Apicella, Paul, Sabrina Ravel, Pierangelo Sardo, and Eric Leend, we compared the activity of toni... more Apicella, Paul, Sabrina Ravel, Pierangelo Sardo, and Eric Leend, we compared the activity of tonic striatal neurons during gallet. Influence of predictive information on responses of tonically performance of a simple reaction time task in the presence active neurons in the monkey striatum. J. Neurophysiol. 80: 3341and absence of an instruction cue presented at a fixed interval 3344, 1998. We investigated how the expectation of a signal of before a trigger stimulus for movement. The results showed behavioral significance influences the activity of tonically active that the trigger stimulus modulated many neurons when its neurons in the striatum of two monkeys performing a simple reaconset remained unpredictable, whereas the same stimulus tion time task under two conditions, an uncued condition in which modulated fewer neurons and at lower magnitudes when it the trigger stimulus occurred randomly in time and a cued condition was preceded by an instruction cue. in which the same trigger was preceded by an instruction stimulus serving as a predictive signal for the forthcoming signal eliciting an immediate behavioral reaction. Both monkeys benefited from
, there are additional adaptive mechanisms for the control of planned eye movements than those th... more , there are additional adaptive mechanisms for the control of planned eye movements than those that engage the SC . These mechanisms are modeled in . Grossberg et al. • Collicular Model of Adaptive Saccade Control J. Neurosci., December 15, 1997, 17(24):9706-9725 9713 Grossberg et al. • Collicular Model of Adaptive Saccade Control J. Neurosci., December 15, 1997, 17(24):9706-9725 9715
Origin of the slow afterhyperpolarization and slow rhythmic bursting in striatal cholinergic inte... more Origin of the slow afterhyperpolarization and slow rhythmic bursting in striatal cholinergic interneurons. . Striatal cholinergic interneurons recorded in slices exhibit three different firing patterns: rhythmic single spiking, irregular bursting, and rhythmic bursting. The rhythmic single-spiking pattern is governed mainly by a prominent brief afterhyperpolarization (mAHP) that follows single spikes. The mAHP arises from an apamin-sensitive calcium-dependent potassium current. A slower AHP (sAHP), also present in these neurons, becomes prominent during rhythmic bursting or driven firing. Although not apamin sensitive, the sAHP is caused by a calciumdependent potassium conductance. It is not present after blockade of calcium current with cadmium or after calcium is removed from the media or when intracellular calcium is buffered with bis-(o-aminophenoxy)ethane-N,N,NЈ,NЈ-tetraacetic acid. It reverses at the potassium equilibrium potential. It can be generated by subthreshold depolarizations and persists after blockade of sodium currents by tetrodotoxin. It is slow, being maximal ϳ1 s after depolarization onset, and takes several seconds to decay. It requires Ͼ300-ms depolarizations to become maximally activated. Its voltage sensitivity is sigmoidal, with a half activation voltage of Ϫ40 mV. We conclude the sAHP is a high-affinity apamin-insensitive calcium-dependent potassium conductance, triggered by calcium currents partly activated at subthreshold levels. In combination with those calcium currents, it accounts for the slow oscillations seen in a subset of cholinergic interneurons exhibiting rhythmic bursting. In all cholinergic interneurons, it contributes to the slowdown or pause in firing that follows driven activity or prolonged subthreshold depolarizations and is therefore a candidate mechanism for the pause response observed in cholinergic neurons in vivo.
Oculomotor tracking of moving objects is an important component of visually based cognition and p... more Oculomotor tracking of moving objects is an important component of visually based cognition and planning. Such tracking is achieved by a combination of saccades and smooth-pursuit eye movements. In particular, the saccadic and smooth-pursuit systems interact to often choose the same target, and to maximize its visibility through time. How do multiple brain regions interact, including frontal cortical areas, to decide the choice of a target among several competing moving stimuli? How is target selection information that is created by a bias (e.g., electrical stimulation) transferred from one movement system to another? These saccade-pursuit interactions are clarified by a new computational neural model, which describes interactions between motion processing areas: the middle temporal area, the middle superior temporal area, the frontal pursuit area, and the dorsal lateral pontine nucleus; saccade specification, selection, and planning areas: the lateral intraparietal area, the frontal eye fields, the substantia nigra pars reticulata, and the superior colliculus; the saccadic generator in the brain stem; and the cerebellum. Model simulations explain a broad range of neuroanatomical and neurophysiological data. These results are in contrast with the simplest parallel model with no interactions between saccades and pursuit other than common-target selection and recruitment of shared motoneurons. Actual tracking episodes in primates reveal multiple systematic deviations from predictions of the simplest parallel model, which are explained by the current model.
SUPPLEMENTARY MATERIAL 1. Detailed description of the equations This section includes detailed de... more SUPPLEMENTARY MATERIAL 1. Detailed description of the equations This section includes detailed description of the newly added equations of the extended GODIVA model (equations 15 to 25), as well as the equations modified from the original GODIVA model (equations 13 and 14). Detailed descriptions of the equations of the original GODIVA model are given elsewhere (Bohland, Bullock, & Guenther, 2010). The list of the equations of the original GODIVA model can be found in Appendix A of the Supplementary material, and the main variables they use are given in Appendix B of the Supplementary material. Each section below details and discusses the equations that correspond to a specific brain region of the BG-vPMC loop, except for the last section that concerns with sound/syllable repetitions. Most equations have the form of shunting equations (Grossberg, 1973): the A parameter indicates the strength of the cell's spontaneous decay of activity; the B parameter controls how high the excitatory input can bring the cell activity; and the C parameter indicates the strength of the inhibitory input to the cell, if such input exists. Exceptions are the equations for cells representing spontaneously-firing neurons (e.g., pallidal cells). Because the activities of these cells should not decay below the level fixed by B, their equations are missing a spontaneous decay term (and the A parameter). Furthermore, some equations are missing the C parameter despite having an inhibitory term; in these equations, the C parameter is assumed to equal 1. The variables used by the equations of the BG-vPMC loop are given in Appendix C of the Supplementary material.
During goal-directed reaching in primates, a sensorimotor transformation generates a dynamical pa... more During goal-directed reaching in primates, a sensorimotor transformation generates a dynamical pattern of muscle activation. Within the context of this sensorimotor transformation, a fundamental question concerns the coordinate systems in which individual cells in the primary motor cortex (MI) encode movement direction. This article develops a mathematical framework that computes, as a function of the coordinate system in which an individual cell is hypothesized to operate, the spatial preferred direction (pd) of that cell as the arm configuration and hand location vary. Three coordinate systems are explicitly modeled: Cartesian spatial, shoulder-centered, and joint angle. The computed patterns of spatial pds are distinct for each of these three coordinate systems, and experimental approaches are described that can capitalize on these differences to compare the empirical adequacy of each coordinate hypothesis. One particular experiment involving curved motion was analyzed from this ...
The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 15, 1997
How does the saccadic movement system select a target when visual, auditory, and planned movement... more How does the saccadic movement system select a target when visual, auditory, and planned movement commands differ? How do retinal, head-centered, and motor error coordinates interact during the selection process? Recent data on superior colliculus (SC) reveal a spreading wave of activation across buildup cells the peak activity of which covaries with the current gaze error. In contrast, the locus of peak activity remains constant at burst cells, whereas their activity level decays with residual gaze error. A neural model answers these questions and simulates burst and buildup responses in visual, overlap, memory, and gap tasks. The model also simulates data on multimodal enhancement and suppression of activity in the deeper SC layers and suggests a functional role for NMDA receptors in this region. In particular, the model suggests how auditory and planned saccadic target positions become aligned and compete with visually reactive target positions to select a movement command. For t...
How does the brain coordinate saccadic and smooth pursuit eye movements to track objects that mov... more How does the brain coordinate saccadic and smooth pursuit eye movements to track objects that move in unpredictable directions and speeds? Saccadic eye movements rapidly foveate peripheral visual or auditory targets, and smooth pursuit eye movements keep the fovea pointed toward an attended moving target. Analyses of tracking data in monkeys and humans reveal systematic deviations from predictions of the simplest model of saccade-pursuit interactions, which would use no interactions other than common target selection and recruitment of shared motoneurons. Instead, saccadic and smooth pursuit movements cooperate to cancel errors of gaze position and velocity, and thus to maximize target visibility through time. How are these two systems coordinated to promote visual localization and identification of moving targets? How are saccades calibrated to correctly foveate a target despite its continued motion during the saccade? The neural model proposed here answers these questions. Modeled interactions encompass motion processing areas MT, MST, FPA, DLPN and NRTP; saccade planning and execution areas FEF, LIP, and SC; the saccadic generator in the brain stem; and the cerebellum. Simulations illustrate the model's ability to functionally explain and quantitatively simulate anatomical, neurophysiological and behavioral data about coordinated saccade-pursuit tracking.
A self-organizing neural model of language comprehension at syntactic and semantic levels has bee... more A self-organizing neural model of language comprehension at syntactic and semantic levels has been developed, in which the competence to process movements emerges as the result of de-synchronization among three working memories. The model consists of a self-organizing parser, a self-organizing lexicon, and a clausal working memory. The parser consists of an argument stream and a head stream, each of which has its own working memory. After being trained with a corpus that consists of mothers' utterances to children, the model was shown to be able to process a type of NP movement.
Neural Networks for Control is based on a workshop entitled "The Application of Neural Networks t... more Neural Networks for Control is based on a workshop entitled "The Application of Neural Networks to Robotics and Control" held in October 1988 ~ at the University of New Hampshire. Consistent with the varied backgrounds of the workshop attendees, this collection of works endeavors not only to present current theoretical research in neural network-based control systems, but also aims to identify industrial applications where neural networks may prove useful in the near future. The book is broken into three parts: (I) general principles, (II) motion control, and (III) application domains. In addition, eight benchmark control problems are included in an appendix. The main theme pervading this effort concerns the synthesis of neural network methods with existing knowledge from the well-studied fields of conventional and adaptive control theory. Perhaps due to the focus on engineering control theory, little direct contact is made with issues involving control in biological systems. Control problems arise when we try to make a physical system exhibit a desired response. If we know the functions relating inputs to outputs and vice versa for the system, we can use these functions to predict system behavior and control the system's response by judicious choice of inputs. Unfortunately, most complex systems exhibit highly nonlinear input-output functions. Moreover, because the complexity of such systems is often necessary for their intended function (e.g., controlling the position and orientation of a hand in 3-D space requires at least a 6 degree-of-freedom "arm") the input-output function of the system cannot be simplified. Therefore, it becomes necessary to solve a difficult prediction problem as a preliminary to effective control. A classical approach to predicting the behavior of such systems is to "open up the black box" (i.e., to build a model of the system that captures its constituent structure and the elementary functions describing interactions among the constituents). An accurate model allows the overall input-output function to be computed as a composition of more elementary functions. Moreover, to paraphrase the Swiss structuralist Piaget, "to understand is to reconstruct." The description of the constituent structure taken together with the local laws of interaction provide an understanding of the mechanisms by which the aggregate input-output function is generated. However, another path can be taken to predicting the response of a system exhibiting a highly nonlinear input-output function: perform a variety of nonlinear regression analysis, or least-squares curve fitting, on a sufficiently broad sample of input-output pairs. Such an analysis can often yield suffi-Despite the 1988 date of the workshop, references as recent as 1990 can he found in the book.
Apicella, Paul, Sabrina Ravel, Pierangelo Sardo, and Eric Leend, we compared the activity of toni... more Apicella, Paul, Sabrina Ravel, Pierangelo Sardo, and Eric Leend, we compared the activity of tonic striatal neurons during gallet. Influence of predictive information on responses of tonically performance of a simple reaction time task in the presence active neurons in the monkey striatum. J. Neurophysiol. 80: 3341and absence of an instruction cue presented at a fixed interval 3344, 1998. We investigated how the expectation of a signal of before a trigger stimulus for movement. The results showed behavioral significance influences the activity of tonically active that the trigger stimulus modulated many neurons when its neurons in the striatum of two monkeys performing a simple reaconset remained unpredictable, whereas the same stimulus tion time task under two conditions, an uncued condition in which modulated fewer neurons and at lower magnitudes when it the trigger stimulus occurred randomly in time and a cued condition was preceded by an instruction cue. in which the same trigger was preceded by an instruction stimulus serving as a predictive signal for the forthcoming signal eliciting an immediate behavioral reaction. Both monkeys benefited from
, there are additional adaptive mechanisms for the control of planned eye movements than those th... more , there are additional adaptive mechanisms for the control of planned eye movements than those that engage the SC . These mechanisms are modeled in . Grossberg et al. • Collicular Model of Adaptive Saccade Control J. Neurosci., December 15, 1997, 17(24):9706-9725 9713 Grossberg et al. • Collicular Model of Adaptive Saccade Control J. Neurosci., December 15, 1997, 17(24):9706-9725 9715
Origin of the slow afterhyperpolarization and slow rhythmic bursting in striatal cholinergic inte... more Origin of the slow afterhyperpolarization and slow rhythmic bursting in striatal cholinergic interneurons. . Striatal cholinergic interneurons recorded in slices exhibit three different firing patterns: rhythmic single spiking, irregular bursting, and rhythmic bursting. The rhythmic single-spiking pattern is governed mainly by a prominent brief afterhyperpolarization (mAHP) that follows single spikes. The mAHP arises from an apamin-sensitive calcium-dependent potassium current. A slower AHP (sAHP), also present in these neurons, becomes prominent during rhythmic bursting or driven firing. Although not apamin sensitive, the sAHP is caused by a calciumdependent potassium conductance. It is not present after blockade of calcium current with cadmium or after calcium is removed from the media or when intracellular calcium is buffered with bis-(o-aminophenoxy)ethane-N,N,NЈ,NЈ-tetraacetic acid. It reverses at the potassium equilibrium potential. It can be generated by subthreshold depolarizations and persists after blockade of sodium currents by tetrodotoxin. It is slow, being maximal ϳ1 s after depolarization onset, and takes several seconds to decay. It requires Ͼ300-ms depolarizations to become maximally activated. Its voltage sensitivity is sigmoidal, with a half activation voltage of Ϫ40 mV. We conclude the sAHP is a high-affinity apamin-insensitive calcium-dependent potassium conductance, triggered by calcium currents partly activated at subthreshold levels. In combination with those calcium currents, it accounts for the slow oscillations seen in a subset of cholinergic interneurons exhibiting rhythmic bursting. In all cholinergic interneurons, it contributes to the slowdown or pause in firing that follows driven activity or prolonged subthreshold depolarizations and is therefore a candidate mechanism for the pause response observed in cholinergic neurons in vivo.
Oculomotor tracking of moving objects is an important component of visually based cognition and p... more Oculomotor tracking of moving objects is an important component of visually based cognition and planning. Such tracking is achieved by a combination of saccades and smooth-pursuit eye movements. In particular, the saccadic and smooth-pursuit systems interact to often choose the same target, and to maximize its visibility through time. How do multiple brain regions interact, including frontal cortical areas, to decide the choice of a target among several competing moving stimuli? How is target selection information that is created by a bias (e.g., electrical stimulation) transferred from one movement system to another? These saccade-pursuit interactions are clarified by a new computational neural model, which describes interactions between motion processing areas: the middle temporal area, the middle superior temporal area, the frontal pursuit area, and the dorsal lateral pontine nucleus; saccade specification, selection, and planning areas: the lateral intraparietal area, the frontal eye fields, the substantia nigra pars reticulata, and the superior colliculus; the saccadic generator in the brain stem; and the cerebellum. Model simulations explain a broad range of neuroanatomical and neurophysiological data. These results are in contrast with the simplest parallel model with no interactions between saccades and pursuit other than common-target selection and recruitment of shared motoneurons. Actual tracking episodes in primates reveal multiple systematic deviations from predictions of the simplest parallel model, which are explained by the current model.
SUPPLEMENTARY MATERIAL 1. Detailed description of the equations This section includes detailed de... more SUPPLEMENTARY MATERIAL 1. Detailed description of the equations This section includes detailed description of the newly added equations of the extended GODIVA model (equations 15 to 25), as well as the equations modified from the original GODIVA model (equations 13 and 14). Detailed descriptions of the equations of the original GODIVA model are given elsewhere (Bohland, Bullock, & Guenther, 2010). The list of the equations of the original GODIVA model can be found in Appendix A of the Supplementary material, and the main variables they use are given in Appendix B of the Supplementary material. Each section below details and discusses the equations that correspond to a specific brain region of the BG-vPMC loop, except for the last section that concerns with sound/syllable repetitions. Most equations have the form of shunting equations (Grossberg, 1973): the A parameter indicates the strength of the cell's spontaneous decay of activity; the B parameter controls how high the excitatory input can bring the cell activity; and the C parameter indicates the strength of the inhibitory input to the cell, if such input exists. Exceptions are the equations for cells representing spontaneously-firing neurons (e.g., pallidal cells). Because the activities of these cells should not decay below the level fixed by B, their equations are missing a spontaneous decay term (and the A parameter). Furthermore, some equations are missing the C parameter despite having an inhibitory term; in these equations, the C parameter is assumed to equal 1. The variables used by the equations of the BG-vPMC loop are given in Appendix C of the Supplementary material.
During goal-directed reaching in primates, a sensorimotor transformation generates a dynamical pa... more During goal-directed reaching in primates, a sensorimotor transformation generates a dynamical pattern of muscle activation. Within the context of this sensorimotor transformation, a fundamental question concerns the coordinate systems in which individual cells in the primary motor cortex (MI) encode movement direction. This article develops a mathematical framework that computes, as a function of the coordinate system in which an individual cell is hypothesized to operate, the spatial preferred direction (pd) of that cell as the arm configuration and hand location vary. Three coordinate systems are explicitly modeled: Cartesian spatial, shoulder-centered, and joint angle. The computed patterns of spatial pds are distinct for each of these three coordinate systems, and experimental approaches are described that can capitalize on these differences to compare the empirical adequacy of each coordinate hypothesis. One particular experiment involving curved motion was analyzed from this ...
The Journal of neuroscience : the official journal of the Society for Neuroscience, Jan 15, 1997
How does the saccadic movement system select a target when visual, auditory, and planned movement... more How does the saccadic movement system select a target when visual, auditory, and planned movement commands differ? How do retinal, head-centered, and motor error coordinates interact during the selection process? Recent data on superior colliculus (SC) reveal a spreading wave of activation across buildup cells the peak activity of which covaries with the current gaze error. In contrast, the locus of peak activity remains constant at burst cells, whereas their activity level decays with residual gaze error. A neural model answers these questions and simulates burst and buildup responses in visual, overlap, memory, and gap tasks. The model also simulates data on multimodal enhancement and suppression of activity in the deeper SC layers and suggests a functional role for NMDA receptors in this region. In particular, the model suggests how auditory and planned saccadic target positions become aligned and compete with visually reactive target positions to select a movement command. For t...
How does the brain coordinate saccadic and smooth pursuit eye movements to track objects that mov... more How does the brain coordinate saccadic and smooth pursuit eye movements to track objects that move in unpredictable directions and speeds? Saccadic eye movements rapidly foveate peripheral visual or auditory targets, and smooth pursuit eye movements keep the fovea pointed toward an attended moving target. Analyses of tracking data in monkeys and humans reveal systematic deviations from predictions of the simplest model of saccade-pursuit interactions, which would use no interactions other than common target selection and recruitment of shared motoneurons. Instead, saccadic and smooth pursuit movements cooperate to cancel errors of gaze position and velocity, and thus to maximize target visibility through time. How are these two systems coordinated to promote visual localization and identification of moving targets? How are saccades calibrated to correctly foveate a target despite its continued motion during the saccade? The neural model proposed here answers these questions. Modeled interactions encompass motion processing areas MT, MST, FPA, DLPN and NRTP; saccade planning and execution areas FEF, LIP, and SC; the saccadic generator in the brain stem; and the cerebellum. Simulations illustrate the model's ability to functionally explain and quantitatively simulate anatomical, neurophysiological and behavioral data about coordinated saccade-pursuit tracking.
A self-organizing neural model of language comprehension at syntactic and semantic levels has bee... more A self-organizing neural model of language comprehension at syntactic and semantic levels has been developed, in which the competence to process movements emerges as the result of de-synchronization among three working memories. The model consists of a self-organizing parser, a self-organizing lexicon, and a clausal working memory. The parser consists of an argument stream and a head stream, each of which has its own working memory. After being trained with a corpus that consists of mothers' utterances to children, the model was shown to be able to process a type of NP movement.
Neural Networks for Control is based on a workshop entitled "The Application of Neural Networks t... more Neural Networks for Control is based on a workshop entitled "The Application of Neural Networks to Robotics and Control" held in October 1988 ~ at the University of New Hampshire. Consistent with the varied backgrounds of the workshop attendees, this collection of works endeavors not only to present current theoretical research in neural network-based control systems, but also aims to identify industrial applications where neural networks may prove useful in the near future. The book is broken into three parts: (I) general principles, (II) motion control, and (III) application domains. In addition, eight benchmark control problems are included in an appendix. The main theme pervading this effort concerns the synthesis of neural network methods with existing knowledge from the well-studied fields of conventional and adaptive control theory. Perhaps due to the focus on engineering control theory, little direct contact is made with issues involving control in biological systems. Control problems arise when we try to make a physical system exhibit a desired response. If we know the functions relating inputs to outputs and vice versa for the system, we can use these functions to predict system behavior and control the system's response by judicious choice of inputs. Unfortunately, most complex systems exhibit highly nonlinear input-output functions. Moreover, because the complexity of such systems is often necessary for their intended function (e.g., controlling the position and orientation of a hand in 3-D space requires at least a 6 degree-of-freedom "arm") the input-output function of the system cannot be simplified. Therefore, it becomes necessary to solve a difficult prediction problem as a preliminary to effective control. A classical approach to predicting the behavior of such systems is to "open up the black box" (i.e., to build a model of the system that captures its constituent structure and the elementary functions describing interactions among the constituents). An accurate model allows the overall input-output function to be computed as a composition of more elementary functions. Moreover, to paraphrase the Swiss structuralist Piaget, "to understand is to reconstruct." The description of the constituent structure taken together with the local laws of interaction provide an understanding of the mechanisms by which the aggregate input-output function is generated. However, another path can be taken to predicting the response of a system exhibiting a highly nonlinear input-output function: perform a variety of nonlinear regression analysis, or least-squares curve fitting, on a sufficiently broad sample of input-output pairs. Such an analysis can often yield suffi-Despite the 1988 date of the workshop, references as recent as 1990 can he found in the book.
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