomotor feedback to control action timing.
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Affiliation(s)
- Jonathan R Cook
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.,Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Champalimaud Centre for the Unknown, Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Hao Li
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bella Nguyen
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Hsiang-Hsuan Huang
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.,Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Payaam Mahdavian
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Megan A Kirchgessner
- Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA.,Skirball Institute for Biomolecular Medicine, New York University School of Medicine, New York, NY, USA
| | - Patrick Strassmann
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.,Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Max Engelhardt
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Edward M Callaway
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Xin Jin
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA. .,Center for Motor Control and Disease, Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai, China. .,NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China.
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Tupikov Y, Jin DZ. Addition of new neurons and the emergence of a local neural circuit for precise timing. PLoS Comput Biol 2021; 17:e1008824. [PMID: 33730085 PMCID: PMC8007041 DOI: 10.1371/journal.pcbi.1008824] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 03/29/2021] [Accepted: 02/19/2021] [Indexed: 11/28/2022] Open
Abstract
During development, neurons arrive at local brain areas in an extended period of time, but how they form local neural circuits is unknown. Here we computationally model the emergence of a network for precise timing in the premotor nucleus HVC in songbird. We show that new projection neurons, added to HVC post hatch at early stages of song development, are recruited to the end of a growing feedforward network. High spontaneous activity of the new neurons makes them the prime targets for recruitment in a self-organized process via synaptic plasticity. Once recruited, the new neurons fire readily at precise times, and they become mature. Neurons that are not recruited become silent and replaced by new immature neurons. Our model incorporates realistic HVC features such as interneurons, spatial distributions of neurons, and distributed axonal delays. The model predicts that the birth order of the projection neurons correlates with their burst timing during the song. Functions of local neural circuits depend on their specific network structures, but how the networks are wired is unknown. We show that such structures can emerge during development through a self-organized process, during which the network is wired by neuron-by-neuron recruitment. This growth is facilitated by steady supply of immature neurons, which are highly excitable and plastic. We suggest that neuron maturation dynamics is an integral part of constructing local neural circuits.
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Affiliation(s)
- Yevhen Tupikov
- Departments of Physics and Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Dezhe Z. Jin
- Departments of Physics and Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
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Paton JJ, Buonomano DV. The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions. Neuron 2019; 98:687-705. [PMID: 29772201 DOI: 10.1016/j.neuron.2018.03.045] [Citation(s) in RCA: 179] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 02/26/2018] [Accepted: 03/24/2018] [Indexed: 12/15/2022]
Abstract
Timing is critical to most forms of learning, behavior, and sensory-motor processing. Converging evidence supports the notion that, precisely because of its importance across a wide range of brain functions, timing relies on intrinsic and general properties of neurons and neural circuits; that is, the brain uses its natural cellular and network dynamics to solve a diversity of temporal computations. Many circuits have been shown to encode elapsed time in dynamically changing patterns of neural activity-so-called population clocks. But temporal processing encompasses a wide range of different computations, and just as there are different circuits and mechanisms underlying computations about space, there are a multitude of circuits and mechanisms underlying the ability to tell time and generate temporal patterns.
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Affiliation(s)
- Joseph J Paton
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
| | - Dean V Buonomano
- Departments of Neurobiology and Psychology and Brain Research Institute, Integrative Center for Learning and Memory, University of California, Los Angeles, Los Angeles, CA, USA.
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Weissenberger F, Meier F, Lengler J, Einarsson H, Steger A. Long Synfire Chains Emerge by Spike-Timing Dependent Plasticity Modulated by Population Activity. Int J Neural Syst 2017; 27:1750044. [DOI: 10.1142/s0129065717500447] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Sequences of precisely timed neuronal activity are observed in many brain areas in various species. Synfire chains are a well-established model that can explain such sequences. However, it is unknown under which conditions synfire chains can develop in initially unstructured networks by self-organization. This work shows that with spike-timing dependent plasticity (STDP), modulated by global population activity, long synfire chains emerge in sparse random networks. The learning rule fosters neurons to participate multiple times in the chain or in multiple chains. Such reuse of neurons has been experimentally observed and is necessary for high capacity. Sparse networks prevent the chains from being short and cyclic and show that the formation of specific synapses is not essential for chain formation. Analysis of the learning rule in a simple network of binary threshold neurons reveals the asymptotically optimal length of the emerging chains. The theoretical results generalize to simulated networks of conductance-based leaky integrate-and-fire (LIF) neurons. As an application of the emerged chain, we propose a one-shot memory for sequences of precisely timed neuronal activity.
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Affiliation(s)
- Felix Weissenberger
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
| | - Florian Meier
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
| | - Johannes Lengler
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
| | - Hafsteinn Einarsson
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
| | - Angelika Steger
- Department of Computer Science, ETH Zürich, Universitätsstrasse 6, 8092, Zürich, Switzerland
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