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Balaguer-Ballester E, Nogueira R, Abofalia JM, Moreno-Bote R, Sanchez-Vives MV. Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions. PLoS Comput Biol 2020; 16:e1007862. [PMID: 32579563 PMCID: PMC7313741 DOI: 10.1371/journal.pcbi.1007862] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 04/09/2020] [Indexed: 12/03/2022] Open
Abstract
Shared neuronal variability has been shown to modulate cognitive processing. However, the relationship between shared variability and behavioral performance is heterogeneous and complex in frontal areas such as the orbitofrontal cortex (OFC). Mounting evidence shows that single-units in OFC encode a detailed cognitive map of task-space events, but the existence of a robust neuronal ensemble coding for the predictability of choice outcome is less established. Here, we hypothesize that the coding of foreseeable outcomes is potentially unclear from the analysis of units activity and their pairwise correlations. However, this code might be established more conclusively when higher-order neuronal interactions are mapped to the choice outcome. As a case study, we investigated the trial-to-trial shared variability of neuronal ensemble activity during a two-choice interval-discrimination task in rodent OFC, specifically designed such that a lose-switch strategy is optimal by repeating the rewarded stimulus in the upcoming trial. Results show that correlations among triplets are higher during correct choices with respect to incorrect ones, and that this is sustained during the entire trial. This effect is not observed for pairwise nor for higher than third-order correlations. This scenario is compatible with constellations of up to three interacting units assembled during trials in which the task is performed correctly. More interestingly, a state-space spanned by such constellations shows that only correct outcome states that can be successfully predicted are robust over 100 trials of the task, and thus they can be accurately decoded. However, both incorrect and unpredictable outcome representations were unstable and thus non-decodeable, due to spurious negative correlations. Our results suggest that predictability of successful outcomes, and hence the optimal behavioral strategy, can be mapped out in OFC ensemble states reliable over trials of the task, and revealed by sufficiency complex neuronal interactions. Neuronal responses can differ substantially during repetitions of the same tasks; however, they are often coordinated (shared) across multiple neighboring neurons. Such correlation between neurons has been related to the capacity of the brain to take decisions, but specifically how this relation is established is still under study. In this work, we address this question by focusing on an intriguing case study, the orbitofrontal cortex, since this brain area has been found in various studies to be useful for decision-making. Here, we question whether orchestrated groups of neurons encode sufficient information for optimizing their decision strategy; that is, whether the outcome of a choice can be predicted or not on the basis of previous experience. We thus designed a decision-making task for a rat in which some of the correct choices can be predicted. We found that only successful outcomes that can actually be predicted were robustly encoded over time. This finding was shown by analyzing sufficiently complex interactions between three neurons, whilst more complex orchestrations did not add further insights. Thus, we propose that coordinated responses of up to three neurons in the OFC could contribute to the capacity of the animal to take the optimal decision.
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Affiliation(s)
- Emili Balaguer-Ballester
- Department of Computing and Informatics, Faculty of Science and Technology, Bournemouth University, Poole, United Kingdom
- Bernstein Center for Computational Neuroscience, Medical Faculty Mannheim and Heidelberg University, Mannheim, Germany
- * E-mail:
| | - Ramon Nogueira
- Center for Theoretical Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
| | - Juan M. Abofalia
- IDIBAPS (Institut d’Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain
| | - Ruben Moreno-Bote
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Center for Brain and Cognition, Mercé Rodoreda building (Ciutadella campus), Barcelona, Spain
- Serra Húnter Fellow Programme, Universitat Pompeu Fabra, Barcelona, Spain
| | - Maria V. Sanchez-Vives
- IDIBAPS (Institut d’Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain
- ICREA (Institució Catalana de Recerca i Estudis Avançats), Barcelona, Spain
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Ghanbari A, Lee CM, Read HL, Stevenson IH. Modeling stimulus-dependent variability improves decoding of population neural responses. J Neural Eng 2019; 16:066018. [PMID: 31404915 DOI: 10.1088/1741-2552/ab3a68] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. APPROACH In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor = 1). Here we use two alternative models-the Conway-Maxwell-Poisson (CMP) model and negative binomial (NB) model-to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor > 1) or, for the CMP model, less than the mean (Fano factor < 1). MAIN RESULTS We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4%-12% compared to the Poisson model. SIGNIFICANCE Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.
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Affiliation(s)
- Abed Ghanbari
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States of America
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Rodríguez AR, O'Neill KM, Swiatkowski P, Patel MV, Firestein BL. Overexpression of cypin alters dendrite morphology, single neuron activity, and network properties via distinct mechanisms. J Neural Eng 2019; 15:016020. [PMID: 29091046 DOI: 10.1088/1741-2552/aa976a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
OBJECTIVE This study investigates the effect that overexpression of cytosolic PSD-95 interactor (cypin), a regulator of synaptic PSD-95 protein localization and a core regulator of dendrite branching, exerts on the electrical activity of rat hippocampal neurons and networks. APPROACH We cultured rat hippocampal neurons and used lipid-mediated transfection and lentiviral gene transfer to achieve high levels of cypin or cypin mutant (cypinΔPDZ; PSD-95 non-binding) expression cellularly and network-wide, respectively. MAIN RESULTS Our analysis revealed that although overexpression of cypin and cypinΔPDZ increase dendrite numbers and decrease spine density, cypin and cypinΔPDZ distinctly regulate neuronal activity. At the single cell level, cypin promotes decreases in bursting activity while cypinΔPDZ reduces sEPSC frequency and further decreases bursting compared to cypin. At the network level, by using the Fano factor as a measure of spike count variability, cypin overexpression results in an increase in variability of spike count, and this effect is abolished when cypin cannot bind PSD-95. This variability is also dependent on baseline activity levels and on mean spike rate over time. Finally, our spike sorting data show that overexpression of cypin results in a more complex distribution of spike waveforms and that binding to PSD-95 is essential for this complexity. SIGNIFICANCE Our data suggest that dendrite morphology does not play a major role in cypin action on electrical activity.
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Affiliation(s)
- Ana R Rodríguez
- Department of Cell Biology and Neuroscience, Rutgers University, 604 Allison Road, Piscataway, NJ 08854, United States of America. Graduate Program in Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, United States of America
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Riehle A, Brochier T, Nawrot M, Grün S. Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex. Front Neural Circuits 2018; 12:52. [PMID: 30050415 PMCID: PMC6052126 DOI: 10.3389/fncir.2018.00052] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 06/15/2018] [Indexed: 11/13/2022] Open
Abstract
Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependance. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the coefficient of variation (CV), reflect variability on long and short time scales, respectively. We co-analyzed FF and the local coefficient of variation (CV2) in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that the FF significantly decreases from wait to movement, while the CV2 increases. The more regular firing (expressed by a low CV2) during wait is related to an increased power of local field potential (LFP) beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF ≈ CV2. This expectation was met during movement, but not during wait where FF ≫ CV22. Our interpretation is that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (expressed by a high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. Our interpretation is in the light of recent computational models that generate non-stationary network conditions.
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Affiliation(s)
- Alexa Riehle
- UMR7289 Institut de Neurosciences de la Timone (INT), Centre National de la Recherche Scientifique (CNRS)-Aix-Marseille Université (AMU), Marseille, France.,Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Brain Institute I, Forschungszentrum Jülich, Jülich, Germany
| | - Thomas Brochier
- UMR7289 Institut de Neurosciences de la Timone (INT), Centre National de la Recherche Scientifique (CNRS)-Aix-Marseille Université (AMU), Marseille, France
| | - Martin Nawrot
- Computational Systems Neuroscience, Institute for Zoology, University of Cologne, Cologne, Germany
| | - Sonja Grün
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Brain Institute I, Forschungszentrum Jülich, Jülich, Germany.,RIKEN Brain Science Institute (BSI), Wako, Japan.,Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany
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Strait DL, Slater J, O'Connell S, Kraus N. Music training relates to the development of neural mechanisms of selective auditory attention. Dev Cogn Neurosci 2015; 12:94-104. [PMID: 25660985 PMCID: PMC6989776 DOI: 10.1016/j.dcn.2015.01.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2014] [Revised: 01/06/2015] [Accepted: 01/06/2015] [Indexed: 11/26/2022] Open
Abstract
Does music training shape the development of neural mechanisms of auditory attention? We compared cortical responses to attended speech in child and adult musicians and nonmusicians. Musician children and adults had less prefrontal auditory response variability during attention.
Selective attention decreases trial-to-trial variability in cortical auditory-evoked activity. This effect increases over the course of maturation, potentially reflecting the gradual development of selective attention and inhibitory control. Work in adults indicates that music training may alter the development of this neural response characteristic, especially over brain regions associated with executive control: in adult musicians, attention decreases variability in auditory-evoked responses recorded over prefrontal cortex to a greater extent than in nonmusicians. We aimed to determine whether this musician-associated effect emerges during childhood, when selective attention and inhibitory control are under development. We compared cortical auditory-evoked variability to attended and ignored speech streams in musicians and nonmusicians across three age groups: preschoolers, school-aged children and young adults. Results reveal that childhood music training is associated with reduced auditory-evoked response variability recorded over prefrontal cortex during selective auditory attention in school-aged child and adult musicians. Preschoolers, on the other hand, demonstrate no impact of selective attention on cortical response variability and no musician distinctions. This finding is consistent with the gradual emergence of attention during this period and may suggest no pre-existing differences in this attention-related cortical metric between children who undergo music training and those who do not.
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Affiliation(s)
- Dana L Strait
- Auditory Neuroscience Laboratory, Northwestern University, Evanston, IL, USA; Institute for Neuroscience, Northwestern University, Chicago, IL, USA
| | - Jessica Slater
- Auditory Neuroscience Laboratory, Northwestern University, Evanston, IL, USA; Department of Communication Sciences, Northwestern University, Evanston, IL, USA
| | - Samantha O'Connell
- Auditory Neuroscience Laboratory, Northwestern University, Evanston, IL, USA
| | - Nina Kraus
- Auditory Neuroscience Laboratory, Northwestern University, Evanston, IL, USA; Institute for Neuroscience, Northwestern University, Chicago, IL, USA; Department of Communication Sciences, Northwestern University, Evanston, IL, USA; Department of Neurobiology and Physiology, Northwestern University, Evanston, IL, USA; Department of Otolaryngology, Northwestern University, Chicago, IL, USA.
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Meyer AF, Diepenbrock JP, Ohl FW, Anemüller J. Temporal variability of spectro-temporal receptive fields in the anesthetized auditory cortex. Front Comput Neurosci 2014; 8:165. [PMID: 25566049 PMCID: PMC4274980 DOI: 10.3389/fncom.2014.00165] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2014] [Accepted: 11/30/2014] [Indexed: 11/13/2022] Open
Abstract
Temporal variability of neuronal response characteristics during sensory stimulation is a ubiquitous phenomenon that may reflect processes such as stimulus-driven adaptation, top-down modulation or spontaneous fluctuations. It poses a challenge to functional characterization methods such as the receptive field, since these often assume stationarity. We propose a novel method for estimation of sensory neurons' receptive fields that extends the classic static linear receptive field model to the time-varying case. Here, the long-term estimate of the static receptive field serves as the mean of a probabilistic prior distribution from which the short-term temporally localized receptive field may deviate stochastically with time-varying standard deviation. The derived corresponding generalized linear model permits robust characterization of temporal variability in receptive field structure also for highly non-Gaussian stimulus ensembles. We computed and analyzed short-term auditory spectro-temporal receptive field (STRF) estimates with characteristic temporal resolution 5-30 s based on model simulations and responses from in total 60 single-unit recordings in anesthetized Mongolian gerbil auditory midbrain and cortex. Stimulation was performed with short (100 ms) overlapping frequency-modulated tones. Results demonstrate identification of time-varying STRFs, with obtained predictive model likelihoods exceeding those from baseline static STRF estimation. Quantitative characterization of STRF variability reveals a higher degree thereof in auditory cortex compared to midbrain. Cluster analysis indicates that significant deviations from the long-term static STRF are brief, but reliably estimated. We hypothesize that the observed variability more likely reflects spontaneous or state-dependent internal fluctuations that interact with stimulus-induced processing, rather than experimental or stimulus design.
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Affiliation(s)
- Arne F Meyer
- Medizinische Physik and Cluster of Excellence Hearing4all, Department of Medical Physics and Acoustics, Carl von Ossietzky University Oldenburg, Germany
| | - Jan-Philipp Diepenbrock
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology Magdeburg, Germany
| | - Frank W Ohl
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology Magdeburg, Germany ; Department of Neuroprosthetics, Institute of Biology, Otto-von-Guericke University Magdeburg, Germany
| | - Jörn Anemüller
- Medizinische Physik and Cluster of Excellence Hearing4all, Department of Medical Physics and Acoustics, Carl von Ossietzky University Oldenburg, Germany
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