The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Apostolos P. Georgopoulos - One of the best experts on this subject based on the ideXlab platform.
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Cell directional spread determines accuracy, precision, and length of the neuronal Population Vector.
Experimental brain research, 2014Co-Authors: Apostolos P. GeorgopoulosAbstract:The neuronal Population Vector (NPV) for movement direction is the sum of weighted neuronal directional contributions. Based on theoretical considerations, we proposed recently that the sharpness of tuning will impact the directional precision, accuracy, and length of the NPV, such that sharper tuning will yield NPV with higher precision, higher accuracy, and shorter length (Mahan and Georgopoulos in Front Neural Circuits 7:92, 2013). Furthermore, we proposed that controlling the inhibitory drive in a local network could be the mechanism by which the sharpness of directional tuning would be varied, resulting in a continuous specification and control of movement's directional precision, accuracy, and speed (Mahan and Georgopoulos in Front Neural Circuits 7:92, 2013, Fig. 5). As a first step in testing this idea, here we analyzed data from 899 cells recorded in the motor cortex during performance of a center → out task. There were two major findings. First, directional selectivity varied with cell activity, such that it was higher in cells with lower mean discharge rates. And second, NPVs calculated from subsets of cells with higher directional selectivity (and, correspondingly, lower mean discharge rates) were more accurate (i.e., closer to the movement), precise (i.e., less variable), and shorter (i.e., slower; Schwartz in Science 265:540-542, 1994). These findings confirm our predictions above made from modeling (Mahan and Georgopoulos in Front Neural Circuits 7:92, 2013) and provide a simple mechanism by which desired attributes of the directional motor command can be implemented. We hypothesize that the inhibitory drive in a local network is controlled directly and independently of recurrent collaterals or common excitatory inputs to other cells. This could be achieved by a private excitation/inhibition of key inhibitory interneurons in a way similar to that in operation for Renshaw cells in the spinal cord. The presence of such a private line of inhibitory control remains to be investigated.
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eight pairs of descending visual neurons in the dragonfly give wing motor centers accurate Population Vector of prey direction
Proceedings of the National Academy of Sciences of the United States of America, 2013Co-Authors: Apostolos P. Georgopoulos, Paloma T Gonzalezbellido, Hanchuan Peng, Jinzhu Yang, Robert M OlbergAbstract:Intercepting a moving object requires prediction of its future location. This complex task has been solved by dragonflies, who intercept their prey in midair with a 95% success rate. In this study, we show that a group of 16 neurons, called target-selective descending neurons (TSDNs), code a Population Vector that reflects the direction of the target with high accuracy and reliability across 360°. The TSDN spatial (receptive field) and temporal (latency) properties matched the area of the retina where the prey is focused and the reaction time, respectively, during predatory flights. The directional tuning curves and morphological traits (3D tracings) for each TSDN type were consistent among animals, but spike rates were not. Our results emphasize that a successful neural circuit for target tracking and interception can be achieved with few neurons and that in dragonflies this information is relayed from the brain to the wing motor centers in Population Vector form.
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Neural coding of finger and wrist movements.
Journal of computational neuroscience, 1999Co-Authors: Apostolos P. Georgopoulos, Giuseppe Pellizzer, Andrew V. Poliakov, Marc H. SchieberAbstract:Previous work (Schieber and Hibbard, 1993) has shown that single motor cortical neurons do not discharge specifically for a particular flexion-extension finger movement but instead are active with movements of different fingers. In addition, neuronal Populations active with movements of different fingers overlap extensively in their spatial locations in the motor cortex. These data suggested that control of any finger movement utilizes a distributed Population of neurons. In this study we applied the neuronal Population Vector analysis (Georgopoulos et al., 1983) to these same data to determine (1) whether single cells are tuned in an abstract, three-dimensional (3D) instructed finger and wrist movement space with hand-like geometry and (2) whether the neuronal Population encodes specific finger movements. We found that the activity of 132/176 (75%) motor cortical neurons related to finger movements was indeed tuned in this space. Moreover, the Population Vector computed in this space predicted well the instructed finger movement. Thus, although single neurons may be related to several disparate finger movements, and neurons related to different finger movements are intermingled throughout the hand area of the motor cortex, the neuronal Population activity does specify particular finger movements.
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Chapter 13 - Motor Cortex: Neural and Computational Studies
Neural-Network Models of Cognition - Biobehavioral Foundations, 1997Co-Authors: Apostolos P. GeorgopoulosAbstract:ABSTRACT The motor cortex can be regarded as a network of neurons processing, inter alia, spatial motor information. A basic component of this information is the direction of movement in space. Experimental studies in behaving monkeys have shown that the impulse activity of single motor-cortical cells relates to this component in an orderly fashion, such that the frequency of cell discharge is a sinusoidal function of the direction of movement, with the direction for which cell discharge is highest denoting the "preferred direction" of the cell. The neuronal ensemble of such directionally tuned cells can be regarded as a network in which each cell is represented as a Vector pointing in the cell's preferred direction. The network operates to generate a signal in the direction of a desired movement. We regard this operation as the Vectorial summation of the cell Vectors, weighted by a scalar measure of the intensity of cell activation. The resulting Vector sum is called the "neuronal Population Vector." Analysis of experimental data has shown that the Population Vector points in the direction of the movement. In addition, the Population Vector can be calculated as a time-varying signal and, as such, is a robust predictor of the direction of the upcoming movement during the reaction time and during instructed and memorized delays. Finally, it has proven a good tool for monitoring and deciphering directional information when more complex directional operations are performed.
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Motor cortical activity in a context-recall task.
Science, 1995Co-Authors: Giuseppe Pellizzer, Patricia Sargent, Apostolos P. GeorgopoulosAbstract:A monkey was trained to respond on the basis of the serial position of a test stimulus in a sequence. First, three stimuli were presented successively on a circle. Then one of them (except the last) changed color (test stimulus) and served as the go signal: The monkey was required to produce a motor response in the direction of the stimulus that followed the test stimulus. When the test stimulus was the second in the sequence, there was a change in motor cortical activity from a pattern reflecting the direction of this stimulus to the pattern associated with the direction of the motor response. This change was abrupt, occurred 100 to 150 milliseconds after the go signal, and was evident both in the activity of single cells and in the time-varying neuronal Population Vector. These findings identify the neural correlates of a switching process that is different from a mental rotation described previously.
Shintaro Funahashi - One of the best experts on this subject based on the ideXlab platform.
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thalamic mediodorsal nucleus and working memory
Neuroscience & Biobehavioral Reviews, 2012Co-Authors: Yumiko Watanabe, Shintaro FunahashiAbstract:Abstract Working memory is a dynamic neural system for temporarily maintaining and processing information. The prefrontal cortex (PFC) plays an important role in working memory. However, several evidences indicate that the thalamic mediodorsal nucleus (MD) also participates in working memory. Neurophysiological studies revealed that MD neurons exhibit sustained delay activity, which is considered to be a neural correlate of the temporary maintenance of information. Most MD neurons with delay activity represented information regarding motor responses, whereas some represented information regarding visual cues, suggesting that the MD participates more in prospective aspects of working memory, in contrast to the PFC, in which a minority participates in prospective aspects of working memory. A Population Vector analysis revealed that the transformation of sensory-to-motor information occurred during the earlier phase of the delay period in the MD compared with the PFC. These results indicate that reverberating neural circuits constructed by reciprocal connections between the MD and the PFC could be an important component for constructing prospective information in the PFC.
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Population Vector Analysis of Primate Mediodorsal Thalamic Activity during Oculomotor Delayed-Response Performance
Cerebral cortex (New York N.Y. : 1991), 2008Co-Authors: Yumiko Watanabe, K. Takeda, Shintaro FunahashiAbstract:To understand functional roles of the thalamic mediodorsal nucleus (MD) in sensory-to-motor information transformation during spatial working memory performance and compare with those of the dorsolateral prefrontal cortex (DLPFC), we calculated Population Vectors using a Population of MD activities recorded during 2 tasks. In the oculomotor delayed-response (ODR) task, monkeys needed to make a memory-guided saccade to the cue location, whereas in the rotatory oculomotor delayed-response (R-ODR) task, they needed to make a memory-guided saccade 90 o clockwise from the cue direction. The directions of Population Vectors calculated from Populations of cue- and response-period activities were similar to the cue and saccade target directions, respectively, which confirmed that Population Vectors represent information regarding the directions of the visual cue and the saccade target. We then calculated Population Vectors of delay-period activity using a sliding 250-ms time window. In the ODR task, Population Vectors were directed toward the cue direction throughout the delay. However, in the R-ODR task, they gradually rotated from the cue direction to the saccade target direction. Based on a comparison with the results obtained from DLPFC neurons, the rotation of Population Vectors started earlier in the MD than in the DLPFC, suggesting that the motor information regarding forthcoming saccade is provided from the MD.
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The Prefrontal Cortex as a Model System to Understand Representation and Processing of Information
Representation and Brain, 2007Co-Authors: Shintaro FunahashiAbstract:Working memory includes temporary active maintenance of information as well as processing of maintained information. Delay-period activity observed in the prefrontal cortex has been shown to be a neural correlate of the mechanism for short-term active maintenance of information. Using spatial working memory tasks, it was found that a great majority of delay-period activity represents retrospective information (e.g., the location of the visual cue) whereas a minority represents prospective information (e.g., the direction of the forthcoming movement). In addition, using Population Vector analysis using a Population of prefrontal activities, the temporal progression of information processing can be seen as a temporal change of the direction as well as the length of the Population Vector during the delay period. The mechanism participating in the gradual change of information represented by a Population of activities remains unresolved. However, functional interactions among neighboring neurons representing different information and dynamic modulation of these interactions depending on the context of the trial could be a mechanism of this process.
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Population Vector Analysis of Primate Prefrontal Activity during Spatial Working Memory
Cerebral cortex (New York N.Y. : 1991), 2004Co-Authors: K. Takeda, Shintaro FunahashiAbstract:Population Vectors were used to examine information represented by a Population of prefrontal activity and its temporal change during spatial working memory processes while monkeys performed ODR and R-ODR tasks. In the ODR task, monkeys made a saccade to the cue location after the delay, whereas in the R-ODR task, they made a saccade 90° clockwise from the cue location. We first constructed Population Vectors using cue- and response-period activity. The directions of Population Vectors were similar to the cue directions and the saccade target directions, respectively, indicating that Population Vectors correctly represented information regarding directions of visual cues and saccade targets. We then calculated Population Vectors during a 250 ms time-window from the cue presentation to the end of the response period. In the ODR task, all Population Vectors were directed toward the cue direction. However, in the RODR task, the Population Vector gradually rotated during the delay period from the cue direction to the saccade direction. These results indicate that spatial information represented by a Population of prefrontal activity can be shown as the direction of the Population Vector and that its temporal change during spatial working memory tasks can be depicted as the temporal change of the Vector’s direction.
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Population-Vector analysis by primate prefrontal neuron activities.
Journal of biological physics, 2002Co-Authors: Shintaro Funahashi, K. TakedaAbstract:The Population-Vector analysis was applied to visualize neuronal processes of sensory-to-motor transformation in the prefrontal cortex while two monkeys performed two types of oculomotor delayed-response (ODR) tasks. In a standard ODR task, monkeys were required to make a quick eye movement to where thevisual cue had been presented 3 s before, whereas in R-ODR task, monkeys wererequired to make an eye movement 90°clockwise to the direction that the visual cue had been presented. In both tasks, directions of Population Vectors calculated from cue- and response-period activity were almost the same as cue directions and saccade directions, respectively, indicating that Population Vectors of cue- and response-period activity represent information of visual inputs and motor outputs, respectively. To visualize neuronal processes of information transformation, Population Vectors were calculated every 250 ms during a whole trial. In ODR task, Population Vectors weredirected the same direction as the cue direction during the delay period. However, in R-ODR task, Population Vector rotated gradually from the direction similar to the cue direction to the saccade direction during the delay period. These results indicate that visual-to-motor transformation occurs during the delay period and that this process can be visualized by the Population-Vectoranalysis.
Haim Sompolinsky - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear Population codes
Neural computation, 2004Co-Authors: Maoz Shamir, Haim SompolinskyAbstract:Theoretical and experimental studies of distributed neuronal representations of sensory and behavioral variables usually assume that the tuning of the mean firing rates is the main source of information. However, recent theoretical studies have investigated the effect of crosscorrelations in the trial-to-trial fluctuations of the neuronal responses on the accuracy of the representation. Assuming that only the first-order statistics of the neuronal responses are tuned to the stimulus, these studies have shown that in the presence of correlations, similar to those observed experimentally in cortical ensembles of neurons, the amount of information in the Population is limited, yielding nonzero error levels even in the limit of infinitely large Populations of neurons.In this letter, we study correlated neuronal Populations whose higher-order statistics, and in particular response variances, are also modulated by the stimulus. We ask two questions: Does the correlated noise limit the accuracy of the neuronal representation of the stimulus? and, How can a biological mechanism extract most of the information embedded in the higher-order statistics of the neuronal responses? Specifically, we address these questions in the context of a Population of neurons coding an angular variable. We show that the information embedded in the variances grows linearly with the Population size despite the presence of strong correlated noise. This information cannot be extracted by linear readout schemes, including the linear Population Vector. Instead, we propose a bilinear readout scheme that involves spatial decorrelation, quadratic non-linearity, and Population Vector summation. We show that this nonlinear Population Vector scheme yields accurate estimates of stimulus parameters, with an efficiency that grows linearly with the Population size. This code can be implemented using biologically plausible neurons.
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Traveling Waves and the Processing of Weakly Tuned Inputs in a Cortical Network Module
Journal of Computational Neuroscience, 1997Co-Authors: Rani Ben-yishai, David Hansel, Haim SompolinskyAbstract:Recent studies have shown that local cortical feedback can havean important effect on the response of neurons in primary visualcortex to the orientation of visual stimuli. In this work, westudy the role of the cortical feedback in shaping thespatiotemporal patterns of activity in cortex. Two questionsare addressed: one, what are the limitations on the ability ofcortical neurons to lock their activity to rotatingoriented stimuli within a single receptive field? Two, can thelocal architecture of visual cortex lead to the generation ofspontaneous traveling pulses of activity? We study theseissues analytically by a Population-dynamic model of ahypercolumn in visual cortex. The order parameter thatdescribes the macroscopic behavior of the network is thetime-dependent Population Vector of the network. We firststudy the network dynamics under the influence of a weakly tunedinput that slowly rotates within the receptive field. We showthat if the cortical interactions have strong spatialmodulation, the network generates a sharply tuned activityprofile that propagates across the hypercolumn in a path thatis completely locked to the stimulus rotation. The resultantrotating Population Vector maintains a constant angular lagrelative to the stimulus, the magnitude of which grows with thestimulus rotation frequency. Beyond a critical frequency thePopulation Vector does not lock to the stimulus but executes aquasi-periodic motion with an average frequency that is smallerthan that of the stimulus. In the second part we consider thestable intrinsic state of the cortex under the influence of isotropic stimulation. We show that if the local inhibitoryfeedback is sufficiently strong, the network does not settleinto a stationary state but develops spontaneous travelingpulses of activity. Unlike recent models of wave propagation incortical networks, the connectivity pattern in our model isspatially symmetric, hence the direction of propagation ofthese waves is arbitrary. The interaction of these waves withan external-oriented stimulus is studied. It is shown that thesystem can lock to a weakly tuned rotating stimulus if thestimulus frequency is close to the frequency of the intrinsic wave.
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Simple models for reading neuronal Population codes
Proceedings of the National Academy of Sciences of the United States of America, 1993Co-Authors: H. S. Seung, Haim SompolinskyAbstract:In many neural systems, sensory information is distributed throughout a Population of neurons. We study simple neural network models for extracting this information. The inputs to the networks are the stochastic responses of a Population of sensory neurons tuned to directional stimuli. The performance of each network model in psychophysical tasks is compared with that of the optimal maximum likelihood procedure. As a model of direction estimation in two dimensions, we consider a linear network that computes a Population Vector. Its performance depends on the width of the Population tuning curves and is maximal for width, which increases with the level of background activity. Although for narrowly tuned neurons the performance of the Population Vector is significantly inferior to that of maximum likelihood estimation, the difference between the two is small when the tuning is broad. For direction discrimination, we consider two models: a perceptron with fully adaptive weights and a network made by adding an adaptive second layer to the Population Vector network. We calculate the error rates of these networks after exhaustive training to a particular direction. By testing on the full range of possible directions, the extent of transfer of training to novel stimuli can be calculated. It is found that for threshold linear networks the transfer of perceptual learning is nonmonotonic. Although performance deteriorates away from the training stimulus, it peaks again at an intermediate angle. This nonmonotonicity provides an important psychophysical test of these models.
Andrew B. Schwartz - One of the best experts on this subject based on the ideXlab platform.
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Population Vector code: A geometric universal as actuator
Biological Cybernetics, 2008Co-Authors: J. L. Van Hemmen, Andrew B. SchwartzAbstract:The Population Vector code relates directional tuning of single cells and global, directional motion incited by an assembly of neurons. In this paper three things are done. First, we analyze the Population Vector code as a purely geometric construct, focusing attention on its universality. Second, we generalize the algorithm on the basis of its geometrical realization so that the same construct that responds to sensation can function as an actuator for behavioral output. Third, we suggest at least a partial answer to the question of what many maps, neuronal representations of the outside sensory world in space-time, are good for: encoding Vectorial input they enable a direct realization of the Population Vector code.
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Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles.
Reviews in the neurosciences, 2003Co-Authors: S.i. Helms Tillery, Dawn M. Taylor, Andrew B. SchwartzAbstract:We have recently developed a closed-loop environment in which we can test the ability of primates to control the motion of a virtual device using ensembles of simultaneously recorded neurons /29/. Here we use a maximum likelihood method to assess the information about task performance contained in the neuronal ensemble. We trained two animals to control the motion of a computer cursor in three dimensions. Initially the animals controlled cursor motion using arm movements, but eventually they learned to drive the cursor directly from cortical activity. Using a Population Vector (PV) based upon the relation between cortical activity and arm motion, the animals were able to control the cursor directly from the brain in a closed-loop environment, but with difficulty. We added a supervised learning method that modified the parameters of the PV according to task performance (adaptive PV), and found that animals were able to exert much finer control over the cursor motion from brain signals. Here we describe a maximum likelihood method (ML) to assess the information about target contained in neuronal ensemble activity. Using this method, we compared the information about target contained in the ensemble during arm control, during brain control early in the adaptive PV, and during brain control after the adaptive PV had settled and the animal could drive the cursor reliably and with fine gradations. During the arm-control task, the ML was able to determine the target of the movement in as few as 10% of the trials, and as many as 75% of the trials, with an average of 65%. This average dropped when the animals used a Population Vector to control motion of the cursor. On average we could determine the target in around 35% of the trials. This low percentage was also reflected in poor control of the cursor, so that the animal was unable to reach the target in a large percentage of trials. Supervised adjustment of the Population Vector parameters produced new weighting coefficients and directional tuning parameters for many neurons. This produced a much better performance of the brain-controlled cursor motion. It was also reflected in the maximum likelihood measure of cell activity, producing the correct target based only on neuronal activity in over 80% of the trials on average. The changes in maximum likelihood estimates of target location based on ensemble firing show that an animal's ability to regulate the motion of a cortically controlled device is not crucially dependent on the experimenter's ability to estimate intention from neuronal activity.
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Direct cortical representation of drawing
Science (New York N.Y.), 1994Co-Authors: Andrew B. SchwartzAbstract:How the intention to act results in movement is a fundamental question of brain organization. Recent work has shown that this operation involves the cooperative interaction of large neuronal Populations. A Population Vector method, by transforming neuronal activity to the spatial domain, was used to visualize the motor cortical representation of the hand's trajectory made by rhesus monkeys as they drew spirals. Hand path was accurately reflected by a series of Population Vectors calculated throughout the task. A psychophysical rule relating speed to curvature, the "power law," was found in this cortical representation. The relative timing between each Population Vector and the corresponding portion of the movement was variable. The Population Vectors only preceded the movement in a predictive manner in portions of the spiral where the radius of curvature was greater than 6 centimeters. These results show that the movement trajectory is an important determinant of motor cortical activity and that this aspect of motor cortical activity may contribute only to discrete portions of the drawing movement.
Stephen Scott - One of the best experts on this subject based on the ideXlab platform.
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dissociation between hand motion and Population Vectors from neural activity in motor cortex
Nature, 2001Co-Authors: Stephen Scott, Paul L Gribble, Kirsten M Graham, William D CabelAbstract:The Population Vector hypothesis was introduced almost twenty years ago to illustrate that a Population Vector constructed from neural activity in primary motor cortex (MI) of non-human primates could predict the direction of hand movement during reaching. Alternative explanations for this Population signal have been suggested but could not be tested experimentally owing to movement complexity in the standard reaching model. We re-examined this issue by recording the activity of neurons in contralateral MI of monkeys while they made reaching movements with their right arms oriented in the horizontal plane-where the mechanics of limb motion are measurable and anisotropic. Here we found systematic biases between the Population Vector and the direction of hand movement. These errors were attributed to a non-uniform distribution of preferred directions of neurons and the non-uniformity covaried with peak joint power at the shoulder and elbow. These observations contradict the Population Vector hypothesis and show that non-human primates are capable of generating reaching movements to spatial targets even though Population Vectors based on MI activity do not point in the direction of hand motion.
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An alternative interpretation of Population Vector rotation in macaque motor cortex.
Neuroscience letters, 1999Co-Authors: Paul Cisek, Stephen ScottAbstract:Abstract Neural recordings from the primary motor cortex of monkeys performing movements at an angle to a cue stimulus have yielded two main results: (A) the Population Vector rotates from the direction of the cue to the direction of movement, and (B) cells with intermediate preferred directions are recruited during the middle of this rotation. These results have been interpreted as the neural correlates of a process of ‘mental rotation’. Here we propose that results A and B are also consistent with an altemate hypothesis of ‘response substitution’, given four well known features of cortical neurophysiology.
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Changes in motor cortex activity during reaching movements with similar hand paths but different arm postures.
Journal of neurophysiology, 1995Co-Authors: Stephen Scott, John F. KalaskaAbstract:1. Neuronal activity was recorded in the motor cortex of a monkey that performed reaching movements with the use of two different arm postures. In the first posture (control), the monkey used its natural arm orientation, approximately in the sagittal plane. In the second posture (abducted), the monkey had to adduct its elbow nearly to shoulder level to grasp the handle. The path of the hand between targets was similar in both arm postures, but the joint kinematics and kinetics were different. 2. In both postures, the activity of single cells was often broadly tuned with movement direction and static arm posture over the targets. In a large proportion of cells, either the level of tonic activity, the directional tuning, or both, varied between the two postures during the movement and target hold periods. 3. For most directions of movement, there was a statistically significant difference in the direction of the Population Vector for the two arm postures. Furthermore, whereas the Population Vector tended to point in the direction of movement for the control posture, there was a poorer correspondence between the direction of movement and the Population Vector for the abducted posture. These observed changes are inconsistent with the notion that the motor cortex encodes purely hand trajectory in space.