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Ter Wal M, Tiesinga PHE. Comprehensive characterization of oscillatory signatures in a model circuit with PV- and SOM-expressing interneurons. BIOLOGICAL CYBERNETICS 2021; 115:487-517. [PMID: 34628539 PMCID: PMC8551150 DOI: 10.1007/s00422-021-00894-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 09/06/2021] [Indexed: 05/06/2023]
Abstract
Neural circuits contain a wide variety of interneuron types, which differ in their biophysical properties and connectivity patterns. The two most common interneuron types, parvalbumin-expressing and somatostatin-expressing cells, have been shown to be differentially involved in many cognitive functions. These cell types also show different relationships with the power and phase of oscillations in local field potentials. The mechanisms that underlie the emergence of different oscillatory rhythms in neural circuits with more than one interneuron subtype, and the roles specific interneurons play in those mechanisms, are not fully understood. Here, we present a comprehensive analysis of all possible circuit motifs and input regimes that can be achieved in circuits comprised of excitatory cells, PV-like fast-spiking interneurons and SOM-like low-threshold spiking interneurons. We identify 18 unique motifs and simulate their dynamics over a range of input strengths. Using several characteristics, such as oscillation frequency, firing rates, phase of firing and burst fraction, we cluster the resulting circuit dynamics across motifs in order to identify patterns of activity and compare these patterns to behaviors that were generated in circuits with one interneuron type. In addition to the well-known PING and ING gamma oscillations and an asynchronous state, our analysis identified three oscillatory behaviors that were generated by the three-cell-type motifs only: theta-nested gamma oscillations, stable beta oscillations and theta-locked bursting behavior, which have also been observed in experiments. Our characterization provides a map to interpret experimental activity patterns and suggests pharmacological manipulations or optogenetics approaches to validate these conclusions.
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Affiliation(s)
- Marije Ter Wal
- Department of Neuroinformatics, Donders Institute, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands.
- School of Psychology, University of Birmingham, Edgbaston, B15 2TT, UK.
| | - Paul H E Tiesinga
- Department of Neuroinformatics, Donders Institute, Radboud University, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands
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2
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Viswanathan V, Sheppard JP, Kim BW, Plantz CL, Ying H, Lee MJ, Raman K, Mulhern FJ, Block MP, Calder B, Lee S, Mortensen DT, Blood AJ, Breiter HC. A Quantitative Relationship between Signal Detection in Attention and Approach/Avoidance Behavior. Front Psychol 2017; 8:122. [PMID: 28270776 PMCID: PMC5318395 DOI: 10.3389/fpsyg.2017.00122] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 01/17/2017] [Indexed: 11/13/2022] Open
Abstract
This study examines how the domains of reward and attention, which are often studied as independent processes, in fact interact at a systems level. We operationalize divided attention with a continuous performance task and variables from signal detection theory (SDT), and reward/aversion with a keypress task measuring approach/avoidance in the framework of relative preference theory (RPT). Independent experiments with the same subjects showed a significant association between one SDT and two RPT variables, visualized as a three-dimensional structure. Holding one of these three variables constant, further showed a significant relationship between a loss aversion-like metric from the approach/avoidance task, and the response bias observed during the divided attention task. These results indicate that a more liberal response bias under signal detection (i.e., a higher tolerance for noise, resulting in a greater proportion of false alarms) is associated with higher "loss aversion." Furthermore, our functional model suggests a mechanism for processing constraints with divided attention and reward/aversion. Together, our results argue for a systematic relationship between divided attention and reward/aversion processing in humans.
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Affiliation(s)
- Vijay Viswanathan
- Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA
| | - John P Sheppard
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA
| | - Byoung W Kim
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
| | - Christopher L Plantz
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign Urbana, IL, USA
| | - Hao Ying
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Department of Electrical Engineering, Wayne State UniversityDetroit, MI, USA
| | - Myung J Lee
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
| | - Kalyan Raman
- Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA
| | - Frank J Mulhern
- Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA
| | - Martin P Block
- Medill Integrated Marketing Communications, Northwestern UniversityEvanston, IL, USA; Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA
| | - Bobby Calder
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Department of Marketing, Kellogg School of Management, Northwestern UniversityEvanston, IL, USA
| | - Sang Lee
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
| | - Dale T Mortensen
- Department of Economics, Northwestern University College of Arts and Sciences Evanston, IL, USA
| | - Anne J Blood
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
| | - Hans C Breiter
- Applied Neuromarketing Consortium, Medill, Kellogg, and Feinberg Schools, Northwestern UniversityEvanston, IL, USA; Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of MedicineChicago, IL, USA; Laboratory of Neuroimaging and Genetics, and Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; MGH Center for Translational Research in Prescription Drug Abuse, Department of Anesthesia, Massachusetts General Hospital and Harvard Medical SchoolBoston, MA, USA; Northwestern University and Massachusetts General Hospital Phenotype Genotype Project in Addiction and Mood DisordersChicago, IL, USA
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3
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Mapelli J, Gandolfi D, Giuliani E, Prencipe FP, Pellati F, Barbieri A, D’Angelo E, Bigiani A. The effect of desflurane on neuronal communication at a central synapse. PLoS One 2015; 10:e0123534. [PMID: 25849222 PMCID: PMC4388506 DOI: 10.1371/journal.pone.0123534] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 02/24/2015] [Indexed: 11/18/2022] Open
Abstract
Although general anesthetics are thought to modify critical neuronal functions, their impact on neuronal communication has been poorly examined. We have investigated the effect induced by desflurane, a clinically used general anesthetic, on information transfer at the synapse between mossy fibers and granule cells of cerebellum, where this analysis can be carried out extensively. Mutual information values were assessed by measuring the variability of postsynaptic output in relationship to the variability of a given set of presynaptic inputs. Desflurane synchronized granule cell firing and reduced mutual information in response to physiologically relevant mossy fibers patterns. The decrease in spike variability was due to an increased postsynaptic membrane excitability, which made granule cells more prone to elicit action potentials, and to a strengthened synaptic inhibition, which markedly hampered membrane depolarization. These concomitant actions on granule cells firing indicate that desflurane re-shapes the transfer of information between neurons by providing a less informative neurotransmission rather than completely silencing neuronal activity.
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Affiliation(s)
- Jonathan Mapelli
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena e Reggio Emilia, Modena, Italy
- * E-mail:
| | - Daniela Gandolfi
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena e Reggio Emilia, Modena, Italy
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università di Pavia, Pavia, Italy
| | - Enrico Giuliani
- Dipartimento di Medicina Diagnostica, Clinica e di Sanità Pubblica, Università di Modena e Reggio Emilia, Modena, Modena, Italy
| | - Francesco P. Prencipe
- Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Modena, Italy
| | - Federica Pellati
- Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Modena, Italy
| | - Alberto Barbieri
- Dipartimento di Medicina Diagnostica, Clinica e di Sanità Pubblica, Università di Modena e Reggio Emilia, Modena, Modena, Italy
| | - Egidio D’Angelo
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università di Pavia, Pavia, Italy
- Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy
| | - Albertino Bigiani
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena e Reggio Emilia, Modena, Italy
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4
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Optimal information transfer in the cortex through synchronization. PLoS Comput Biol 2010; 6. [PMID: 20862355 PMCID: PMC2940722 DOI: 10.1371/journal.pcbi.1000934] [Citation(s) in RCA: 123] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2010] [Accepted: 08/17/2010] [Indexed: 11/19/2022] Open
Abstract
In recent experimental work it has been shown that neuronal interactions are modulated by neuronal synchronization and that this modulation depends on phase shifts in neuronal oscillations. This result suggests that connections in a network can be shaped through synchronization. Here, we test and expand this hypothesis using a model network. We use transfer entropy, an information theoretical measure, to quantify the exchanged information. We show that transferred information depends on the phase relation of the signal, that the amount of exchanged information increases as a function of oscillations in the signal and that the speed of the information transfer increases as a function of synchronization. This implies that synchronization makes information transport more efficient. In summary, our results reinforce the hypothesis that synchronization modulates neuronal interactions and provide further evidence that gamma band synchronization has behavioral relevance. Different brain areas are involved in any cognitive task. This implies that information has to be transmitted between different brain areas. Recent experimental results suggest that synchronization plays a crucial role in information exchange between cortical areas. They show that synchronization is capable of rendering network connections effective or ineffective. We study this hypothesis using a neurodynamical model and present results suggesting that both phase and strength of neuronal oscillations in the gamma frequency band influence amount and speed of information transport. We conclude that neuronal synchronization is crucial for information transmission and therefore might even have behavioral relevance.
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Kim BW, Kennedy DN, Lehár J, Lee MJ, Blood AJ, Lee S, Perlis RH, Smoller JW, Morris R, Fava M, Breiter HC. Recurrent, robust and scalable patterns underlie human approach and avoidance. PLoS One 2010; 5:e10613. [PMID: 20532247 PMCID: PMC2879576 DOI: 10.1371/journal.pone.0010613] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2010] [Accepted: 04/08/2010] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Approach and avoidance behavior provide a means for assessing the rewarding or aversive value of stimuli, and can be quantified by a keypress procedure whereby subjects work to increase (approach), decrease (avoid), or do nothing about time of exposure to a rewarding/aversive stimulus. To investigate whether approach/avoidance behavior might be governed by quantitative principles that meet engineering criteria for lawfulness and that encode known features of reward/aversion function, we evaluated whether keypress responses toward pictures with potential motivational value produced any regular patterns, such as a trade-off between approach and avoidance, or recurrent lawful patterns as observed with prospect theory. METHODOLOGY/PRINCIPAL FINDINGS Three sets of experiments employed this task with beautiful face images, a standardized set of affective photographs, and pictures of food during controlled states of hunger and satiety. An iterative modeling approach to data identified multiple law-like patterns, based on variables grounded in the individual. These patterns were consistent across stimulus types, robust to noise, describable by a simple power law, and scalable between individuals and groups. Patterns included: (i) a preference trade-off counterbalancing approach and avoidance, (ii) a value function linking preference intensity to uncertainty about preference, and (iii) a saturation function linking preference intensity to its standard deviation, thereby setting limits to both. CONCLUSIONS/SIGNIFICANCE These law-like patterns were compatible with critical features of prospect theory, the matching law, and alliesthesia. Furthermore, they appeared consistent with both mean-variance and expected utility approaches to the assessment of risk. Ordering of responses across categories of stimuli demonstrated three properties thought to be relevant for preference-based choice, suggesting these patterns might be grouped together as a relative preference theory. Since variables in these patterns have been associated with reward circuitry structure and function, they may provide a method for quantitative phenotyping of normative and pathological function (e.g., psychiatric illness).
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Affiliation(s)
- Byoung Woo Kim
- Motivation and Emotion Neuroscience Collaboration (MENC), Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - David N. Kennedy
- Center for Morphometric Analysis, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Joseph Lehár
- Department of Bioinformatics, Boston University, Boston, Massachusetts, United States of America
| | - Myung Joo Lee
- Motivation and Emotion Neuroscience Collaboration (MENC), Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Anne J. Blood
- Motivation and Emotion Neuroscience Collaboration (MENC), Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Sang Lee
- Motivation and Emotion Neuroscience Collaboration (MENC), Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Roy H. Perlis
- Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Psychiatric and Neurodevelopmental Genetics Unit of the Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit of the Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Robert Morris
- Motivation and Emotion Neuroscience Collaboration (MENC), Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Maurizio Fava
- Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Hans C. Breiter
- Motivation and Emotion Neuroscience Collaboration (MENC), Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Mood and Motor Control Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
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Hirata Y, Aihara K. Representing spike trains using constant sampling intervals. J Neurosci Methods 2009; 183:277-86. [PMID: 19583980 DOI: 10.1016/j.jneumeth.2009.06.030] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2009] [Revised: 06/26/2009] [Accepted: 06/27/2009] [Indexed: 10/20/2022]
Abstract
Sensory neurons encode external information by a series of times of action potentials, which is called a spike train. However, since it is a point process, it is hard to analyze. Here we propose a method for converting a spike train into a real-valued time series with a fixed sampling interval under the assumption of temporal codes. The proposed method yields time series that represent encoded signals. Especially when the method is applied to spike trains generated using integrate-and-fire models, the yielded time series look very similar to those of encoded information. The method works robustly even when a spike train is contaminated with noise. Since unlike filters it does not use its original signals for the conversion, the proposed method can be widely used for investigating spike train data in the real world.
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7
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Kayser C, Montemurro MA, Logothetis NK, Panzeri S. Spike-Phase Coding Boosts and Stabilizes Information Carried by Spatial and Temporal Spike Patterns. Neuron 2009; 61:597-608. [PMID: 19249279 DOI: 10.1016/j.neuron.2009.01.008] [Citation(s) in RCA: 321] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2008] [Revised: 10/30/2008] [Accepted: 01/12/2009] [Indexed: 10/21/2022]
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McLelland D, Paulsen O. Neuronal oscillations and the rate-to-phase transform: mechanism, model and mutual information. J Physiol 2008; 587:769-85. [PMID: 19103680 PMCID: PMC2669970 DOI: 10.1113/jphysiol.2008.164111] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Theoretical and experimental studies suggest that oscillatory modes of processing play an important role in neuronal computations. One well supported idea is that the net excitatory input during oscillations will be reported in the phase of firing, a ‘rate-to-phase transform’, and that this transform might enable a temporal code. Here, we investigate the efficiency of this code at the level of fundamental single cell computations. We first develop a general framework for the understanding of the rate-to-phase transform as implemented by single neurons. Using whole cell patch-clamp recordings of rat hippocampal pyramidal neurons in vitro, we investigated the relationship between tonic excitation and phase of firing during simulated theta frequency (5 Hz) and gamma frequency (40 Hz) oscillations, over a range of physiological firing rates. During theta frequency oscillations, the phase of the first spike per cycle was a near-linear function of tonic excitation, advancing through a full 180 deg, from the peak to the trough of the oscillation cycle as excitation increased. In contrast, this relationship was not apparent for gamma oscillations, during which the phase of firing was virtually independent of the level of tonic excitatory input within the range of physiological firing rates. We show that a simple analytical model can substantially capture this behaviour, enabling generalization to other oscillatory states and cell types. The capacity of such a transform to encode information is limited by the temporal precision of neuronal activity. Using the data from our whole cell recordings, we calculated the information about the input available in the rate or phase of firing, and found the phase code to be significantly more efficient. Thus, temporal modes of processing can enable neuronal coding to be inherently more efficient, thereby allowing a reduction in processing time or in the number of neurons required.
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Affiliation(s)
- Douglas McLelland
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
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9
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Testing a neural coding hypothesis using surrogate data. J Neurosci Methods 2008; 172:312-22. [PMID: 18565591 DOI: 10.1016/j.jneumeth.2008.05.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2007] [Revised: 04/21/2008] [Accepted: 05/08/2008] [Indexed: 11/24/2022]
Abstract
Determining how a particular neuron, or population of neurons, encodes information in their spike trains is not a trivial problem, because multiple coding schemes exist and are not necessarily mutually exclusive. Coding schemes generally fall into one of two broad categories, which we refer to as rate and temporal coding. In rate coding schemes, information is encoded in the variations of the average firing rate of the spike train. In contrast, in temporal coding schemes, information is encoded in the specific timing of the individual spikes that comprise the train. Here, we describe a method for testing the presence of temporal encoding of information. Suppose that a set of original spike trains is given. First, surrogate spike trains are generated by randomizing each of the original spike trains subject to the following constraints: the local average firing rate is approximately preserved, while the overall average firing rate and the distribution of primary interspike intervals are perfectly preserved. These constraints ensure that any rate coding of information present in the original spike trains is preserved in the members of the surrogate population. The null-hypothesis is rejected when additional information is found to be present in the original spike trains, implying that temporal coding is present. The method is validated using artificial data, and then demonstrated using real neuronal data.
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10
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Tiesinga P, Fellous JM, Sejnowski TJ. Regulation of spike timing in visual cortical circuits. Nat Rev Neurosci 2008; 9:97-107. [PMID: 18200026 PMCID: PMC2868969 DOI: 10.1038/nrn2315] [Citation(s) in RCA: 250] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.
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Affiliation(s)
- Paul Tiesinga
- Physics and Astronomy Department, University of North Carolina at Chapel Hill, North Carolina 27599-3255, USA.
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11
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Abstract
Recent experimental and theoretical investigations have made considerable advances in three major areas relating to the structural basis of quantitative cortical microcircuit theory. The first concerns the nature of the cellular units, encompassing the increasingly precise identification and progressively more complete listing of the individual cellular species that constitute the various cortical networks. The second element addresses the problem of heterogeneity, including the demonstration of the importance of cell to cell variability within defined interneuronal populations and the application of the Shannon-Wiener diversity index for the quantitative assessment of the number and relative abundance of interneuronal species. The third component relates to the discovery of basic topological principles underlying the circuit wiring, revealing a surprising order in the architectural design of networks. These new advances deepen our understanding of the computational principles embedded in cortical microcircuits, and they also provide novel opportunities for building realistic models of mammalian cortical microcircuits.
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Affiliation(s)
- Csaba Földy
- Department of Anatomy and Neurobiology, University of California, Irvine, CA 92697-1280, USA
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12
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Abstract
This work investigated the roles of spontaneous synchronized rhythmic oscillations in information processing in a globally coupled neural network. We found that in the encoding of input aperiodic signals, there exists a range of optimal synchronous states in the network, where the information transmission rate and coding efficiency of the network are maximized. Our results indicate that it is not the weaker or stronger but an appropriate synchronous state may be of more functional significance in sensory encoding.
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Affiliation(s)
- Yuguo Yu
- Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
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13
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Tiesinga PHE. Chaos-induced modulation of reliability boosts output firing rate in downstream cortical areas. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2004; 69:031912. [PMID: 15089327 DOI: 10.1103/physreve.69.031912] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2003] [Indexed: 05/24/2023]
Abstract
The reproducibility of neural spike train responses to an identical stimulus across different presentations (trials) has been studied extensively. Reliability, the degree of reproducibility of spike trains, was found to depend in part on the amplitude and frequency content of the stimulus [J. Hunter and J. Milton, J. Neurophysiol. 90, 387 (2003)]. The responses across different trials can sometimes be interpreted as the response of an ensemble of similar neurons to a single stimulus presentation. How does the reliability of the activity of neural ensembles affect information transmission between different cortical areas? We studied a model neural system consisting of two ensembles of neurons with Hodgkin-Huxley-type channels. The first ensemble was driven by an injected sinusoidal current that oscillated in the gamma-frequency range (40 Hz) and its output spike trains in turn drove the second ensemble by fast excitatory synaptic potentials with short term depression. We determined the relationship between the reliability of the first ensemble and the response of the second ensemble. In our paradigm the neurons in the first ensemble were initially in a chaotic state with unreliable and imprecise spike trains. The neurons became entrained to the oscillation and responded reliably when the stimulus power was increased by less than 10%. The firing rate of the first ensemble increased by 30%, whereas that of the second ensemble could increase by an order of magnitude. We also determined the response of the second ensemble when its input spike trains, which had non-Poisson statistics, were replaced by an equivalent ensemble of Poisson spike trains. The resulting output spike trains were significantly different from the original response, as assessed by the metric introduced by Victor and Purpura [J. Neurophysiol. 76, 1310 (1996)]. These results are a proof of principle that weak temporal modulations in the power of gamma-frequency oscillations in a given cortical area can strongly affect firing rate responses downstream by way of reliability in spite of rather modest changes in firing rate in the originating area.
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Affiliation(s)
- P H E Tiesinga
- Department of Physics & Astronomy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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14
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Tiesinga PHE. Precision and reliability of periodically and quasiperiodically driven integrate-and-fire neurons. PHYSICAL REVIEW E 2002; 65:041913. [PMID: 12005879 DOI: 10.1103/physreve.65.041913] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2001] [Indexed: 11/07/2022]
Abstract
Neurons in the brain communicate via trains of all-or-none electric events known as spikes. How the brain encodes information using spikes-the neural code-remains elusive. Here the robustness against noise of stimulus-induced neural spike trains is studied in terms of attractors and bifurcations. The dynamics of model neurons converges after a transient onto an attractor yielding a reproducible sequence of spike times. At a bifurcation point the spike times on the attractor change discontinuously when a parameter is varied. Reliability, the stability of the attractor against noise, is reduced when the neuron operates close to a bifurcation point. We determined using analytical spike-time maps the attractor and bifurcation structure of an integrate-and-fire model neuron driven by a periodic or a quasiperiodic piecewise constant current and investigated the stability of attractors against noise. The integrate-and-fire model neuron became mode locked to the periodic current with a rational winding number p/q and produced p spikes per q cycles. There were q attractors. p:q mode-locking regions formed Arnold tongues. In the model, reliability was the highest during 1:1 mode locking when there was only one attractor, as was also observed in recent experiments. The quasiperiodically driven neuron mode locked to either one of the two drive periods, or to a linear combination of both of them. Mode-locking regions were organized in Arnold tongues and reliability was again highest when there was only one attractor. These results show that neuronal reliability in response to the rhythmic drive generated by synchronized networks of neurons is profoundly influenced by the location of the Arnold tongues in parameter space.
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Affiliation(s)
- P H E Tiesinga
- Sloan-Swartz Center for Theoretical Neurobiology and Computational Neurobiology Laboratory, Salk Institute, 10010 North Torrey Pines Road, La Jolla, California 92037, USA
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