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Theodore W. Berger - One of the best experts on this subject based on the ideXlab platform.

  • Decoding memory features from hippocampal spiking activities using sparse classification models
    2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016
    Co-Authors: Dong Song, Robert E. Hampson, Vasilis Z. Marmarelis, Sam A. Deadwyler, Brian S. Robinson, Theodore W. Berger
    Abstract:

    To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed Match-to-Sample Task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory Tasks and categories of sample images used in the Task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

  • EMBC - Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2015
    Co-Authors: Dong Song, Robert E. Hampson, Vasilis Z. Marmarelis, Sam A. Deadwyler, Brian S. Robinson, Theodore W. Berger
    Abstract:

    In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed Match-to-Sample Task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.

  • Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses
    2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
    Co-Authors: Dong Song, Robert E. Hampson, Vasilis Z. Marmarelis, Sam A. Deadwyler, Brian S. Robinson, Theodore W. Berger
    Abstract:

    In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed Match-to-Sample Task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.

  • EMBC - Functional connectivity between Layer 2/3 and Layer 5 neurons in prefrontal cortex of nonhuman primates during a delayed Match-to-Sample Task
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012
    Co-Authors: Dong Song, Ioan Opris, Robert E. Hampson, Rosa H. M. Chan, Vasilis Z. Marmarelis, Sam A. Deadwyler, Theodore W. Berger
    Abstract:

    The prefrontal cortex (PFC) has been postulated to play critical roles in cognitive control and the formation of long-term memories. To gain insights into the neurobiological mechanism of such high-order cognitive functions, it is important to understand the input-output transformational properties of the PFC micro-circuitry. In this study, we identify the functional connectivity between the Layer 2/3 (input) neurons and the Layer 5 (output) neurons using a previously developed generalized Volterra model (GVM). Input-output spike trains are recorded from the PFCs of nonhuman primates performing a memory-dependent delayed Match-to-Sample Task with a customized conformal ceramic multi-electrode array. The GVM describes how the input spike trains are transformed into the output spike trains by the PFC micro-circuitry and represents the transformation in the form of Volterra kernels. Results show that Layer 2/3 neurons have strong and transient facilitatory effects on the firings of Layer 5 neurons. The magnitude and temporal range of the input-output nonlinear dynamics are strikingly different from those of the hippocampal CA3-CA1. This form of functional connectivity may have important implications to understanding the computational principle of the PFC.

  • Functional connectivity between Layer 2/3 and Layer 5 neurons in prefrontal cortex of nonhuman primates during a delayed Match-to-Sample Task
    2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
    Co-Authors: Dong Song, Ioan Opris, Robert E. Hampson, Rosa H. M. Chan, Vasilis Z. Marmarelis, Sam A. Deadwyler, Theodore W. Berger
    Abstract:

    The prefrontal cortex (PFC) has been postulated to play critical roles in cognitive control and the formation of long-term memories. To gain insights into the neurobiological mechanism of such high-order cognitive functions, it is important to understand the input-output transformational properties of the PFC micro-circuitry. In this study, we identify the functional connectivity between the Layer 2/3 (input) neurons and the Layer 5 (output) neurons using a previously developed generalized Volterra model (GVM). Input-output spike trains are recorded from the PFCs of nonhuman primates performing a memory-dependent delayed Match-to-Sample Task with a customized conformal ceramic multi-electrode array. The GVM describes how the input spike trains are transformed into the output spike trains by the PFC micro-circuitry and represents the transformation in the form of Volterra kernels. Results show that Layer 2/3 neurons have strong and transient facilitatory effects on the firings of Layer 5 neurons. The magnitude and temporal range of the input-output nonlinear dynamics are strikingly different from those of the hippocampal CA3-CA1. This form of functional connectivity may have important implications to understanding the computational principle of the PFC.

Vasilis Z. Marmarelis - One of the best experts on this subject based on the ideXlab platform.

  • Decoding memory features from hippocampal spiking activities using sparse classification models
    2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016
    Co-Authors: Dong Song, Robert E. Hampson, Vasilis Z. Marmarelis, Sam A. Deadwyler, Brian S. Robinson, Theodore W. Berger
    Abstract:

    To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed Match-to-Sample Task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory Tasks and categories of sample images used in the Task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

  • EMBC - Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2015
    Co-Authors: Dong Song, Robert E. Hampson, Vasilis Z. Marmarelis, Sam A. Deadwyler, Brian S. Robinson, Theodore W. Berger
    Abstract:

    In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed Match-to-Sample Task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.

  • Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses
    2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
    Co-Authors: Dong Song, Robert E. Hampson, Vasilis Z. Marmarelis, Sam A. Deadwyler, Brian S. Robinson, Theodore W. Berger
    Abstract:

    In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed Match-to-Sample Task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.

  • On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes
    Journal of Computational Neuroscience, 2014
    Co-Authors: Vasilis Z. Marmarelis, D. C. Shin, D. Song, Sam A. Deadwyler, R. E. Hampson, T.w. Berger
    Abstract:

    Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample Task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed “input–output channels of communication” corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different Tasks. We found that behavioral performance during the Delayed-Match-to-Sample Task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.

  • Model-based design of optimal neurostimulation in the NHP hippocampus for enhancing behavioral Task performance
    2013 6th International IEEE EMBS Conference on Neural Engineering (NER), 2013
    Co-Authors: Vasilis Z. Marmarelis, D. C. Shin, D. Song, Robert E. Hampson, Sam A. Deadwyler, T.w. Berger
    Abstract:

    A general model-based methodology is presented for the optimal design of stimulation patterns in the CA1 region of the hippocampus of non-human primates (NHP) that seeks to enhance performance in a Delayed-Match-to-Sample Task. The methodology follows a hierarchical Volterra-type modeling approach that expresses the probability of a correct behavioral outcome in terms of multi-convolutional modules involving "Triggering Likelihood Functions" (TLFs), which represent the interactions among multiple neuronal spikes as they impact the outcome. This TLF-based model is estimated from experimental spike-train data recorded in the CA1 region of the hippocampus with multi-electrode arrays. The model can be used to compute the likelihood of a correct outcome for any given set of spike-train data of CA1 multi-neuron activity. This enables the design of the optimal stimulation pattern through a computational search procedure under proper constraints on mean firing rates. We present results of the TLF-based model obtained from experimental NHP data and initial experimental validation of the designed optimal stimulation pattern.

Tobias Egner - One of the best experts on this subject based on the ideXlab platform.

  • Neural Representation of Working Memory Content Is Modulated by Visual Attentional Demand
    Journal of Cognitive Neuroscience, 2017
    Co-Authors: Anastasia Kiyonaga, Emma Wu Dowd, Tobias Egner
    Abstract:

    Recent theories assert that visual working memory (WM) relies on the same attentional resources and sensory substrates as visual attention to external stimuli. Behavioral studies have observed competitive tradeoffs between internal (i.e., WM) and external (i.e., visual) attentional demands, and neuroimaging studies have revealed representations of WM content as distributed patterns of activity within the same cortical regions engaged by perception of that content. Although a key function of WM is to protect memoranda from competing input, it remains unknown how neural representations of WM content are impacted by incoming sensory stimuli and concurrent attentional demands. Here, we investigated how neural evidence for WM information is affected when attention is occupied by visual search—at varying levels of difficulty—during the delay interval of a WM Match-to-Sample Task. Behavioral and fMRI analyses suggested that WM maintenance was impacted by the difficulty of a concurrent visual Task. Critically, multivariate classification analyses of category-specific ventral visual areas revealed a reduction in decodable WM-related information when attention was diverted to a visual search Task, especially when the search was more difficult. This study suggests that the amount of available attention during WM maintenance influences the detection of sensory WM representations.

Dong Song - One of the best experts on this subject based on the ideXlab platform.

  • Decoding memory features from hippocampal spiking activities using sparse classification models
    2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016
    Co-Authors: Dong Song, Robert E. Hampson, Vasilis Z. Marmarelis, Sam A. Deadwyler, Brian S. Robinson, Theodore W. Berger
    Abstract:

    To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed Match-to-Sample Task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory Tasks and categories of sample images used in the Task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.

  • EMBC - Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2015
    Co-Authors: Dong Song, Robert E. Hampson, Vasilis Z. Marmarelis, Sam A. Deadwyler, Brian S. Robinson, Theodore W. Berger
    Abstract:

    In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed Match-to-Sample Task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.

  • Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses
    2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
    Co-Authors: Dong Song, Robert E. Hampson, Vasilis Z. Marmarelis, Sam A. Deadwyler, Brian S. Robinson, Theodore W. Berger
    Abstract:

    In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed Match-to-Sample Task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.

  • EMBC - Functional connectivity between Layer 2/3 and Layer 5 neurons in prefrontal cortex of nonhuman primates during a delayed Match-to-Sample Task
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012
    Co-Authors: Dong Song, Ioan Opris, Robert E. Hampson, Rosa H. M. Chan, Vasilis Z. Marmarelis, Sam A. Deadwyler, Theodore W. Berger
    Abstract:

    The prefrontal cortex (PFC) has been postulated to play critical roles in cognitive control and the formation of long-term memories. To gain insights into the neurobiological mechanism of such high-order cognitive functions, it is important to understand the input-output transformational properties of the PFC micro-circuitry. In this study, we identify the functional connectivity between the Layer 2/3 (input) neurons and the Layer 5 (output) neurons using a previously developed generalized Volterra model (GVM). Input-output spike trains are recorded from the PFCs of nonhuman primates performing a memory-dependent delayed Match-to-Sample Task with a customized conformal ceramic multi-electrode array. The GVM describes how the input spike trains are transformed into the output spike trains by the PFC micro-circuitry and represents the transformation in the form of Volterra kernels. Results show that Layer 2/3 neurons have strong and transient facilitatory effects on the firings of Layer 5 neurons. The magnitude and temporal range of the input-output nonlinear dynamics are strikingly different from those of the hippocampal CA3-CA1. This form of functional connectivity may have important implications to understanding the computational principle of the PFC.

  • Functional connectivity between Layer 2/3 and Layer 5 neurons in prefrontal cortex of nonhuman primates during a delayed Match-to-Sample Task
    2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
    Co-Authors: Dong Song, Ioan Opris, Robert E. Hampson, Rosa H. M. Chan, Vasilis Z. Marmarelis, Sam A. Deadwyler, Theodore W. Berger
    Abstract:

    The prefrontal cortex (PFC) has been postulated to play critical roles in cognitive control and the formation of long-term memories. To gain insights into the neurobiological mechanism of such high-order cognitive functions, it is important to understand the input-output transformational properties of the PFC micro-circuitry. In this study, we identify the functional connectivity between the Layer 2/3 (input) neurons and the Layer 5 (output) neurons using a previously developed generalized Volterra model (GVM). Input-output spike trains are recorded from the PFCs of nonhuman primates performing a memory-dependent delayed Match-to-Sample Task with a customized conformal ceramic multi-electrode array. The GVM describes how the input spike trains are transformed into the output spike trains by the PFC micro-circuitry and represents the transformation in the form of Volterra kernels. Results show that Layer 2/3 neurons have strong and transient facilitatory effects on the firings of Layer 5 neurons. The magnitude and temporal range of the input-output nonlinear dynamics are strikingly different from those of the hippocampal CA3-CA1. This form of functional connectivity may have important implications to understanding the computational principle of the PFC.

Chantal E. Stern - One of the best experts on this subject based on the ideXlab platform.

  • Role of the hippocampus and orbitofrontal cortex during the disambiguation of social cues in working memory
    Cognitive Affective & Behavioral Neuroscience, 2013
    Co-Authors: Robert S. Ross, Matthew L. Lopresti, Karin Schon, Chantal E. Stern
    Abstract:

    Human social interactions are complex behaviors requiring the concerted effort of multiple neural systems to track and monitor the individuals around us. Cognitively, adjusting our behavior on the basis of changing social cues such as facial expressions relies on working memory and the ability to disambiguate, or separate, the representations of overlapping stimuli resulting from viewing the same individual with different facial expressions. We conducted an fMRI experiment examining the brain regions contributing to the encoding, maintenance, and retrieval of overlapping identity information during working memory using a delayed Match-to-Sample Task. In the overlapping condition, two faces from the same individual with different facial expressions were presented at sample. In the nonoverlapping condition, the two sample faces were from two different individuals with different expressions. fMRI activity was assessed by contrasting the overlapping and nonoverlapping conditions at sample, delay, and test. The lateral orbitofrontal cortex showed increased fMRI signal in the overlapping condition in all three phases of the delayed Match-to-Sample Task and increased functional connectivity with the hippocampus when encoding overlapping stimuli. The hippocampus showed increased fMRI signal at test. These data suggest that lateral orbitofrontal cortex helps encode and maintain representations of overlapping stimuli in working memory, whereas the orbitofrontal cortex and hippocampus contribute to the successful retrieval of overlapping stimuli. We suggest that the lateral orbitofrontal cortex and hippocampus play a role in encoding, maintaining, and retrieving social cues, especially when multiple interactions with an individual need to be disambiguated in a rapidly changing social context in order to make appropriate social responses.

  • persistence of parahippocampal representation in the absence of stimulus input enhances long term encoding a functional magnetic resonance imaging study of subsequent memory after a delayed match to sample Task
    The Journal of Neuroscience, 2004
    Co-Authors: Karin Schon, Michael E. Hasselmo, Matthew L. Lopresti, Marisa D Tricarico, Chantal E. Stern
    Abstract:

    Recent theoretical models based on cellular processes in parahippocampal structures show that persistent neuronal spiking in the absence of stimulus input is important for encoding. The goal of this study was to examine in humans how sustained activity in the parahippocampal gyrus may underlie long-term encoding as well as active maintenance of novel information. The relationship between long-term encoding and active maintenance of novel information during brief memory delays was studied using functional magnetic resonance imaging (fMRI) in humans performing a delayed matching-to-sample (DMS) Task and a post-scan subsequent recognition memory Task of items encountered during DMS Task performance. Multiple regression analyses revealed fMRI activity in parahippocampal structures associated with the active maintenance of trial-unique visual information during a brief memory delay. In addition to a role in active maintenance, we found that the subsequent memory for the sample stimuli as measured by the post-scan subsequent recognition memory Task correlated with activity in the parahippocampal gyrus during the delay period. The results provide direct evidence that encoding mechanisms are engaged during brief memory delays when novel information is actively maintained. The relationship between active maintenance during the delay period and long-term subsequent memory is consistent with current theoretical models and experimental data that suggest that long-term encoding is enhanced by sustained parahippocampal activity.