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Yuste R, Cossart R, Yaksi E. Neuronal ensembles: Building blocks of neural circuits. Neuron 2024; 112:875-892. [PMID: 38262413 PMCID: PMC10957317 DOI: 10.1016/j.neuron.2023.12.008] [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: 02/14/2022] [Revised: 06/07/2023] [Accepted: 12/13/2023] [Indexed: 01/25/2024]
Abstract
Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.
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Affiliation(s)
- Rafael Yuste
- NeuroTechnology Center, Department of Biological Sciences, Columbia University, New York, NY, USA.
| | - Rosa Cossart
- Inserm, INMED, Turing Center for Living Systems Aix-Marseille University, Marseille, France.
| | - Emre Yaksi
- Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway; Koç University Research Center for Translational Medicine, Koç University School of Medicine, Istanbul, Turkey.
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2
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Lobov SA, Berdnikova ES, Zharinov AI, Kurganov DP, Kazantsev VB. STDP-Driven Rewiring in Spiking Neural Networks under Stimulus-Induced and Spontaneous Activity. Biomimetics (Basel) 2023; 8:320. [PMID: 37504208 PMCID: PMC10807410 DOI: 10.3390/biomimetics8030320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/18/2023] [Accepted: 07/19/2023] [Indexed: 07/29/2023] Open
Abstract
Mathematical and computer simulation of learning in living neural networks have typically focused on changes in the efficiency of synaptic connections represented by synaptic weights in the models. Synaptic plasticity is believed to be the cellular basis for learning and memory. In spiking neural networks composed of dynamical spiking units, a biologically relevant learning rule is based on the so-called spike-timing-dependent plasticity or STDP. However, experimental data suggest that synaptic plasticity is only a part of brain circuit plasticity, which also includes homeostatic and structural plasticity. A model of structural plasticity proposed in this study is based on the activity-dependent appearance and disappearance of synaptic connections. The results of the research indicate that such adaptive rewiring enables the consolidation of the effects of STDP in response to a local external stimulation of a neural network. Subsequently, a vector field approach is used to demonstrate the successive "recording" of spike paths in both functional connectome and synaptic connectome, and finally in the anatomical connectome of the network. Moreover, the findings suggest that the adaptive rewiring could stabilize network dynamics over time in the context of activity patterns' reproducibility. A universal measure of such reproducibility introduced in this article is based on similarity between time-consequent patterns of the special vector fields characterizing both functional and anatomical connectomes.
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Affiliation(s)
- Sergey A. Lobov
- Laboratory of Neurobiomorphic Technologies, The Moscow Institute of Physics and Technology, 117303 Moscow, Russia;
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia; (E.S.B.); (A.I.Z.)
| | - Ekaterina S. Berdnikova
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia; (E.S.B.); (A.I.Z.)
| | - Alexey I. Zharinov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia; (E.S.B.); (A.I.Z.)
| | - Dmitry P. Kurganov
- Laboratory of Neuromodeling, Samara State Medical University, 443079 Samara, Russia;
| | - Victor B. Kazantsev
- Laboratory of Neurobiomorphic Technologies, The Moscow Institute of Physics and Technology, 117303 Moscow, Russia;
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia; (E.S.B.); (A.I.Z.)
- Laboratory of Neuromodeling, Samara State Medical University, 443079 Samara, Russia;
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3
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Swindale NV, Spacek MA, Krause M, Mitelut C. Spontaneous activity in cortical neurons is stereotyped and non-Poisson. Cereb Cortex 2023; 33:6508-6525. [PMID: 36708015 PMCID: PMC10233306 DOI: 10.1093/cercor/bhac521] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/09/2022] [Accepted: 12/10/2022] [Indexed: 01/29/2023] Open
Abstract
Neurons fire even in the absence of sensory stimulation or task demands. Numerous theoretical studies have modeled this spontaneous activity as a Poisson process with uncorrelated intervals between successive spikes and a variance in firing rate equal to the mean. Experimental tests of this hypothesis have yielded variable results, though most have concluded that firing is not Poisson. However, these tests say little about the ways firing might deviate from randomness. Nor are they definitive because many different distributions can have equal means and variances. Here, we characterized spontaneous spiking patterns in extracellular recordings from monkey, cat, and mouse cerebral cortex neurons using rate-normalized spike train autocorrelation functions (ACFs) and a logarithmic timescale. If activity was Poisson, this function should be flat. This was almost never the case. Instead, ACFs had diverse shapes, often with characteristic peaks in the 1-700 ms range. Shapes were stable over time, up to the longest recording periods used (51 min). They did not fall into obvious clusters. ACFs were often unaffected by visual stimulation, though some abruptly changed during brain state shifts. These behaviors may have their origin in the intrinsic biophysics and dendritic anatomy of the cells or in the inputs they receive.
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Affiliation(s)
- Nicholas V Swindale
- Department of Ophthalmology and Visual Sciences, University of British Columbia, 2550 Willow St., Vancouver, BC V5Z 3N9, Canada
| | - Martin A Spacek
- Division of Neurobiology, Department of Biology II, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Matthew Krause
- Montreal Neurological Institute, McGill University, 3801 University St., Montreal, QC H3A 2B4, Canada
| | - Catalin Mitelut
- Institute of Molecular and Clinical Ophthalmology, University of Basel, Mittlere Strasse 91, CH-4031 Basel, Switzerland
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4
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Teichner R, Shomar A, Barak O, Brenner N, Marom S, Meir R, Eytan D. Identifying regulation with adversarial surrogates. Proc Natl Acad Sci U S A 2023; 120:e2216805120. [PMID: 36920920 PMCID: PMC10041131 DOI: 10.1073/pnas.2216805120] [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: 10/10/2022] [Accepted: 02/15/2023] [Indexed: 03/16/2023] Open
Abstract
Homeostasis, the ability to maintain a relatively constant internal environment in the face of perturbations, is a hallmark of biological systems. It is believed that this constancy is achieved through multiple internal regulation and control processes. Given observations of a system, or even a detailed model of one, it is both valuable and extremely challenging to extract the control objectives of the homeostatic mechanisms. In this work, we develop a robust data-driven method to identify these objectives, namely to understand: "what does the system care about?". We propose an algorithm, Identifying Regulation with Adversarial Surrogates (IRAS), that receives an array of temporal measurements of the system and outputs a candidate for the control objective, expressed as a combination of observed variables. IRAS is an iterative algorithm consisting of two competing players. The first player, realized by an artificial deep neural network, aims to minimize a measure of invariance we refer to as the coefficient of regulation. The second player aims to render the task of the first player more difficult by forcing it to extract information about the temporal structure of the data, which is absent from similar "surrogate" data. We test the algorithm on four synthetic and one natural data set, demonstrating excellent empirical results. Interestingly, our approach can also be used to extract conserved quantities, e.g., energy and momentum, in purely physical systems, as we demonstrate empirically.
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Affiliation(s)
- Ron Teichner
- Viterbi Department of Electrical & Computer Engineering, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Aseel Shomar
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Department of Chemical Engineering, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Omri Barak
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Naama Brenner
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Department of Chemical Engineering, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Shimon Marom
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Ron Meir
- Viterbi Department of Electrical & Computer Engineering, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
| | - Danny Eytan
- Network Biology Research Lab, Technion, Israel Institute of Technology, 32000 Haifa, Israel
- Rappaport Faculty of Medicine, Technion, Israel Institute of Technology, 32000 Haifa, Israel
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5
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Berkowitz A. Expanding our horizons: central pattern generation in the context of complex activity sequences. J Exp Biol 2019; 222:222/20/jeb192054. [DOI: 10.1242/jeb.192054] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
ABSTRACT
Central pattern generators (CPGs) are central nervous system (CNS) networks that can generate coordinated output in the absence of patterned sensory input. For decades, this concept was applied almost exclusively to simple, innate, rhythmic movements with essentially identical cycles that repeat continually (e.g. respiration) or episodically (e.g. locomotion). But many natural movement sequences are not simple rhythms, as they include different elements in a complex order, and some involve learning. The concepts and experimental approaches of CPG research have also been applied to the neural control of complex movement sequences, such as birdsong, though this is not widely appreciated. Experimental approaches to the investigation of CPG networks, both for simple rhythms and for complex activity sequences, have shown that: (1) brief activation of the CPG elicits a long-lasting naturalistic activity sequence; (2) electrical stimulation of CPG elements alters the timing of subsequent cycles or sequence elements; and (3) warming or cooling CPG elements respectively speeds up or slows down the rhythm or sequence rate. The CPG concept has also been applied to the activity rhythms of populations of mammalian cortical neurons. CPG concepts and methods might further be applied to a variety of fixed action patterns typically used in courtship, rivalry, nest building and prey capture. These complex movements could be generated by CPGs within CPGs (‘nested’ CPGs). Stereotypical, non-motor, non-rhythmic neuronal activity sequences may also be generated by CPGs. My goal here is to highlight previous applications of the CPG concept to complex but stereotypical activity sequences and to suggest additional possible applications, which might provoke new hypotheses and experiments.
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Affiliation(s)
- Ari Berkowitz
- Department of Biology and Cellular & Behavioral Neurobiology Graduate Program, University of Oklahoma, Norman, OK 73019, USA
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6
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Evidence for Long-Timescale Patterns of Synaptic Inputs in CA1 of Awake Behaving Mice. J Neurosci 2017; 38:1821-1834. [PMID: 29279309 DOI: 10.1523/jneurosci.1519-17.2017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 12/11/2017] [Accepted: 12/18/2017] [Indexed: 12/14/2022] Open
Abstract
Repeated sequences of neural activity are a pervasive feature of neural networks in vivo and in vitro In the hippocampus, sequential firing of many neurons over periods of 100-300 ms reoccurs during behavior and during periods of quiescence. However, it is not known whether the hippocampus produces longer sequences of activity or whether such sequences are restricted to specific network states. Furthermore, whether long repeated patterns of activity are transmitted to single cells downstream is unclear. To answer these questions, we recorded intracellularly from hippocampal CA1 of awake, behaving male mice to examine both subthreshold activity and spiking output in single neurons. In eight of nine recordings, we discovered long (900 ms) reoccurring subthreshold fluctuations or "repeats." Repeats generally were high-amplitude, nonoscillatory events reoccurring with 10 ms precision. Using statistical controls, we determined that repeats occurred more often than would be expected from unstructured network activity (e.g., by chance). Most spikes occurred during a repeat, and when a repeat contained a spike, the spike reoccurred with precision on the order of ≤20 ms, showing that long repeated patterns of subthreshold activity are strongly connected to spike output. Unexpectedly, we found that repeats occurred independently of classic hippocampal network states like theta oscillations or sharp-wave ripples. Together, these results reveal surprisingly long patterns of repeated activity in the hippocampal network that occur nonstochastically, are transmitted to single downstream neurons, and strongly shape their output. This suggests that the timescale of information transmission in the hippocampal network is much longer than previously thought.SIGNIFICANCE STATEMENT We found long (≥900 ms), repeated, subthreshold patterns of activity in CA1 of awake, behaving mice. These repeated patterns ("repeats") occurred more often than expected by chance and with 10 ms precision. Most spikes occurred within repeats and reoccurred with a precision on the order of 20 ms. Surprisingly, there was no correlation between repeat occurrence and classical network states such as theta oscillations and sharp-wave ripples. These results provide strong evidence that long patterns of activity are repeated and transmitted to downstream neurons, suggesting that the hippocampus can generate longer sequences of repeated activity than previously thought.
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7
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Shultz AM, Lee S, Guaraldi M, Shea TB, Yanco HC. Robot-Embodied Neuronal Networks as an Interactive Model of Learning. Open Neurol J 2017; 11:39-47. [PMID: 29151990 PMCID: PMC5678239 DOI: 10.2174/1874205x01711010039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 06/19/2017] [Accepted: 08/03/2017] [Indexed: 11/23/2022] Open
Abstract
Background and Objective: The reductionist approach of neuronal cell culture has been useful for analyses of synaptic signaling. Murine cortical neurons in culture spontaneously form an ex vivo network capable of transmitting complex signals, and have been useful for analyses of several fundamental aspects of neuronal development hitherto difficult to clarify in situ. However, these networks lack the ability to receive and respond to sensory input from the environment as do neurons in vivo. Establishment of these networks in culture chambers containing multi-electrode arrays allows recording of synaptic activity as well as stimulation. Method: This article describes the embodiment of ex vivo neuronal networks neurons in a closed-loop cybernetic system, consisting of digitized video signals as sensory input and a robot arm as motor output. Results: In this system, the neuronal network essentially functions as a simple central nervous system. This embodied network displays the ability to track a target in a naturalistic environment. These findings underscore that ex vivo neuronal networks can respond to sensory input and direct motor output. Conclusion: These analyses may contribute to optimization of neuronal-computer interfaces for perceptive and locomotive prosthetic applications. Ex vivo networks display critical alterations in signal patterns following treatment with subcytotoxic concentrations of amyloid-beta. Future studies including comparison of tracking accuracy of embodied networks prepared from mice harboring key mutations with those from normal mice, accompanied with exposure to Abeta and/or other neurotoxins, may provide a useful model system for monitoring subtle impairment of neuronal function as well as normal and abnormal development.
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Affiliation(s)
| | - Sangmook Lee
- Laboratory for Neuroscience, Department of Biological Sciences University of Massachusetts Lowell, Lowell, MA 01854, USA
| | - Mary Guaraldi
- Laboratory for Neuroscience, Department of Biological Sciences University of Massachusetts Lowell, Lowell, MA 01854, USA
| | - Thomas B Shea
- Laboratory for Neuroscience, Department of Biological Sciences University of Massachusetts Lowell, Lowell, MA 01854, USA
| | - Holly C Yanco
- Robotics Laboratory, Department of Computer Science, USA
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8
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Carrillo-Reid L, Yang W, Kang Miller JE, Peterka DS, Yuste R. Imaging and Optically Manipulating Neuronal Ensembles. Annu Rev Biophys 2017; 46:271-293. [PMID: 28301770 DOI: 10.1146/annurev-biophys-070816-033647] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The neural code that relates the firing of neurons to the generation of behavior and mental states must be implemented by spatiotemporal patterns of activity across neuronal populations. These patterns engage selective groups of neurons, called neuronal ensembles, which are emergent building blocks of neural circuits. We review optical and computational methods, based on two-photon calcium imaging and two-photon optogenetics, to detect, characterize, and manipulate neuronal ensembles in three dimensions. We review data using these methods in the mammalian cortex that demonstrate the existence of neuronal ensembles in the spontaneous and evoked cortical activity in vitro and in vivo. Moreover, two-photon optogenetics enable the possibility of artificially imprinting neuronal ensembles into awake, behaving animals and of later recalling those ensembles selectively by stimulating individual cells. These methods could enable deciphering the neural code and also be used to understand the pathophysiology of and design novel therapies for neurological and mental diseases.
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Affiliation(s)
- Luis Carrillo-Reid
- NeuroTechnology Center, Columbia University, New York, NY 10027.,Department of Biological Sciences, Columbia University, New York, NY 10027
| | - Weijian Yang
- NeuroTechnology Center, Columbia University, New York, NY 10027.,Department of Biological Sciences, Columbia University, New York, NY 10027
| | - Jae-Eun Kang Miller
- NeuroTechnology Center, Columbia University, New York, NY 10027.,Department of Biological Sciences, Columbia University, New York, NY 10027
| | - Darcy S Peterka
- NeuroTechnology Center, Columbia University, New York, NY 10027.,Department of Biological Sciences, Columbia University, New York, NY 10027
| | - Rafael Yuste
- NeuroTechnology Center, Columbia University, New York, NY 10027.,Department of Biological Sciences, Columbia University, New York, NY 10027.,Department of Neuroscience, Columbia University, New York, NY 10027;
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9
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10
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Lee S, Zemianek JM, Shultz A, Vo A, Maron BY, Therrien M, Courtright C, Guaraldi M, Yanco HA, Shea TB. Synaptic signal streams generated by ex vivo neuronal networks contain non-random, complex patterns. Int J Dev Neurosci 2014; 38:184-94. [PMID: 25172170 DOI: 10.1016/j.ijdevneu.2014.08.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2014] [Revised: 08/19/2014] [Accepted: 08/19/2014] [Indexed: 11/27/2022] Open
Abstract
Cultured embryonic neurons develop functional networks that transmit synaptic signals over multiple sequentially connected neurons as revealed by multi-electrode arrays (MEAs) embedded within the culture dish. Signal streams of ex vivo networks contain spikes and bursts of varying amplitude and duration. Despite the random interactions inherent in dissociated cultures, neurons are capable of establishing functional ex vivo networks that transmit signals among synaptically connected neurons, undergo developmental maturation, and respond to exogenous stimulation by alterations in signal patterns. These characteristics indicate that a considerable degree of organization is an inherent property of neurons. We demonstrate herein that (1) certain signal types occur more frequently than others, (2) the predominant signal types change during and following maturation, (3) signal predominance is dependent upon inhibitory activity, and (4) certain signals preferentially follow others in a non-reciprocal manner. These findings indicate that the elaboration of complex signal streams comprised of a non-random distribution of signal patterns is an emergent property of ex vivo neuronal networks.
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Affiliation(s)
- Sangmook Lee
- Center for Neurobiology and Neurodegeneration Research, UMass Lowell, Lowell, MA 01854, United States; Department of Biological Sciences, UMass Lowell, Lowell, MA 01854, United States
| | - Jill M Zemianek
- Center for Neurobiology and Neurodegeneration Research, UMass Lowell, Lowell, MA 01854, United States; Department of Biological Sciences, UMass Lowell, Lowell, MA 01854, United States
| | - Abraham Shultz
- Center for Neurobiology and Neurodegeneration Research, UMass Lowell, Lowell, MA 01854, United States; Department of Computer Science, UMass Lowell, Lowell, MA 01854, United States
| | - Anh Vo
- Center for Neurobiology and Neurodegeneration Research, UMass Lowell, Lowell, MA 01854, United States
| | - Ben Y Maron
- Center for Neurobiology and Neurodegeneration Research, UMass Lowell, Lowell, MA 01854, United States
| | - Mikaela Therrien
- Department of Biological Sciences, UMass Lowell, Lowell, MA 01854, United States
| | - Christina Courtright
- Department of Biological Sciences, UMass Lowell, Lowell, MA 01854, United States
| | - Mary Guaraldi
- Center for Neurobiology and Neurodegeneration Research, UMass Lowell, Lowell, MA 01854, United States; Department of Biological Sciences, UMass Lowell, Lowell, MA 01854, United States
| | - Holly A Yanco
- Center for Neurobiology and Neurodegeneration Research, UMass Lowell, Lowell, MA 01854, United States; Department of Computer Science, UMass Lowell, Lowell, MA 01854, United States
| | - Thomas B Shea
- Center for Neurobiology and Neurodegeneration Research, UMass Lowell, Lowell, MA 01854, United States; Department of Biological Sciences, UMass Lowell, Lowell, MA 01854, United States.
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11
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Luczak A, Maclean JN. Default activity patterns at the neocortical microcircuit level. Front Integr Neurosci 2012; 6:30. [PMID: 22701405 PMCID: PMC3373160 DOI: 10.3389/fnint.2012.00030] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2011] [Accepted: 05/24/2012] [Indexed: 11/17/2022] Open
Abstract
Even in absence of sensory stimuli cortical networks exhibit complex, self-organized activity patterns. While the function of those spontaneous patterns of activation remains poorly understood, recent studies both in vivo and in vitro have demonstrated that neocortical neurons activate in a surprisingly similar sequential order both spontaneously and following input into cortex. For example, neurons that tend to fire earlier within spontaneous bursts of activity also fire earlier than other neurons in response to sensory stimuli. These “default patterns” can last hundreds of milliseconds and are strongly conserved under a variety of conditions. In this paper, we will review recent evidence for these default patterns at the local cortical level. We speculate that cortical architecture imposes common constraints on spontaneous and evoked activity flow, which result in the similarity of the patterns.
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Affiliation(s)
- Artur Luczak
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada
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12
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Intracellular recording in behaving animals. Curr Opin Neurobiol 2011; 22:34-44. [PMID: 22054814 DOI: 10.1016/j.conb.2011.10.013] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2011] [Revised: 09/08/2011] [Accepted: 10/12/2011] [Indexed: 11/20/2022]
Abstract
Electrophysiological recordings from behaving animals provide an unparalleled view into the functional role of individual neurons. Intracellular approaches can be especially revealing as they provide information about a neuron's inputs and intrinsic cellular properties, which together determine its spiking output. Recent technical developments have made intracellular recording possible during an ever-increasing range of behaviors in both head-fixed and freely moving animals. These recordings have yielded fundamental insights into the cellular and circuit mechanisms underlying neural activity during natural behaviors in such areas as sensory perception, motor sequence generation, and spatial navigation, forging a direct link between cellular and systems neuroscience.
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13
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Gilson M, Masquelier T, Hugues E. STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS Comput Biol 2011; 7:e1002231. [PMID: 22046113 PMCID: PMC3203056 DOI: 10.1371/journal.pcbi.1002231] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2011] [Accepted: 09/01/2011] [Indexed: 11/18/2022] Open
Abstract
Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (∼10–20 ms) for sufficiently many inputs (∼100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks. In vivo neural responses to stimuli are known to have a lot of variability across trials. If the same number of spikes is emitted from trial to trial, the neuron is said to be reliable. If the timing of such spikes is roughly preserved across trials, the neuron is said to be precise. Here we demonstrate both analytically and numerically that the well-established Hebbian learning rule of spike-timing-dependent plasticity (STDP) can learn response patterns despite relatively low reliability (Poisson-like variability) and low temporal precision (10–20 ms). These features are in line with many experimental observations, in which a poststimulus time histogram (PSTH) is evaluated over multiple trials. In our model, however, information is extracted from the relative spike times between afferents without the need of an absolute reference time, such as a stimulus onset. Relevantly, recent experiments show that relative timing is often more informative than the absolute timing. Furthermore, the scope of application for our study is not restricted to sensory systems. Taken together, our results suggest a fine temporal resolution for the neural code, and that STDP is an appropriate candidate for encoding and decoding such activity.
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Affiliation(s)
- Matthieu Gilson
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Australia
- Lab for Neural Circuit Theory, Riken Brain Science Insitute, Wako-shi, Saitama, Japan
- * E-mail: (MG); (TM)
| | - Timothée Masquelier
- Unit for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
- * E-mail: (MG); (TM)
| | - Etienne Hugues
- Unit for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
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14
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Abstract
Identifying similar spike-train patterns is a key element in understanding neural coding and computation. For single neurons, similar spike patterns evoked by stimuli are evidence of common coding. Across multiple neurons, similar spike trains indicate potential cell assemblies. As recording technology advances, so does the urgent need for grouping methods to make sense of large-scale datasets of spike trains. Existing methods require specifying the number of groups in advance, limiting their use in exploratory analyses. I derive a new method from network theory that solves this key difficulty: it self-determines the maximum number of groups in any set of spike trains, and groups them to maximize intragroup similarity. This method brings us revealing new insights into the encoding of aversive stimuli by dopaminergic neurons, and the organization of spontaneous neural activity in cortex. I show that the characteristic pause response of a rat's dopaminergic neuron depends on the state of the superior colliculus: when it is inactive, aversive stimuli invoke a single pattern of dopaminergic neuron spiking; when active, multiple patterns occur, yet the spike timing in each is reliable. In spontaneous multineuron activity from the cortex of anesthetized cat, I show the existence of neural ensembles that evolve in membership and characteristic timescale of organization during global slow oscillations. I validate these findings by showing that the method both is remarkably reliable at detecting known groups and can detect large-scale organization of dynamics in a model of the striatum.
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15
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Yassin L, Benedetti BL, Jouhanneau JS, Wen JA, Poulet JFA, Barth AL. An embedded subnetwork of highly active neurons in the neocortex. Neuron 2011; 68:1043-50. [PMID: 21172607 DOI: 10.1016/j.neuron.2010.11.029] [Citation(s) in RCA: 177] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2010] [Indexed: 01/18/2023]
Abstract
VIDEO ABSTRACT Unbiased methods to assess the firing activity of individual neurons in the neocortex have revealed that a large proportion of cells fire at extremely low rates (<0.1 Hz), both in their spontaneous and evoked activity. Thus, firing in neocortical networks appears to be dominated by a small population of highly active neurons. Here, we use a fosGFP transgenic mouse to examine the properties of cells with a recent history of elevated activity. FosGFP-expressing layer 2/3 pyramidal cells fired at higher rates compared to fosGFP(-) neurons, both in vivo and in vitro. Elevated activity could be attributed to increased excitatory and decreased inhibitory drive to fosGFP(+) neurons. Paired-cell recordings indicated that fosGFP(+) neurons had a greater likelihood of being connected to each other. These findings indicate that highly active, interconnected neuronal ensembles are present in the neocortex and suggest these cells may play a role in the encoding of sensory information.
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Affiliation(s)
- Lina Yassin
- Department of Biological Sciences and Center for the Neural Basis of Cognition, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
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Schrader S, Diesmann M, Morrison A. A compositionality machine realized by a hierarchic architecture of synfire chains. Front Comput Neurosci 2011; 4:154. [PMID: 21258641 PMCID: PMC3020397 DOI: 10.3389/fncom.2010.00154] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2010] [Accepted: 12/05/2010] [Indexed: 11/17/2022] Open
Abstract
The composition of complex behavior is thought to rely on the concurrent and sequential activation of simpler action components, or primitives. Systems of synfire chains have previously been proposed to account for either the simultaneous or the sequential aspects of compositionality; however, the compatibility of the two aspects has so far not been addressed. Moreover, the simultaneous activation of primitives has up until now only been investigated in the context of reactive computations, i.e., the perception of stimuli. In this study we demonstrate how a hierarchical organization of synfire chains is capable of generating both aspects of compositionality for proactive computations such as the generation of complex and ongoing action. To this end, we develop a network model consisting of two layers of synfire chains. Using simple drawing strokes as a visualization of abstract primitives, we map the feed-forward activity of the upper level synfire chains to motion in two-dimensional space. Our model is capable of producing drawing strokes that are combinations of primitive strokes by binding together the corresponding chains. Moreover, when the lower layer of the network is constructed in a closed-loop fashion, drawing strokes are generated sequentially. The generated pattern can be random or deterministic, depending on the connection pattern between the lower level chains. We propose quantitative measures for simultaneity and sequentiality, revealing a wide parameter range in which both aspects are fulfilled. Finally, we investigate the spiking activity of our model to propose candidate signatures of synfire chain computation in measurements of neural activity during action execution.
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