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.

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.

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.

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.

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.

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.

The BRAIN Initiative: developing technology to catalyse neuroscience discovery.

The evolution of the field of neuroscience has been propelled by the advent of novel technological capabilities, and the pace at which these capabilities are being developed has accelerated dramatically in the past decade. Capitalizing on this momentum, the United States launched the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative to develop and apply new tools and technologies for revolutionizing our understanding of the brain. In this article, we review the scientific vision for this initiative set forth by the National Institutes of Health and discuss its implications for the future of neuroscience research. Particular emphasis is given to its potential impact on the mapping and study of neural circuits, and how this knowledge will transform our understanding of the complexity of the human brain and its diverse array of behaviours, perceptions, thoughts and emotions.

Natural grouping of neural responses reveals spatially segregated clusters in prearcuate cortex.

A fundamental challenge in studying the frontal lobe is to parcellate this cortex into "natural" functional modules despite the absence of topographic maps, which are so helpful in primary sensory areas. Here we show that unsupervised clustering algorithms, applied to 96-channel array recordings from prearcuate gyrus, reveal spatially segregated subnetworks that remain stable across behavioral contexts. Looking for natural groupings of neurons based on response similarities, we discovered that the recorded area includes at least two spatially segregated subnetworks that differentially represent behavioral choice and reaction time. Importantly, these subnetworks are detectable during different behavioral states and, surprisingly, are defined better by "common noise" than task-evoked responses. Our parcellation process works well on "spontaneous" neural activity, and thus bears strong resemblance to the identification of "resting-state" networks in fMRI data sets. Our results demonstrate a powerful new tool for identifying cortical subnetworks by objective classification of simultaneously recorded electrophysiological activity.

Effects of cortical microstimulation on confidence in a perceptual decision.

Decisions are often associated with a degree of certainty, or confidence--an estimate of the probability that the chosen option will be correct. Recent neurophysiological results suggest that the central processing of evidence leading to a perceptual decision also establishes a level of confidence. Here we provide a causal test of this hypothesis by electrically stimulating areas of the visual cortex involved in motion perception. Monkeys discriminated the direction of motion in a noisy display and were sometimes allowed to opt out of the direction choice if their confidence was low. Microstimulation did not reduce overall confidence in the decision but instead altered confidence in a manner that mimicked a change in visual motion, plus a small increase in sensory noise. The results suggest that the same sensory neural signals support choice, reaction time, and confidence in a decision and that artificial manipulation of these signals preserves the quantitative relationship between accumulated evidence and confidence.

Dynamics of neural population responses in prefrontal cortex indicate changes of mind on single trials.

Decision making is a complex process in which different sources of information are combined into a decision variable (DV) that guides action [1, 2]. Neurophysiological studies have typically sought insight into the dynamics of the decision-making process and its neural mechanisms through statistical analysis of large numbers of trials from sequentially recorded single neurons or small groups of neurons [3-6]. However, detecting and analyzing the DV on individual trials has been challenging [7]. Here we show that by recording simultaneously from hundreds of units in prearcuate gyrus of macaque monkeys performing a direction discrimination task, we can predict the monkey's choices with high accuracy and decode DV dynamically as the decision unfolds on individual trials. This advance enabled us to study changes of mind (CoMs) that occasionally happen before the final commitment to a decision [8-10]. On individual trials, the decoded DV varied significantly over time and occasionally changed its sign, identifying a potential CoM. Interrogating the system by random stopping of the decision-making process during the delay period after stimulus presentation confirmed the validity of identified CoMs. Importantly, the properties of the candidate CoMs also conformed to expectations based on prior theoretical and behavioral studies [8]: they were more likely to go from an incorrect to a correct choice, they were more likely for weak and intermediate stimuli than for strong stimuli, and they were more likely earlier in the trial. We suggest that simultaneous recording of large neural populations provides a good estimate of DV and explains idiosyncratic aspects of the decision-making process that were inaccessible before.

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.

Comment on "In monkeys making value-based decisions, LIP neurons encode cue salience and not action value".

Leathers and Olson (Reports, 5 October 2012, p. 132) draw the strong conclusion that neurons in the monkey lateral intraparietal (LIP) cortical area encode only cue salience, and not action value, during value-based decision-making. Although their findings regarding cue salience are interesting, their broader conclusions are problematic because (i) their primary conclusion is based on responses observed during a brief interval at the beginning of behavioral trials but is extended to all subsequent temporal epochs and (ii) the authors failed to replicate basic hallmarks of LIP physiology observed in those subsequent temporal epochs by many laboratories.

Effective parameters for ultrasound-induced in vivo neurostimulation.

Ultrasound-induced neurostimulation has recently gained increasing attention, but little is known about the mechanisms by which it affects neural activity or about the range of acoustic parameters and stimulation protocols that elicit responses. We have established conditions for transcranial stimulation of the nervous system in vivo, using the mouse somatomotor response. We report that (1) continuous-wave stimuli are as effective as or more effective than pulsed stimuli in eliciting responses, and responses are elicited with stimulus onset rather than stimulus offset; (2) stimulation success increases as a function of both acoustic intensity and acoustic duration; (3) interactions of intensity and duration suggest that successful stimulation results from the integration of stimulus amplitude over a time interval of 50 to 150 ms; and (4) the motor response elicited appears to be an all-or-nothing phenomenon, meaning stronger stimulus intensities and durations increase the probability of a motor response without affecting the duration or strength of the response.

Tracking the eye non-invasively: simultaneous comparison of the scleral search coil and optical tracking techniques in the macaque monkey.

From human perception to primate neurophysiology, monitoring eye position is critical to the study of vision, attention, oculomotor control, and behavior. Two principal techniques for the precise measurement of eye position-the long-standing sclera-embedded search coil and more recent optical tracking techniques-are in use in various laboratories, but no published study compares the performance of the two methods simultaneously in the same primates. Here we compare two popular systems-a sclera-embedded search coil from C-N-C Engineering and the EyeLink 1000 optical system from SR Research-by recording simultaneously from the same eye in the macaque monkey while the animal performed a simple oculomotor task. We found broad agreement between the two systems, particularly in positional accuracy during fixation, measurement of saccade amplitude, detection of fixational saccades, and sensitivity to subtle changes in eye position from trial to trial. Nonetheless, certain discrepancies persist, particularly elevated saccade peak velocities, post-saccadic ringing, influence of luminance change on reported position, and greater sample-to-sample variation in the optical system. Our study shows that optical performance now rivals that of the search coil, rendering optical systems appropriate for many if not most applications. This finding is consequential, especially for animal subjects, because the optical systems do not require invasive surgery for implantation and repair of search coils around the eye. Our data also allow laboratories using the optical system in human subjects to assess the strengths and limitations of the technique for their own applications.

Dissociation of neuronal and psychophysical responses to local and global motion.

Most neurons in cortical area MT (V5) are strongly direction selective, and their activity is closely associated with the perception of visual motion. These neurons have large receptive fields built by combining inputs with smaller receptive fields that respond to local motion. Humans integrate motion over large areas and can perceive what has been referred to as global motion. The large size and direction selectivity of MT receptive fields suggests that MT neurons may represent global motion. We have explored this possibility by measuring responses to a stimulus in which the directions of simultaneously presented local and global motion are independently controlled. Surprisingly, MT responses depended only on the local motion and were unaffected by the global motion. Yet, under similar conditions, human observers perceive global motion and are impaired in discriminating local motion. Although local motion perception might depend on MT signals, global motion perception depends on mechanisms qualitatively different from those in MT. Motion perception therefore does not depend on a single cortical area but reflects the action and interaction of multiple brain systems.