Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks.

Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca2+ activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

Decoding and perturbing decision states in real time.

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment1. The process of deliberation can be described by a time-varying decision variable (DV), decoded from neural population activity, that predicts a subject's upcoming decision2. Within single trials, however, there are large moment-to-moment fluctuations in the DV, the behavioural significance of which is unclear. Here, using real-time, neural feedback control of stimulus duration, we show that within-trial DV fluctuations, decoded from motor cortex, are tightly linked to decision state in macaques, predicting behavioural choices substantially better than the condition-averaged DV or the visual stimulus alone. Furthermore, robust changes in DV sign have the statistical regularities expected from behavioural studies of changes of mind3. Probing the decision process on single trials with weak stimulus pulses, we find evidence for time-varying absorbing decision bounds, enabling us to distinguish between specific models of decision making.

Opportunities and limitations of genetically modified nonhuman primate models for neuroscience research.

The recently developed new genome-editing technologies, such as the CRISPR/Cas system, have opened the door for generating genetically modified nonhuman primate (NHP) models for basic neuroscience and brain disorders research. The complex circuit formation and experience-dependent refinement of the human brain are very difficult to model in vitro, and thus require use of in vivo whole-animal models. For many neurodevelopmental and psychiatric disorders, abnormal circuit formation and refinement might be at the center of their pathophysiology. Importantly, many of the critical circuits and regional cell populations implicated in higher human cognitive function and in many psychiatric disorders are not present in lower mammalian brains, while these analogous areas are replicated in NHP brains. Indeed, neuropsychiatric disorders represent a tremendous health and economic burden globally. The emerging field of genetically modified NHP models has the potential to transform our study of higher brain function and dramatically facilitate the development of effective treatment for human brain disorders. In this paper, we discuss the importance of developing such models, the infrastructure and training needed to maximize the impact of such models, and ethical standards required for using these models.

Context-dependent computation by recurrent dynamics in prefrontal cortex.

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.

Estimates of the contribution of single neurons to perception depend on timescale and noise correlation.

The sensitivity of a population of neurons, and therefore the amount of sensory information available to an animal, is limited by the sensitivity of single neurons in the population and by noise correlation between neurons. For decades, therefore, neurophysiologists have devised increasingly clever and rigorous ways to measure these critical variables (Parker and Newsome, 1998). Previous studies examining the relationship between the responses of single middle temporal (MT) neurons and direction-discrimination performance uncovered an apparent paradox. Sensitivity measurements from single neurons suggested that small numbers of neurons may account for a monkey's psychophysical performance (Britten et al., 1992), but trial-to-trial variability in activity of single MT neurons are only weakly correlated with the monkey's behavior, suggesting that the monkey's decision must be based on the responses of many neurons (Britten et al., 1996). We suggest that the resolution to this paradox lies (1) in the long stimulus duration used in the original studies, which led to an overestimate of neural sensitivity relative to psychophysical sensitivity, and (2) mistaken assumptions (because no data were available) about the level of noise correlation in MT columns with opposite preferred directions. We therefore made new physiological and psychophysical measurements in a reaction time version of the direction-discrimination task that matches neural measurements to the actual decision time of the animals. These new data, considered together with our recent data on noise correlation in MT (Cohen and Newsome, 2008), provide a substantially improved account of psychometric performance in the direction-discrimination task.

Brain stimulation: feeling the buzz.

A recent study demonstrates that artificially generated patterns of brain activity are surprisingly easy to sense. Brain areas that differ substantially in their functional specialization are remarkably similar in their ability to support this awareness.

Can monkeys choose optimally when faced with noisy stimuli and unequal rewards?

We review the leaky competing accumulator model for two-alternative forced-choice decisions with cued responses, and propose extensions to account for the influence of unequal rewards. Assuming that stimulus information is integrated until the cue to respond arrives and that firing rates of stimulus-selective neurons remain well within physiological bounds, the model reduces to an Ornstein-Uhlenbeck (OU) process that yields explicit expressions for the psychometric function that describes accuracy. From these we compute strategies that optimize the rewards expected over blocks of trials administered with mixed difficulty and reward contingencies. The psychometric function is characterized by two parameters: its midpoint slope, which quantifies a subject's ability to extract signal from noise, and its shift, which measures the bias applied to account for unequal rewards. We fit these to data from two monkeys performing the moving dots task with mixed coherences and reward schedules. We find that their behaviors averaged over multiple sessions are close to optimal, with shifts erring in the direction of smaller penalties. We propose two methods for biasing the OU process to produce such shifts.

Value and choice as separable and stable representations in orbitofrontal cortex.

Value-based decision-making requires different variables-including offer value, choice, expected outcome, and recent history-at different times in the decision process. Orbitofrontal cortex (OFC) is implicated in value-based decision-making, but it is unclear how downstream circuits read out complex OFC responses into separate representations of the relevant variables to support distinct functions at specific times. We recorded from single OFC neurons while macaque monkeys made cost-benefit decisions. Using a novel analysis, we find separable neural dimensions that selectively represent the value, choice, and expected reward of the present and previous offers. The representations are generally stable during periods of behavioral relevance, then transition abruptly at key task events and between trials. Applying new statistical methods, we show that the sensitivity, specificity and stability of the representations are greater than expected from the population's low-level features-dimensionality and temporal smoothness-alone. The separability and stability suggest a mechanism-linear summation over static synaptic weights-by which downstream circuits can select for specific variables at specific times.

Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales.

Behavior deviating from our normative expectations often appears irrational. For example, even though behavior following the so-called matching law can maximize reward in a stationary foraging task, actual behavior commonly deviates from matching. Such behavioral deviations are interpreted as a failure of the subject; however, here we instead suggest that they reflect an adaptive strategy, suitable for uncertain, non-stationary environments. To prove it, we analyzed the behavior of primates that perform a dynamic foraging task. In such nonstationary environment, learning on both fast and slow timescales is beneficial: fast learning allows the animal to react to sudden changes, at the price of large fluctuations (variance) in the estimates of task relevant variables. Slow learning reduces the fluctuations but costs a bias that causes systematic behavioral deviations. Our behavioral analysis shows that the animals solved this bias-variance tradeoff by combining learning on both fast and slow timescales, suggesting that learning on multiple timescales can be a biologically plausible mechanism for optimizing decisions under uncertainty.

Task representations in neural networks trained to perform many cognitive tasks.

The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cognitive tasks that depend on working memory, decision making, categorization, and inhibitory control. We found that after training, recurrent units can develop into clusters that are functionally specialized for different cognitive processes, and we introduce a simple yet effective measure to quantify relationships between single-unit neural representations of tasks. Learning often gives rise to compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for other tasks. Finally, networks developed mixed task selectivity similar to recorded prefrontal neurons after learning multiple tasks sequentially with a continual-learning technique. This work provides a computational platform to investigate neural representations of many cognitive tasks.

Local field potential in cortical area MT: stimulus tuning and behavioral correlations.

Low-frequency electrical signals like those that compose the local field potential (LFP) can be detected at substantial distances from their point of origin within the brain. It is thus unclear how useful the LFP might be for assessing local function, for example, on the spatial scale of cortical columns. We addressed this problem by comparing speed and direction tuning of LFPs obtained from middle temporal area MT with the tuning of multiunit (MU) activity recorded simultaneously. We found that the LFP can be well tuned for speed and direction and is highly correlated with that of MU activity, particularly for frequencies at and above the gamma band. LFP tuning is substantially poorer for lower frequencies, although tuning for direction extends to lower frequencies than does tuning for speed. Our data suggest that LFP signals at and above the gamma band reflect neural processing on the spatial scale of cortical columns, within a few hundred micrometers of the electrode tip. Consistent with this notion, we also found that frequencies at and above the gamma band measured during a speed discrimination task exhibit an effect known as "choice probability," which reveals a particularly close relationship between neural activity and behavioral choices. In the LFP, this signature of the perceptual choice comprises a shift in relative power from low-frequency bands (alpha and beta) to the gamma band. It remains to be determined how LFP choice probability, which is a temporal signature, is related to conventional choice probability effects observed in spike rates.

Perceptual "read-out" of conjoined direction and disparity maps in extrastriate area MT.

Cortical neurons are frequently tuned to several stimulus dimensions, and many cortical areas contain intercalated maps of multiple variables. Relatively little is known about how information is "read out" of these multidimensional maps. For example, how does an organism extract information relevant to the task at hand from neurons that are also tuned to other, irrelevant stimulus dimensions? We addressed this question by employing microstimulation techniques to examine the contribution of disparity-tuned neurons in the middle temporal (MT) visual area to performance on a direction discrimination task. Most MT neurons are tuned to both binocular disparity and the direction of stimulus motion, and MT contains topographic maps of both parameters. We assessed the effect of microstimulation on direction judgments after first characterizing the disparity tuning of each stimulation site. Although the disparity of the stimulus was irrelevant to the required task, we found that microstimulation effects were strongly modulated by the disparity tuning of the stimulated neurons. For two of three monkeys, microstimulation of nondisparity-selective sites produced large biases in direction judgments, whereas stimulation of disparity-selective sites had little or no effect. The binocular disparity was optimized for each stimulation site, and our result could not be explained by variations in direction tuning, response strength, or any other tuning property that we examined. When microstimulation of a disparity-tuned site did affect direction judgments, the effects tended to be stronger at the preferred disparity of a stimulation site than at the nonpreferred disparity, indicating that monkeys can selectively monitor direction columns that are best tuned to an appropriate conjunction of parameters. We conclude that the contribution of neurons to behavior can depend strongly upon tuning to stimulus dimensions that appear to be irrelevant to the current task, and we suggest that these findings are best explained in terms of the strategy used by animals to perform the task.

Laminar differences in decision-related neural activity in dorsal premotor cortex.

Dorsal premotor cortex is implicated in somatomotor decisions. However, we do not understand the temporal patterns and laminar organization of decision-related firing rates in dorsal premotor cortex. We recorded neurons from dorsal premotor cortex of monkeys performing a visual discrimination task with reaches as the behavioral report. We show that these neurons can be organized along a bidirectional visuomotor continuum based on task-related firing rates. "Increased" neurons at one end of the continuum increased their firing rates ~150 ms after stimulus onset and these firing rates covaried systematically with choice, stimulus difficulty, and reaction time-characteristics of a candidate decision variable. "Decreased" neurons at the other end of the continuum reduced their firing rate after stimulus onset, while "perimovement" neurons at the center of the continuum responded only ~150 ms before movement initiation. These neurons did not show decision variable-like characteristics. "Increased" neurons were more prevalent in superficial layers of dorsal premotor cortex; deeper layers contained more "decreased" and "perimovement" neurons. These results suggest a laminar organization for decision-related responses in dorsal premotor cortex.Dorsal premotor cortex (PMd) is thought to be involved in making somatomotor decisions. Chandrasekaran et al. investigated the temporal response dynamics of PMd neurons across cortical layers and show stronger and earlier decision-related responses in the superficial layers and more action execution-related signals in the deeper layers.

Correlation between speed perception and neural activity in the middle temporal visual area.

We conducted electrophysiological recording and microstimulation experiments to test the hypothesis that the middle temporal visual area (MT) plays a direct role in perception of the speed of moving visual stimuli. We trained rhesus monkeys on a speed discrimination task in which monkeys chose the faster speed of two moving random dot patterns presented simultaneously in spatially segregated apertures. In electrophysiological experiments, we analyzed the activity of speed-tuned MT neurons and multiunit clusters during the discrimination task. Neural activity was correlated with the monkeys' behavioral choices on a trial-to-trial basis (choice probability), and the correlation was predicted by the speed-tuning properties of each unit. In microstimulation experiments, we activated clusters of MT neurons with homogeneous speed-tuning properties during the same speed discrimination task. In one monkey, microstimulation biased speed judgments toward the preferred speed of the stimulated neurons. Together, evidence from these two experiments suggests that MT neurons play a direct role in the perception of visual speed. Comparison of psychometric and neurometric thresholds revealed that single and multineuronal signals were, on average, considerably less sensitive than were the monkeys perceptually, suggesting that signals must be pooled across neurons to account for performance.

What electrical microstimulation has revealed about the neural basis of cognition.

Neurophysiologists have shown repeatedly that neural activity in different brain structures can be correlated with specific perceptual and cognitive functions, but the causal efficacy of the observed activity has generally been a matter of conjecture. By contrast, electrical microstimulation, which allows the experimenter to manipulate the activity of small groups of neurons with spatial and temporal precision, can now be used to demonstrate causal links between neural activity and specific cognitive functions. Here, we review this growing literature, including applications to the study of attention, visual and somatosensory perception, 'read-out' mechanisms for interpreting sensory maps, and contextual effects on perception. We also discuss potential applications of microstimulation to studies of higher cognitive functions such as decision-making and subjective experience.

A general mechanism for decision-making in the human brain?

A new fMRI study by Heekeren and colleagues suggests that left dorsolateral prefrontal cortex (DLPFC) contains a region that integrates sensory evidence supporting perceptual decisions. DLPFC meets two criteria posited by Heekeren et al. for such a region: (1) its activity is correlated in time with the output of sensory areas of the visual cortex measured simultaneously, and (2) as expected of an integrator, its activity is greater on trials for which the sensory evidence is substantial than on trials for which the sensory evidence is weak. Complementary experiments in humans and monkeys now offer a realistic hope of elucidating decision-making networks in the primate brain.

Orbitofrontal Cortex Value Signals Depend on Fixation Location during Free Viewing.

In the natural world, monkeys and humans judge the economic value of numerous competing stimuli by moving their gaze from one object to another, in a rapid series of eye movements. This suggests that the primate brain processes value serially, and that value-coding neurons may be modulated by changes in gaze. To test this hypothesis, we presented monkeys with value-associated visual cues and took the unusual step of allowing unrestricted free viewing while we recorded neurons in the orbitofrontal cortex (OFC). By leveraging natural gaze patterns, we found that a large proportion of OFC cells encode gaze location and, that in some cells, value coding is amplified when subjects fixate near the cue. These findings provide the first cellular-level mechanism for previously documented behavioral effects of gaze on valuation and suggest a major role for gaze in neural mechanisms of valuation and decision-making under ecologically realistic conditions.

Comment on "Single-trial spike trains in parietal cortex reveal discrete steps during decision-making".

Latimeret al (Reports, 10 July 2015, p. 184) claim that during perceptual decision formation, parietal neurons undergo one-time, discrete steps in firing rate instead of gradual changes that represent the accumulation of evidence. However, that conclusion rests on unsubstantiated assumptions about the time window of evidence accumulation, and their stepping model cannot explain existing data as effectively as evidence-accumulation models.

Middle temporal visual area microstimulation influences veridical judgments of motion direction.

Microstimulation of direction columns in the middle temporal visual area (MT, or V5) provides a powerful tool for probing the relationship between cortical physiology and visual motion perception. In the current study we obtained "veridical" reports of perceived motion from rhesus monkeys by permitting a continuous range of possible responses that mapped isomorphically onto a continuous range of possible motion directions. In contrast to previous studies, therefore, the animals were freed from experimenter-imposed "categories" that typify forced choice tasks. We report three new findings: (1) MT neurons with widely disparate preferred directions can cooperate to shape direction estimates, inconsistent with a pure "winner-take-all" read-out algorithm and consistent with a distributed coding scheme like vector averaging, whereas neurons with nearly opposite preferred directions seem to compete in a manner consistent with the winner-take-all hypothesis, (2) microstimulation can influence direction estimates even when paired with the most powerful motion stimuli available, and (3) microstimulation effects can be elicited when a manual response (instead of our standard oculomotor response) is used to communicate the perceptual report.

Linear-Nonlinear-Poisson models of primate choice dynamics.

The equilibrium phenomenon of matching behavior traditionally has been studied in stationary environments. Here we attempt to uncover the local mechanism of choice that gives rise to matching by studying behavior in a highly dynamic foraging environment. In our experiments, 2 rhesus monkeys (Macacca mulatta) foraged for juice rewards by making eye movements to one of two colored icons presented on a computer monitor, each rewarded on dynamic variable-interval schedules. Using a generalization of Wiener kernel analysis, we recover a compact mechanistic description of the impact of past reward on future choice in the form of a Linear-Nonlinear-Poisson model. We validate this model through rigorous predictive and generative testing. Compared to our earlier work with this same data set, this model proves to be a better description of choice behavior and is more tightly correlated with putative neural value signals. Refinements over previous models include hyperbolic (as opposed to exponential) temporal discounting of past rewards, and differential (as opposed to fractional) comparisons of option value. Through numerical simulation we find that within this class of strategies, the model parameters employed by animals are very close to those that maximize reward harvesting efficiency.