SummaryPrefrontal cortex is thought to play a fundamental role in flexible, context-dependent behavior, but the exact nature of the computations underlying this role remains largely mysterious. In particular, individual prefrontal neurons often generate remarkably complex responses that defy deep understanding of their contribution to behavior. Here we study prefrontal cortex in 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 implies 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.

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. Since the earliest single-unit recordings, it has been apparent that the irregularity of the neural discharge might limit the sensitivity of the nervous system to sensory stimuli (for review, see Rieke et al., 1997). In visual cortex, for example, repeated presentations of an identical stimulus elicit a variable number of action potentials (Schiller et al., 1976;Dean, 1981;Tolhurst et al., 1983;Vogels et al., 1989;Snowden et al., 1992;Britten et al., 1993), and the time between successive action potentials [interspike interval (ISI)] is highly irregular (Tomko and Crapper, 1974;Softky and Koch, 1993). These observations have led to numerous speculations on the nature of the neural code (Abeles, 1991;Konig et al., 1996;Rieke et al., 1997). On the one hand, the irregular timing of spikes could convey information, imparting broad information bandwidth on the neural spike train, much like a Morse code. Alternatively this irregularity may reflect noise, relegating the signal carried by the neuron to a crude estimate of spike rate.In principle we could ascertain...

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.

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.

Single neurons can signal subtle changes in the sensory environment with surprising fidelity, often matching the perceptual sensitivity of trained psychophysical observers. This similarity poses an intriguing puzzle: why is psychophysical sensitivity not greater than that of single neurons? Pooling responses across neurons should average out noise in the activity of single cells, leading to substantially improved psychophysical performance. If, however, noise is correlated among these neurons, the beneficial effects of pooling would be diminished. To assess correlation within a pool, the responses of pairs of neurons were recorded simultaneously during repeated stimulus presentations. We report here that the observed covariation in spike count was relatively weak, the correlation coefficient averaging 0.12. A theoretical analysis revealed, however, that weak correlation can limit substantially the signalling capacity of the pool. In addition, theory suggests a relationship between neuronal responses and psychophysical decisions which may prove useful for identifying cell populations underlying specific perceptual capacities.

We have previously documented the exquisite motion sensitivity of neurons in extrastriate area MT by studying the relationship between their responses and the direction and strength of visual motion signals delivered to their receptive fields. These results suggested that MT neurons might provide the signals supporting behavioral choice in visual discrimination tasks. To approach this question from another direction, we have now studied the relationship between the discharge of MT neurons and behavioral choice, independently of the effects of visual stimulation. We found that trial-to-trial variability in neuronal signals was correlated with the choices the monkey made. Therefore, when a directionally selective neuron in area MT fires more vigorously, the monkey is more likely to make a decision in favor of the preferred direction of the cell. The magnitude of the relationship was modest, on average, but was highly significant across a sample of 299 cells from four monkeys. The relationship was present for all stimuli (including those without a net motion signal), and for all but the weakest responses. The relationship was reduced or eliminated when the demands of the task were changed so that the directional signal carried by the cell was less informative. The relationship was evident within 50 ms of response onset, and persisted throughout the stimulus presentation. On average, neurons that were more sensitive to weak motion signals had a stronger relationship to behavior than those that were less sensitive. These observations are consistent with the idea that neuronal signals in MT are used by the monkey to determine the direction of stimulus motion. The modest relationship between behavioral choice and the discharge of any one neuron, and the prevalence of the relationship across the population, make it likely that signals from many neurons are pooled to form the data on which behavioral choices are based.

Neural responses are typically characterized by computing the mean firing rate. Yet response variability can exist across trials. Many studies have examined the impact of a stimulus on the mean response, yet few have examined the impact on response variability. We measured neural variability in 13 extracellularly-recorded datasets and one intracellularly-recorded dataset from 7 areas spanning the four cortical lobes. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observable in membrane potential recordings, in the spiking of individual neurons, and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving, or anaesthetized. This widespread variability decline suggests a rather general property of cortex: that its state is stabilized by an input.

Psychologists and economists have long appreciated the contribution of reward history and expectation to decision-making. Yet we know little about how specific histories of choice and reward lead to an internal representation of the "value" of possible actions. We approached this problem through an integrated application of behavioral, computational, and physiological techniques. Monkeys were placed in a dynamic foraging environment in which they had to track the changing values of alternative choices through time. In this context, the monkeys' foraging behavior provided a window into their subjective valuation. We found that a simple model based on reward history can duplicate this behavior and that neurons in the parietal cortex represent the relative value of competing actions predicted by this model.