Biomedical subjects

Bruno B Averbeck

Publications and source records attributed to Bruno B Averbeck.

18 recordsLinked to original sources

PubMed2006

Probabilistic encoding of vocalizations in macaque ventral lateral prefrontal cortex.

We examined strategies for classifying macaque vocalizations into their corresponding categories, as well as whether or not there was evidence that prefrontal auditory neurons were related to this process. We found that static estimates of the spectral and temporal contrasts of the calls were not effective features for discriminating among the call classes. A hidden Markov model (HMM), however, was more effective at discriminating among the call classes, reaching a performance of almost 75% correct. Finally, we found that the responses of prefrontal auditory neurons could be predicted more effectively as linear functions of the probabilistic output of the HMM than as linear functions of the spectral features of the calls. This provides evidence that, for call recognition, the macaque auditory system likely performs dynamic processing of vocalizations, and that prefrontal auditory neurons carry a signal related to the output of this processing.

Acoustic Stimulation↗

PubMed2006

Integration of auditory and visual communication information in the primate ventrolateral prefrontal cortex.

The integration of auditory and visual stimuli is crucial for recognizing objects, communicating effectively, and navigating through our complex world. Although the frontal lobes are involved in memory, communication, and language, there has been no evidence that the integration of communication information occurs at the single-cell level in the frontal lobes. Here, we show that neurons in the macaque ventrolateral prefrontal cortex (VLPFC) integrate audiovisual communication stimuli. The multisensory interactions included both enhancement and suppression of a predominantly auditory or a predominantly visual response, although multisensory suppression was the more common mode of response. The multisensory neurons were distributed across the VLPFC and within previously identified unimodal auditory and visual regions (O'Scalaidhe et al., 1997; Romanski and Goldman-Rakic, 2002). Thus, our study demonstrates, for the first time, that single prefrontal neurons integrate communication information from the auditory and visual domains, suggesting that these neurons are an important node in the cortical network responsible for communication.

Acoustic Stimulation↗

PubMed2006

Effects of noise correlations on information encoding and decoding.

Response variability is often correlated across populations of neurons, and these noise correlations may play a role in information coding. In previous studies, this possibility has been examined from the encoding and decoding perspectives. Here we used d prime and related information measures to examine how studies of noise correlations from these two perspectives are related. We found that for a pair of neurons, the effect of noise correlations on information decoding can be zero when the effect of noise correlations on the information encoded obtains its largest positive or negative values. Furthermore, there can be no effect of noise correlations on the information encoded when it has an effect on information decoding. We also measured the effect of noise correlations on information encoding and decoding in simultaneously recorded neurons in the supplementary motor area to see how well d prime accounted for the information actually present in the neural responses and to see how noise correlations affected encoding and decoding in real data. These analyses showed that d prime provides an accurate measure of information encoding and decoding in our population of neurons. We also found that the effect of noise correlations on information encoding was somewhat larger than the effect of noise correlations on information decoding, but both were relatively small. Finally, as predicted theoretically, the effects of correlations were slightly greater for larger ensembles (3-8 neurons) than for pairs of neurons.

Action Potentials↗

PubMed2006

Activity in prefrontal cortex during dynamic selection of action sequences.

Completing everyday tasks often requires the execution of action sequences matched to a particular problem. To study the neural processes underlying these behaviors, we trained monkeys to produce a series of eye movements according to a sequence that changed unpredictably from one block of trials to the next. We then applied a decoding algorithm to estimate which sequence was being represented by the ensemble activity in prefrontal cortex. We found that the sequence predicted by this analysis changed gradually from the sequence that had been correct in the previous block to the sequence that was correct in the current block, closely following the fraction of executed movements that were consistent with the corresponding sequence. Thus, the neural activity dynamically tracked the monkeys' uncertainty about the correct sequence of actions. These results are consistent with prefrontal involvement in representing subjective knowledge of the correct action sequence.

Algorithms↗

PubMed2006

Neural correlations, population coding and computation.

How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.

Action Potentials↗

PubMed2005

Dynamics of parietal neural activity during spatial cognitive processing.

Dynamic neural processing unrelated to changes in sensory input or motor output is likely to be a hallmark of cognitive operations. Here we show that neural representations of space in parietal cortex are dynamic while monkeys perform a spatial cognitive operation on a static visual stimulus. We recorded neural activity in area 7a during a visual maze task in which monkeys mentally followed a path without moving their eyes. We found that the direction of the followed path could be recovered from neuronal population activity. When the monkeys covertly processed a path that turned, the population representation of path direction shifted in the direction of the turn. This neural population dynamic took place during a period of unchanging visual input and showed characteristics of both serial and parallel processing. The data suggest that the dynamic evolution of parietal neuronal activity is associated with the progression of spatial cognitive operations.

Action Potentials↗

PubMed2005

Neural correlates of spatial judgement during object construction in parietal cortex.

We recorded the activity of parietal area 7a neurons in monkeys performing an object construction task. In each trial, a model object consisting of a variable arrangement of squares was presented, followed after a delay by a copy of the model object that was missing a single square. Monkeys replaced the missing square to reconstruct the model configuration. Activity of many 7a neurons varied systematically with the position of the missing square and predicted where monkeys were going to add parts to the object they were building. The location of the missing square was a computed spatial datum important to object construction which did not correlate with the retinal location of a visual stimulus or the direction of the required motor response. The population of cells coding this coordinate was generally inactive when the same spatial locations were made relevant by visual targets to which monkeys either planned saccades or directed attention in other behavioral contexts. The data suggest that some parietal neurons participate in neural representations of space that reflect spatial cognitive as opposed to sensorimotor processing, coding the results of spatial computations performed on visual stimuli to meet cognitive objectives.

Animals↗

PubMed2004

Neural representation of vocalizations in the primate ventrolateral prefrontal cortex.

In this study, we examined the role of the ventrolateral prefrontal cortex in encoding communication stimuli. Specifically, we recorded single-unit responses from the ventrolateral prefrontal cortext (vlPFC) in awake behaving rhesus macaques in response to species-specific vocalizations. We determined the selectivity of vlPFC cells for 10 types of rhesus vocalizations and also asked what types of vocalizations cluster together in the neuronal response. The data from the present study demonstrate that vlPFC auditory neurons respond to a variety of species-specific vocalizations from a previously characterized library. Most vlPFC neurons responded to two to five vocalizations, while a small percentage of cells responded either selectively to a particular vocalization type or nonselectively to most auditory stimuli tested. Use of information theoretic approaches to examine vocalization tuning indicates that on average, vlPFC neurons encode information about one or two vocalizations. Further analysis of the types of vocalizations that vlPFC cells typically respond to using hierarchical cluster analysis suggests that the responses of vlPFC cells to multiple vocalizations is not based strictly on the call's function or meaning but may be due to other features including acoustic morphology. These data are consistent with a role for the primate vlPFC in assessing distinctive acoustic features.

Animals↗

PubMed2004

Parietal representation of hand velocity in a copy task.

We recorded neural activity from ensembles of neurons in areas 5 and 2 of parietal cortex, while two monkeys copied triangles, squares, trapezoids, and inverted triangles and used both linear and nonlinear models to predict the hand velocity from the neural activity of the ensembles. The linear model generally outperformed the nonlinear model, suggesting a reasonably linear relation between the neural activity and the hand velocity. We also found that the average transfer function of the linear model fit to individual cells was a low-pass filter because the neural response had considerable high-frequency power, whereas the hand velocity only had power at frequencies below approximately 5 Hz. Increasing the width of the transfer function, up to a width of 700-800 ms, improved the fit of the model. Furthermore, the Rsqr of the linear model improved monotonically with the number of cells in the ensemble, saturating at 60-80% for a filter width of 700 ms. Finally, it was found that including an interaction term, which allowed the transfer function to shift with the eye position, did not improve the fit of the model. Thus ensemble neural responses in superior parietal cortex provide a high-fidelity, linear representation of hand kinematics within our task.

Action Potentials↗

PubMed2004

Participation of primary motor cortical neurons in a distributed network during maze solution: representation of spatial parameters and time-course comparison with parietal area 7a.

Traditionally, primary motor cortex (M1) has been thought to be involved solely in planning and generating movements. Recent evidence suggests that the arm area of M1 plays a role in other functions, such as the representation of serial order (Pellizzer et al. 1995, Science 269:702-705; Carpenter et al. 1999, Science 283:1752-1757) and spatial processing (Georgopoulos et al. 1989, Science 243:234-236). Previous studies of such cognitive processes have used tasks in which a directed arm movement was required, raising a question as to whether this brain area is involved in cognitive processing per se, or whether such cognitive signals may be gated into the arm area of M1 only when arm movements are required. To study this question, we developed a task that required a spatial analysis of a complex visual stimulus, but required no arm movement as a response. In this task, monkeys were shown an octagonal maze. After an imposed delay of 2 to 2.5 s, they indicated whether a path that emanated from the center of the maze exited at the perimeter (exit maze) or terminated within the maze (no-exit maze) by pressing a pedal with their left or right foot, respectively. We recorded from 785 cells from the arm area of M1 from two monkeys during the delay period of the maze task. We found that cell activity was influenced by both the exit status and the direction of the path, beginning soon after the maze was displayed. This activity was not related to the activation of arm muscles, suggesting that the directional signals observed represented abstract spatial aspects of maze processing. Finally, we compared maze-related activity of M1 neurons with those recorded from posterior parietal area 7a, reported previously (Crowe et al. 2004). Interestingly, cells from each area exhibited similar properties. Both the exit status and path direction were encoded by cells in M1 and 7a, although to different extents. An analysis of the time-course of the neural representation of these factors revealed that area 7a and M1 begin to encode these factors at the same time, suggesting these brain areas are part of a distributed system performing the spatial computations involved in maze solution.

Action Potentials↗

PubMed2004

Learning and production of movement sequences: behavioral, neurophysiological, and modeling perspectives.

A wave of recent behavioral studies has generated a new wealth of parametric observations about serial order behavior. What was a trickle of neurophysiological studies has grown to a steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we review major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2 x N, discrete sequence production, and serial reaction time. These tasks populate a continuum from higher to lower degrees of internal control of sequential organization and probe important contemporary issues such as the nature of working-memory representations for sequential behavior, and the development and role of chunks in hierarchical control. The main movement classes reviewed are speech and keypressing, both involving small amplitude movements amenable to parametric study. A synopsis of serial order models, vis-a-vis major empirical findings leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been complemented by successful prediction of distinctively patterned electrophysiological recordings. In lateral prefrontal cortex, parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model--the N-STREAMS neural network model--exemplifies ongoing attempts to accommodate a broad range of both behavioral and neurobiological data within a CQ-consistent theory.

Animals↗

PubMed2004

Coding and transmission of information by neural ensembles.

The brain processes information about sensory stimuli and motor intentions using a massive ensemble of neurons arrayed in parallel. Individual neurons receive convergent inputs from thousands of other neurons, leading to the possibility that patterns of spikes across the input neurons might be crucial components of the neural code. Recently, advances in multielectrode recording techniques have allowed several laboratories to investigate the nature of the interactions between neurons, and their potential role in information coding. Several recent studies have found that the amount of information coded by correlated activity about sensory and motor variables is small, casting doubt on the hypothesis that correlations between pairs of neurons are important for information coding. However, other studies have documented the appearance of coherent oscillations, during particular task epochs and conditions that require selective processing of sensory information, supporting the hypothesis that coherent oscillations between neurons might reflect the dynamic flow of information in the brain.

Animals↗

PubMed2004

Neural activity in primate parietal area 7a related to spatial analysis of visual mazes.

Cognitive psychological studies of humans and monkeys solving visual mazes have provided evidence that a covert analysis of the maze takes place during periods of eye fixation interspersed between saccades, or when mazes are solved without eye movements. We investigated the neural basis of this process in posterior parietal cortex by recording the activity of single neurons in area 7a during maze solution. Monkeys were required to determine from a single point of fixation whether a critical path through the maze reached an exit or a blind ending. We found that during this process the activity of approximately one in four neurons in area 7a was spatially tuned to maze path direction. We obtained evidence that path tuning did not reflect a covert saccade plan insofar as the majority of neurons active during maze solution were not active on a delayed-saccade control task, and the minority that were active on both tasks did not exhibit congruent spatial tuning in the two conditions. We also obtained evidence that path tuning during maze solution was not due to the locations of visual receptive fields mapped outside the behavioral context of maze solution, in that receptive field centers and preferred path directions were not spatially aligned. Finally, neurons tuned to path direction were not present in area 7a when a naïve animal viewed the same visual maze stimuli but did not solve them. These data support the hypothesis that path tuning in parietal cortex is not due to the lower level visual features of the maze stimulus, but rather is associated with maze solution, and as such, reflects a cognitive process applied to a complex visual stimulus.

Animals↗

PubMed2004

Principal and independent components of macaque vocalizations: constructing stimuli to probe high-level sensory processing.

Neurons in high-level sensory cortical areas respond to complex features in sensory stimuli. Feature elimination is a useful technique for studying these responses. In this approach, a complex stimulus, which evokes a neuronal response, is simplified, and if the cell responds to the reduced stimulus, it is considered selective for the remaining features. We have developed a feature-elimination technique that uses either the principal or the independent components of a stimulus to define a subset of features, to which a neuron might be sensitive. The original stimulus can be filtered using these components, resulting in a stimulus that retains only a fraction of the features present in the original. We demonstrate the use of this technique on macaque vocalizations, an important class of stimuli being used to study auditory function in awake, behaving primate experiments. We show that principal-component analysis extracts features that are closely related to the dominant Fourier components of the stimuli, often called formants in the study of speech perception. Conversely, independent-component analysis extracts features that preserve the relative phase across a set of harmonically related frequencies. We have used several statistical techniques to explore the original and filtered stimuli, as well as the components extracted by each technique. This novel approach provides a powerful method for determining the essential features within complex stimuli that activate higher-order sensory neurons.

Acoustic Stimulation↗

PubMed2003

Neural noise and movement-related codes in the macaque supplementary motor area.

We analyzed the variability of spike counts and the coding capacity of simultaneously recorded pairs of neurons in the macaque supplementary motor area (SMA). We analyzed the mean-variance functions for single neurons, as well as signal and noise correlations between pairs of neurons. All three statistics showed a strong dependence on the bin width chosen for analysis. Changes in the correlation structure of single neuron spike trains over different bin sizes affected the mean-variance function, and signal and noise correlations between pairs of neurons were much smaller at small bin widths, increasing monotonically with the width of the bin. Analyses in the frequency domain showed that the noise between pairs of neurons, on average, was most strongly correlated at low frequencies, which explained the increase in noise correlation with increasing bin width. The coding performance was analyzed to determine whether the temporal precision of spike arrival times and the interactions within and between neurons could improve the prediction of the upcoming movement. We found that in approximately 62% of neuron pairs, the arrival times of spikes at a resolution between 66 and 40 msec carried more information than spike counts in a 200 msec bin. In addition, in 19% of neuron pairs, inclusion of within (11%)- or between-neuron (8%) correlations in spike trains improved decoding accuracy. These results suggest that in some SMA neurons elements of the spatiotemporal pattern of activity may be relevant for neural coding.

Action Potentials↗

PubMed2003

Neural activity in prefrontal cortex during copying geometrical shapes. I. Single cells encode shape, sequence, and metric parameters.

In drawing a copy of a geometrical shape, a sequence of movements must be produced to represent the sides of the object in the proper spatial relationship. We investigated neural mechanisms of this process by training monkeys to draw (using a joystick) copies of geometrical shapes (triangles, squares, trapezoids and inverted triangles) presented on a video monitor while recording single cell activity in prefrontal cortex. The drawing trajectories monkeys produced were divided into a series of discrete segments, varying in direction and length. We performed a stepwise multiple linear regression analysis to identify those copy parameters significantly influencing cell activity. The copied shape (e.g., triangle, square) and the serial position of the segment within each trajectory were the most prevalent effects (in 46% and 43% of cells, respectively), followed by segment direction (32%) and length (16%). Effects of temporal factors (maximum segment speed and time to maximum segment speed) were less frequent. These results demonstrate that prefrontal neurons encode several spatial and sequence variables that define copy trajectories. We also found that specific groupings of significant effects tended to occur together in single neurons. Specifically, single neurons simultaneously processed the serial position of a segment within each trajectory along with the corresponding spatial (but not temporal) attributes of that segment (i.e., direction and length), as well as with the overall shape to which the segments belong. Finally, we discovered that relationships between neural activity and segment serial position were systematic in many instances, described by monotonically increasing and decreasing functions, as well as parabolic functions. These findings indicate that, within the copying task, the serial segment position is a key factor for neural activity in the periprincipalis area of the prefrontal cortex.

Action Potentials↗

PubMed2003

Neural activity in prefrontal cortex during copying geometrical shapes. II. Decoding shape segments from neural ensembles.

We trained two monkeys to draw copies of geometrical shapes (e.g. squares, triangles) using a joystick, and found that several variables describing the arm trajectories were encoded in the activity of individual prefrontal neurons (Averbeck et al. 2003). Copy trajectories were drawn as sequences of segments, identified by the serial order in which they were drawn and the shape that they together produced. Here we use linear discriminant analysis to test how well the segments of copied shapes could be decoded from the neural activity patterns of small ensembles (3-22 neurons) of simultaneously recorded cells in prefrontal cortex. Using this analysis, the proper segment (drawn by the monkey) was correctly decoded from the ensemble activity pattern during the drawing of that segment in 60-80% of the cases when the largest ensembles were considered. The information transmitted by these ensembles, as well as by single neurons, was also calculated. We found that the information transmitted by the ensembles increased on average with the number of neurons they contained. Each neuron conveyed information about multiple segments within the drawing trajectory, suggesting that neurons were 'broadly tuned' across segments and that the neural code of segment was distributed.

Action Potentials↗

PubMed2002

Parallel processing of serial movements in prefrontal cortex.

A key idea in Lashley's formulation of the problem of serial order in behavior is the postulated neural representation of all serial elements before the action begins. We studied this question by recording the activity of individual neurons simultaneously in small ensembles in prefrontal cortex while monkeys copied geometrical shapes shown on a screen. Monkeys drew the shapes as sequences of movement segments, and these segments were associated with distinct patterns of neuronal ensemble activity. Here we show that these patterns were present during the time preceding the actual drawing. The rank of the strength of representation of a segment in the neuronal population during this time, as assessed by discriminant analysis, predicted the serial position of the segment in the motor sequence. An analysis of errors in copying and their neural correlates supplied additional evidence for this code and provided a neural basis for Lashley's hypothesis that errors in motor sequences would be most likely to occur when executing elements that had prior representations of nearly equal strength.

Animals↗