Abstract representations emerge in human hippocampal neurons during inference behavior.

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization 1 . However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior 2,3 . Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.

The representational geometry of emotional states in basolateral amygdala.

Sensory stimuli associated with aversive outcomes can cause multiple behavioral responses related to an animal's evolving emotional state. We employed chemogenetic inactivation and two-photon imaging to reveal how the basolateral amygdala (BLA) mediates these state changes. Mice were presented stimuli in a virtual burrow, causing two responses reflecting fear and flight to safety: tremble and ingress into the burrow. Inactivation eliminated differential tremble and ingress to aversive and neutral stimuli without eliminating responses themselves. Multiple variables, including stimulus valence and identity, and being in the tremble or ingressed state, typically modulated each neuron's activity (mixed-selectivity). BLA neural ensembles represented these variables even after neurons with apparent specialized selectivity were eliminated from analyses. Thus, implementing different readouts of BLA ensembles comprised of mixed-selectivity neurons can identify distinct emotional states defined by responses, like tremble for fear and ingress for safety. This mechanism relies on BLA's representational geometry, not its circuit specialization.

Visuospatial information foraging describes search behavior in learning latent environmental features.

In the real world, making sequences of decisions to achieve goals often depends upon the ability to learn aspects of the environment that are not directly perceptible. Learning these so-called latent features requires seeking information about them. Prior efforts to study latent feature learning often used single decisions, used few features, and failed to distinguish between reward-seeking and information-seeking. To overcome this, we designed a task in which humans and monkeys made a series of choices to search for shapes hidden on a grid. On our task, the effects of reward and information outcomes from uncovering parts of shapes could be disentangled. Members of both species adeptly learned the shapes and preferred to select tiles expected to be informative earlier in trials than previously rewarding ones, searching a part of the grid until their outcomes dropped below the average information outcome-a pattern consistent with foraging behavior. In addition, how quickly humans learned the shapes was predicted by how well their choice sequences matched the foraging pattern, revealing an unexpected connection between foraging and learning. This adaptive search for information may underlie the ability in humans and monkeys to learn latent features to support goal-directed behavior in the long run.

Prelimbic cortex drives discrimination of non-aversion via amygdala somatostatin interneurons.

The amygdala and prelimbic cortex (PL) communicate during fear discrimination retrieval, but how they coordinate discrimination of a non-threatening stimulus is unknown. Here, we show that somatostatin (SOM) interneurons in the basolateral amygdala (BLA) become active specifically during learned non-threatening cues and desynchronize cell firing by blocking phase reset of theta oscillations during the safe cue. Furthermore, we show that SOM activation and desynchronization of the BLA is PL-dependent and promotes discrimination of non-threat. Thus, fear discrimination engages PL-dependent coordination of BLA SOM responses to non-threatening stimuli.

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.

The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex.

The curse of dimensionality plagues models of reinforcement learning and decision making. The process of abstraction solves this by constructing variables describing features shared by different instances, reducing dimensionality and enabling generalization in novel situations. Here, we characterized neural representations in monkeys performing a task described by different hidden and explicit variables. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training, which requires a particular geometry of neural representations. Neural ensembles in prefrontal cortex, hippocampus, and simulated neural networks simultaneously represented multiple variables in a geometry reflecting abstraction but that still allowed a linear classifier to decode a large number of other variables (high shattering dimensionality). Furthermore, this geometry changed in relation to task events and performance. These findings elucidate how the brain and artificial systems represent variables in an abstract format while preserving the advantages conferred by high shattering dimensionality.

Low-dimensional dynamics for working memory and time encoding.

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear "ramping" component of each neuron's firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

The contribution of nonhuman primate research to the understanding of emotion and cognition and its clinical relevance.

Psychiatric disorders are often conceptualized as arising from dysfunctional interactions between neural systems mediating cognitive and emotional processes. Mechanistic insights into these interactions have been lacking in part because most work in emotions has occurred in rodents, often without concurrent manipulations of cognitive variables. Nonhuman primate (NHP) model systems provide a powerful platform for investigating interactions between cognitive operations and emotions due to NHPs' strong homology with humans in behavioral repertoire and brain anatomy. Recent electrophysiological studies in NHPs have delineated how neural signals in the amygdala, a brain structure linked to emotion, predict impending appetitive and aversive stimuli. In addition, abstract conceptual information has also been shown to be represented in the amygdala and in interconnected brain structures such as the hippocampus and prefrontal cortex. Flexible adjustments of emotional behavior require the ability to apply conceptual knowledge and generalize to different, often novel, situations, a hallmark example of interactions between cognitive and emotional processes. Elucidating the neural mechanisms that explain how the brain processes conceptual information in relation to emotional variables promises to provide important insights into the pathophysiology accounting for symptoms in neuropsychiatric disorders.

The coding of valence and identity in the mammalian taste system.

The ability of the taste system to identify a tastant (what it tastes like) enables animals to recognize and discriminate between the different basic taste qualities1,2. The valence of a tastant (whether it is appetitive or aversive) specifies its hedonic value and elicits the execution of selective behaviours. Here we examine how sweet and bitter are afforded valence versus identity in mice. We show that neurons in the sweet-responsive and bitter-responsive cortex project to topographically distinct areas of the amygdala, with strong segregation of neural projections conveying appetitive versus aversive taste signals. By manipulating selective taste inputs to the amygdala, we show that it is possible to impose positive or negative valence on a neutral water stimulus, and even to reverse the hedonic value of a sweet or bitter tastant. Remarkably, mice with silenced neurons in the amygdala no longer exhibit behaviour that reflects the valence associated with direct stimulation of the taste cortex, or with delivery of sweet and bitter chemicals. Nonetheless, these mice can still identify and discriminate between tastants, just as wild-type controls do. These results help to explain how the taste system generates stereotypic and predetermined attractive and aversive taste behaviours, and support the existence of distinct neural substrates for the discrimination of taste identity and the assignment of valence.

Basolateral amygdala circuitry in positive and negative valence.

All organisms must solve the same fundamental problem: they must acquire rewards and avoid danger in order to survive. A key challenge for the nervous system is therefore to connect motivationally salient sensory stimuli to neural circuits that engage appropriate valence-specific behavioral responses. Anatomical, behavioral, and electrophysiological data have long suggested that the amygdala plays a central role in this process. Here we review experimental efforts leveraging recent technological advances to provide previously unattainable insights into the functional, anatomical, and genetic identity of neural populations within the amygdala that connect sensory stimuli to valence-specific behavioral responses.

Shared neural coding for social hierarchy and reward value in primate amygdala.

The social brain hypothesis posits that dedicated neural systems process social information. In support of this, neurophysiological data have shown that some brain regions are specialized for representing faces. It remains unknown, however, whether distinct anatomical substrates also represent more complex social variables, such as the hierarchical rank of individuals within a social group. Here we show that the primate amygdala encodes the hierarchical rank of individuals in the same neuronal ensembles that encode the rewards associated with nonsocial stimuli. By contrast, orbitofrontal and anterior cingulate cortices lack strong representations of hierarchical rank while still representing reward values. These results challenge the conventional view that dedicated neural systems process social information. Instead, information about hierarchical rank-which contributes to the assessment of the social value of individuals within a group-is linked in the amygdala to representations of rewards associated with nonsocial stimuli.

Distinct Roles for the Amygdala and Orbitofrontal Cortex in Representing the Relative Amount of Expected Reward.

The same reward can possess different motivational meaning depending upon its magnitude relative to other rewards. To study the neurophysiological mechanisms mediating assignment of motivational meaning, we recorded the activity of neurons in the amygdala and orbitofrontal cortex (OFC) of monkeys during a Pavlovian task in which the relative amount of liquid reward associated with one conditioned stimulus (CS) was manipulated by changing the reward amount associated with a second CS. Anticipatory licking tracked relative reward magnitude, implying that monkeys integrated information about recent rewards to adjust the motivational meaning of a CS. Upon changes in relative reward magnitude, neural responses to reward-predictive cues updated more rapidly in OFC than amygdala, and activity in OFC but not the amygdala was modulated by recent reward history. These results highlight a distinction between the amygdala and OFC in assessing reward history to support the flexible assignment of motivational meaning to sensory cues.

Reward expectation differentially modulates attentional behavior and activity in visual area V4.

Neural activity in visual area V4 is enhanced when attention is directed into neuronal receptive fields. However, the source of this enhancement is unclear, as most physiological studies have manipulated attention by changing the absolute reward associated with a particular location as well as its value relative to other locations. We trained monkeys to discriminate the orientation of two stimuli presented simultaneously in different hemifields while we independently varied the reward magnitude associated with correct discrimination at each location. Behavioral measures of attention were controlled by the relative value of each location. By contrast, neurons in V4 were consistently modulated by absolute reward value, exhibiting increased activity, increased gamma-band power and decreased trial-to-trial variability whenever receptive field locations were associated with large rewards. These data challenge the notion that the perceptual benefits of spatial attention rely on increased signal-to-noise in V4. Instead, these benefits likely derive from downstream selection mechanisms.

Understanding the brain by controlling neural activity.

Causal methods to interrogate brain function have been employed since the advent of modern neuroscience in the nineteenth century. Initially, randomly placed electrodes and stimulation of parts of the living brain were used to localize specific functions to these areas. Recent technical developments have rejuvenated this approach by providing more precise tools to dissect the neural circuits underlying behaviour, perception and cognition. Carefully controlled behavioural experiments have been combined with electrical devices, targeted genetically encoded tools and neurochemical approaches to manipulate information processing in the brain. The ability to control brain activity in these ways not only deepens our understanding of brain function but also provides new avenues for clinical intervention, particularly in conditions where brain processing has gone awry.

Manipulating neural activity in physiologically classified neurons: triumphs and challenges.

Understanding brain function requires knowing both how neural activity encodes information and how this activity generates appropriate responses. Electrophysiological, imaging and immediate early gene immunostaining studies have been instrumental in identifying and characterizing neurons that respond to different sensory stimuli, events and motor actions. Here we highlight approaches that have manipulated the activity of physiologically classified neurons to determine their role in the generation of behavioural responses. Previous experiments have often exploited the functional architecture observed in many cortical areas, where clusters of neurons share response properties. However, many brain structures do not exhibit such functional architecture. Instead, neurons with different response properties are anatomically intermingled. Emerging genetic approaches have enabled the identification and manipulation of neurons that respond to specific stimuli despite the lack of discernable anatomical organization. These approaches have advanced understanding of the circuits mediating sensory perception, learning and memory, and the generation of behavioural responses by providing causal evidence linking neural response properties to appropriate behavioural output. However, significant challenges remain for understanding cognitive processes that are probably mediated by neurons with more complex physiological response properties. Currently available strategies may prove inadequate for determining how activity in these neurons is causally related to cognitive behaviour.

Neural Representations of Unconditioned Stimuli in Basolateral Amygdala Mediate Innate and Learned Responses.

Stimuli that possess inherently rewarding or aversive qualities elicit emotional responses and also induce learning by imparting valence upon neutral sensory cues. Evidence has accumulated implicating the amygdala as a critical structure in mediating these processes. We have developed a genetic strategy to identify the representations of rewarding and aversive unconditioned stimuli (USs) in the basolateral amygdala (BLA) and have examined their role in innate and learned responses. Activation of an ensemble of US-responsive cells in the BLA elicits innate physiological and behavioral responses of different valence. Activation of this US ensemble can also reinforce appetitive and aversive learning when paired with differing neutral stimuli. Moreover, we establish that the activation of US-responsive cells in the BLA is necessary for the expression of a conditioned response. Neural representations of conditioned and unconditioned stimuli therefore ultimately connect to US-responsive cells in the BLA to elicit both innate and learned responses.

Task-dependent spatial selectivity in the primate amygdala.

Humans and other animals routinely encounter visual stimuli that indicate whether future reward delivery depends upon the identity or location of a stimulus, or the performance of a particular action. These reinforcement contingencies can influence how much attention is directed toward a stimulus. Neurons in the primate amygdala encode information about the association between visual stimuli and reinforcement as well as about the location of reward-predictive stimuli. Amygdala neural activity also predicts variability in spatial attention. In principle, the spatial properties of amygdala neurons may be present independent of spatial attention allocation. Alternatively, the encoding of spatial information may require attention. We trained monkeys to perform tasks that engaged spatial attention to varying degrees to understand the genesis of spatial processing in the amygdala. During classical conditioning tasks, conditioned stimuli appeared at different locations; amygdala neurons responded selectively to the location of stimuli. These spatial signals diminished rapidly upon stimulus disappearance and were unrelated to selectivity for expected reward. In contrast, spatial selectivity was sustained in time when monkeys performed a delayed saccade task that required sustained spatial attention. This temporally extended spatial signal was correlated with signals encoding reward expectation. Furthermore, variability in firing rates was correlated with variability in spatial attention, as measured by reaction time. These results reveal two types of spatial signals in the amygdala: one that is tied to initial visual responses and a second that reflects coordination between spatial and reinforcement information and that relates to the engagement of spatial attention.

Amygdala neural activity reflects spatial attention towards stimuli promising reward or threatening punishment.

Humans and other animals routinely identify and attend to sensory stimuli so as to rapidly acquire rewards or avoid aversive experiences. Emotional arousal, a process mediated by the amygdala, can enhance attention to stimuli in a non-spatial manner. However, amygdala neural activity was recently shown to encode spatial information about reward-predictive stimuli, and to correlate with spatial attention allocation. If representing the motivational significance of sensory stimuli within a spatial framework reflects a general principle of amygdala function, then spatially selective neural responses should also be elicited by sensory stimuli threatening aversive events. Recordings from amygdala neurons were therefore obtained while monkeys directed spatial attention towards stimuli promising reward or threatening punishment. Neural responses encoded spatial information similarly for stimuli associated with both valences of reinforcement, and responses reflected spatial attention allocation. The amygdala therefore may act to enhance spatial attention to sensory stimuli associated with rewarding or aversive experiences.

The amygdala and basal forebrain as a pathway for motivationally guided attention.

Visual stimuli associated with rewards attract spatial attention. Neurophysiological mechanisms that mediate this process must register both the motivational significance and location of visual stimuli. Recent neurophysiological evidence indicates that the amygdala encodes information about both of these parameters. Furthermore, the firing rate of amygdala neurons predicts the allocation of spatial attention. One neural pathway through which the amygdala might influence attention involves the intimate and bidirectional connections between the amygdala and basal forebrain (BF), a brain area long implicated in attention. Neurons in the rhesus monkey amygdala and BF were therefore recorded simultaneously while subjects performed a detection task in which the stimulus-reward associations of visual stimuli modulated spatial attention. Neurons in BF were spatially selective for reward-predictive stimuli, much like the amygdala. The onset of reward-predictive signals in each brain area suggested different routes of processing for reward-predictive stimuli appearing in the ipsilateral and contralateral fields. Moreover, neurons in the amygdala, but not BF, tracked trial-to-trial fluctuations in spatial attention. These results suggest that the amygdala and BF could play distinct yet inter-related roles in influencing attention elicited by reward-predictive stimuli.

The primate amygdala combines information about space and value.

A stimulus predicting reinforcement can trigger emotional responses, such as arousal, and cognitive ones, such as increased attention toward the stimulus. Neuroscientists have long appreciated that the amygdala mediates spatially nonspecific emotional responses, but it remains unclear whether the amygdala links motivational and spatial representations. To test whether amygdala neurons encode spatial and motivational information, we presented reward-predictive cues in different spatial configurations to monkeys and assessed how these cues influenced spatial attention. Cue configuration and predicted reward magnitude modulated amygdala neural activity in a coordinated fashion. Moreover, fluctuations in activity were correlated with trial-to-trial variability in spatial attention. Thus, the amygdala integrates spatial and motivational information, which may influence the spatial allocation of cognitive resources. These results suggest that amygdala dysfunction may contribute to deficits in cognitive processes normally coordinated with emotional responses, such as the directing of attention toward the location of emotionally relevant stimuli.