William T. Newsome

Bio: William T. Newsome is an academic researcher from Stanford University. The author has contributed to research in topics: Visual cortex & Motion perception. The author has an hindex of 77, co-authored 137 publications receiving 36315 citations. Previous affiliations of William T. Newsome include Howard Hughes Medical Institute & California Institute of Technology.

The variable discharge of cortical neurons: implications for connectivity, computation, and information coding

Abstract: Cortical neurons exhibit tremendous variability in the number and temporal distribution of spikes in their discharge patterns. Furthermore, this variability appears to be conserved over large regions of the cerebral cortex, suggesting that it is neither reduced nor expanded from stage to stage within a processing pathway. To investigate the principles underlying such statistical homogeneity, we have analyzed a model of synaptic integration incorporating a highly simplified integrate and fire mechanism with decay. We analyzed a “high-input regime” in which neurons receive hundreds of excitatory synaptic inputs during each interspike interval. To produce a graded response in this regime, the neuron must balance excitation with inhibition. We find that a simple integrate and fire mechanism with balanced excitation and inhibition produces a highly variable interspike interval, consistent with experimental data. Detailed information about the temporal pattern of synaptic inputs cannot be recovered from the pattern of output spikes, and we infer that cortical neurons are unlikely to transmit information in the temporal pattern of spike discharge. Rather, we suggest that quantities are represented as rate codes in ensembles of 50‐ 100 neurons. These column-like ensembles tolerate large fractions of common synaptic input and yet covary only weakly in their spike discharge. We find that an ensemble of 100 neurons provides a reliable estimate of rate in just one interspike interval (10‐50 msec). Finally, we derived an expression for the variance of the neural spike count that leads to a stable propagation of signal and noise in networks of neurons—that is, conditions that do not impose an accumulation or diminution of noise. The solution implies that single neurons perform simple algebra resembling averaging, and that more sophisticated computations arise by virtue of the anatomical convergence of novel combinations of inputs to the cortical column from external sources.

The analysis of visual motion: a comparison of neuronal and psychophysical performance.

Abstract: We compared the ability of psychophysical observers and single cortical neurons to discriminate weak motion signals in a stochastic visual display. All data were obtained from rhesus monkeys trained to perform a direction discrimination task near psychophysical threshold. The conditions for such a comparison were ideal in that both psychophysical and physiological data were obtained in the same animals, on the same sets of trials, and using the same visual display. In addition, the psychophysical task was tailored in each experiment to the physiological properties of the neuron under study; the visual display was matched to each neuron's preference for size, speed, and direction of motion. Under these conditions, the sensitivity of most MT neurons was very similar to the psychophysical sensitivity of the animal observers. In fact, the responses of single neurons typically provided a satisfactory account of both absolute psychophysical threshold and the shape of the psychometric function relating performance to the strength of the motion signal. Thus, psychophysical decisions in our task are likely to be based upon a relatively small number of neural signals. These signals could be carried by a small number of neurons if the responses of the pooled neurons are statistically independent. Alternatively, the signals may be carried by a much larger pool of neurons if their responses are partially intercorrelated.

Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey

Abstract: We recorded the activity of single neurons in the posterior parietal cortex (area LIP) of two rhesus monkeys while they discriminated the direction of motion in random-dot visual stimuli. The visual task was similar to a motion discrimination task that has been used in previous investigations of motion-sensitive regions of the extrastriate cortex. The monkeys were trained to decide whether the direction of motion was toward one of two choice targets that appeared on either side of the random-dot stimulus. At the end of the trial, the monkeys reported their direction judgment by making an eye movement to the appropriate target. We studied neurons in LIP that exhibited spatially selective persistent activity during delayed saccadic eye movement tasks. These neurons are thought to carry high-level signals appropriate for identifying salient visual targets and for guiding saccadic eye movements. We arranged the motion discrimination task so that one of the choice targets was in the LIP neuron's response field (RF) while the other target was positioned well away from the RF. During motion viewing, neurons in LIP altered their firing rate in a manner that predicted the saccadic eye movement that the monkey would make at the end of the trial. The activity thus predicted the monkey's judgment of motion direction. This predictive activity began early in the motion-viewing period and became increasingly reliable as the monkey viewed the random-dot motion. The neural activity predicted the monkey's direction judgment on both easy and difficult trials (strong and weak motion), whether or not the judgment was correct. In addition, the timing and magnitude of the response was affected by the strength of the motion signal in the stimulus. When the direction of motion was toward the RF, stronger motion led to larger neural responses earlier in the motion-viewing period. When motion was away from the RF, stronger motion led to greater suppression of ongoing activity. Thus the activity of single neurons in area LIP reflects both the direction of an impending gaze shift and the quality of the sensory information that instructs such a response. The time course of the neural response suggests that LIP accumulates sensory signals relevant to the selection of a target for an eye movement.

A selective impairment of motion perception following lesions of the middle temporal visual area (MT)

Abstract: Physiological experiments indicate that the middle temporal visual area (MT) of primates plays a prominent role in the cortical analysis of visual motion. We investigated the role of MT in visual perception by examining the effect of chemical lesions of MT on psychophysical thresholds. We trained rhesus monkeys on psychophysical tasks that enabled us to assess their sensitivity to motion and to contrast. For motion psychophysics, we employed a dynamic random dot display that permitted us to vary the intensity of a motion signal in the midst of masking motion noise. We measured the threshold intensity for which the monkey could successfully complete a direction discrimination. In the contrast task, we measured the threshold contrast for which the monkeys could successfully discriminate the orientation of stationary gratings. Injections of ibotenic acid into MT caused striking elevations in motion thresholds, but had little or no effect on contrast thresholds. The results indicate that neural activity in MT contributes selectively to the perception of motion.

Context-dependent computation by recurrent dynamics in prefrontal cortex

Abstract: Prefrontal cortex is thought to have a fundamental role in flexible, context-dependent behaviour, but the exact nature of the computations underlying this role remains largely unknown. In particular, individual prefrontal neurons often generate remarkably complex responses that defy deep understanding of their contribution to behaviour. Here we study prefrontal cortex activity in macaque monkeys trained to flexibly select and integrate noisy sensory inputs towards a choice. We find that the observed complexity and functional roles of single neurons are readily understood in the framework of a dynamical process unfolding at the level of the population. The population dynamics can be reproduced by a trained recurrent neural network, which suggests a previously unknown mechanism for selection and integration of task-relevant inputs. This mechanism indicates that selection and integration are two aspects of a single dynamical process unfolding within the same prefrontal circuits, and potentially provides a novel, general framework for understanding context-dependent computations.

A default mode of brain function.

Abstract: A baseline or control state is fundamental to the understanding of most complex systems. Defining a baseline state in the human brain, arguably our most complex system, poses a particular challenge. Many suspect that left unconstrained, its activity will vary unpredictably. Despite this prediction we identify a baseline state of the normal adult human brain in terms of the brain oxygen extraction fraction or OEF. The OEF is defined as the ratio of oxygen used by the brain to oxygen delivered by flowing blood and is remarkably uniform in the awake but resting state (e.g., lying quietly with eyes closed). Local deviations in the OEF represent the physiological basis of signals of changes in neuronal activity obtained with functional MRI during a wide variety of human behaviors. We used quantitative metabolic and circulatory measurements from positron-emission tomography to obtain the OEF regionally throughout the brain. Areas of activation were conspicuous by their absence. All significant deviations from the mean hemisphere OEF were increases, signifying deactivations, and resided almost exclusively in the visual system. Defining the baseline state of an area in this manner attaches meaning to a group of areas that consistently exhibit decreases from this baseline, during a wide variety of goal-directed behaviors monitored with positron-emission tomography and functional MRI. These decreases suggest the existence of an organized, baseline default mode of brain function that is suspended during specific goal-directed behaviors.

A Neural Substrate of Prediction and Reward

Abstract: The capacity to predict future events permits a creature to detect, model, and manipulate the causal structure of its interactions with its environment. Behavioral experiments suggest that learning is driven by changes in the expectations about future salient events such as rewards and punishments. Physiological work has recently complemented these studies by identifying dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events. Taken together, these findings can be understood through quantitative theories of adaptive optimizing control.

Distributed Hierarchical Processing in the Primate Cerebral Cortex

Abstract: In recent years, many new cortical areas have been identified in the macaque monkey. The number of identified connections between areas has increased even more dramatically. We report here on (1) a summary of the layout of cortical areas associated with vision and with other modalities, (2) a computerized database for storing and representing large amounts of information on connectivity patterns, and (3) the application of these data to the analysis of hierarchical organization of the cerebral cortex. Our analysis concentrates on the visual system, which includes 25 neocortical areas that are predominantly or exclusively visual in function, plus an additional 7 areas that we regard as visual-association areas on the basis of their extensive visual inputs. A total of 305 connections among these 32 visual and visual-association areas have been reported. This represents 31% of the possible number of pathways if each area were connected with all others. The actual degree of connectivity is likely to be closer to 40%. The great majority of pathways involve reciprocal connections between areas. There are also extensive connections with cortical areas outside the visual system proper, including the somatosensory cortex, as well as neocortical, transitional, and archicortical regions in the temporal and frontal lobes. In the somatosensory/motor system, there are 62 identified pathways linking 13 cortical areas, suggesting an overall connectivity of about 40%. Based on the laminar patterns of connections between areas, we propose a hierarchy of visual areas and of somatosensory/motor areas that is more comprehensive than those suggested in other recent studies. The current version of the visual hierarchy includes 10 levels of cortical processing. Altogether, it contains 14 levels if one includes the retina and lateral geniculate nucleus at the bottom as well as the entorhinal cortex and hippocampus at the top. Within this hierarchy, there are multiple, intertwined processing streams, which, at a low level, are related to the compartmental organization of areas V1 and V2 and, at a high level, are related to the distinction between processing centers in the temporal and parietal lobes. However, there are some pathways and relationships (about 10% of the total) whose descriptions do not fit cleanly into this hierarchical scheme for one reason or another. In most instances, though, it is unclear whether these represent genuine exceptions to a strict hierarchy rather than inaccuracies or uncertainities in the reported assignment.

Conflict monitoring and cognitive control.

Abstract: A neglected question regarding cognitive control is how control processes might detect situations calling for their involvement. The authors propose here that the demand for control may be evaluated in part by monitoring for conflicts in information processing. This hypothesis is supported by data concerning the anterior cingulate cortex, a brain area involved in cognitive control, which also appears to respond to the occurrence of conflict. The present article reports two computational modeling studies, serving to articulate the conflict monitoring hypothesis and examine its implications. The first study tests the sufficiency of the hypothesis to account for brain activation data, applying a measure of conflict to existing models of tasks shown to engage the anterior cingulate. The second study implements a feedback loop connecting conflict monitoring to cognitive control, using this to simulate a number of important behavioral phenomena.

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Abstract: Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.