Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes.

Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and use that to plan and anticipate the consequences of actions. However, the neural mechanisms underlying these computations are unclear. We combine a goal-driven modeling approach with dense neurophysiological data and high-throughput human behavioral readouts that contain thousands of comparisons to directly impinge on this question. Specifically, we construct and evaluate several classes of sensory-cognitive networks to predict the future state of rich, ethologically-relevant environments, ranging from self-supervised end-to-end models with pixel-wise or object-slot objectives, to models that future predict in the latent space of purely static image-pretrained or dynamic video-pretrained foundation models. We find that "scale is not all you need", and that many state-of-the-art machine learning models fail to perform well on our neural and behavioral benchmarks for future prediction. In fact, only one class of models matches these data well overall. We find that neural responses are currently best predicted by models trained to predict the future state of their environment in the latent space of pretrained foundation models optimized for dynamic scenes in a self-supervised manner. These models also approach the neurons' ability to predict the environmental state variables that are visually hidden from view, despite not being explicitly trained to do so. Finally, we find that not all foundation model latents are equal. Notably, models that future predict in the latent space of video foundation models that are optimized to support a diverse range of egocentric sensorimotor tasks, reasonably match both human behavioral error patterns and neural dynamics across all environmental scenarios that we were able to test. Overall, these findings suggest that the neural mechanisms and behaviors of primate mental simulation have strong inductive biases associated with them, and are thus far most consistent with being optimized to future predict on reusable visual representations that are useful for Embodied AI more generally.

Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task.

Primates can richly parse sensory inputs to infer latent information. This ability is hypothesized to rely on establishing mental models of the external world and running mental simulations of those models. However, evidence supporting this hypothesis is limited to behavioral models that do not emulate neural computations. Here, we test this hypothesis by directly comparing the behavior of primates (humans and monkeys) in a ball interception task to that of a large set of recurrent neural network (RNN) models with or without the capacity to dynamically track the underlying latent variables. Humans and monkeys exhibit similar behavioral patterns. This primate behavioral pattern is best captured by RNNs endowed with dynamic inference, consistent with the hypothesis that the primate brain uses dynamic inferences to support flexible physical predictions. Moreover, our work highlights a general strategy for using model neural systems to test computational hypotheses of higher brain function.

Chronically implantable LED arrays for behavioral optogenetics in primates.

Optogenetic methods have been widely used in rodent brains, but remain relatively under-developed for nonhuman primates such as rhesus macaques, an animal model with a large brain expressing sophisticated sensory, motor and cognitive behaviors. To address challenges in behavioral optogenetics in large brains, we developed Opto-Array, a chronically implantable array of light-emitting diodes for high-throughput optogenetic perturbation. We demonstrated that optogenetic silencing in the macaque primary visual cortex with the help of the Opto-Array results in reliable retinotopic visual deficits in a luminance discrimination task. We separately confirmed that Opto-Array illumination results in local neural silencing, and that behavioral effects are not due to tissue heating. These results demonstrate the effectiveness of the Opto-Array for behavioral optogenetic applications in large brains.

A Network Perspective on Sensorimotor Learning.

What happens in the brain when we learn? Ever since the foundational work of Cajal, the field has made numerous discoveries as to how experience could change the structure and function of individual synapses. However, more recent advances have highlighted the need for understanding learning in terms of complex interactions between populations of neurons and synapses. How should one think about learning at such a macroscopic level? Here, we develop a conceptual framework to bridge the gap between the different scales at which learning operates, from synapses to neurons to behavior. Using this framework, we explore the principles that guide sensorimotor learning across these scales, and set the stage for future experimental and theoretical work in the field.

The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys.

The ability to recognize written letter strings is foundational to human reading, but the underlying neuronal mechanisms remain largely unknown. Recent behavioral research in baboons suggests that non-human primates may provide an opportunity to investigate this question. We recorded the activity of hundreds of neurons in V4 and the inferior temporal cortex (IT) while naïve macaque monkeys passively viewed images of letters, English words and non-word strings, and tested the capacity of those neuronal representations to support a battery of orthographic processing tasks. We found that simple linear read-outs of IT (but not V4) population responses achieved high performance on all tested tasks, even matching the performance and error patterns of baboons on word classification. These results show that the IT cortex of untrained primates can serve as a precursor of orthographic processing, suggesting that the acquisition of reading in humans relies on the recycling of a brain network evolved for other visual functions.

Reversible Inactivation of Different Millimeter-Scale Regions of Primate IT Results in Different Patterns of Core Object Recognition Deficits.

Extensive research suggests that the inferior temporal (IT) population supports visual object recognition behavior. However, causal evidence for this hypothesis has been equivocal, particularly beyond the specific case of face-selective subregions of IT. Here, we directly tested this hypothesis by pharmacologically inactivating individual, millimeter-scale subregions of IT while monkeys performed several core object recognition subtasks, interleaved trial-by trial. First, we observed that IT inactivation resulted in reliable contralateral-biased subtask-selective behavioral deficits. Moreover, inactivating different IT subregions resulted in different patterns of subtask deficits, predicted by each subregion's neuronal object discriminability. Finally, the similarity between different inactivation effects was tightly related to the anatomical distance between corresponding inactivation sites. Taken together, these results provide direct evidence that the IT cortex causally supports general core object recognition and that the underlying IT coding dimensions are topographically organized.

Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks.

Primates, including humans, can typically recognize objects in visual images at a glance despite naturally occurring identity-preserving image transformations (e.g., changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected more than one million behavioral trials from 1472 anonymous humans and five male macaque monkeys for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feedforward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly nonpredictive of primate performance and that this prediction failure was not accounted for by simple image attributes nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks such as those obtained here could serve as direct guides for discovering such models.SIGNIFICANCE STATEMENT Recently, specific feedforward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.

Characterization of neurons in the primate medial intraparietal area reveals a joint representation of intended reach direction and amplitude.

To support accurate memory-guided reaching, the brain must represent both the direction and amplitude of reaches in a movement plan. Several cortical areas have been shown to represent the direction of a planned reaching movement, but the neuronal representation of reach amplitude is still unclear, especially in sensory-motor integration areas. To investigate this, we recorded from neurons in the medial intraparietal area (MIP) of monkeys performing a variable amplitude memory reach task. In one monkey, we additionally recorded from the dorsal premotor cortex (PMd) for direct cross-area comparisons. In both areas, we found modest but significant proportions of neurons with movement-planning activity sensitive to reach amplitude. However, reach amplitude was under-represented relative to direction in the neuronal population, with approximately one third as many selective neurons. We observed an interaction between neuronal selectivity for amplitude and direction; neurons in both areas exhibited significant modulation of neuronal activity by reach amplitude in some but not all directions. Consistent with an encoding of reach goals as a position in visual space, the response patterns of MIP/PMd neurons were best predicted by 2D Gaussian position encoding model, in contrast to a number of alternative direction and amplitude tuning models. Taken together, these results suggest that amplitude and direction jointly modulate activity in MIP, as in PMd, to form representations of intended reach position.

Comparison of Object Recognition Behavior in Human and Monkey.

Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize "pooled human" object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT: To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to further the goal of the field of translating knowledge gained from animal models to humans. To the best of our knowledge, this study is the first systematic attempt at comparing a high-level visual behavior of humans and macaque monkeys.

Modulation of neural activity by reward in medial intraparietal cortex is sensitive to temporal sequence of reward.

To restore movements to paralyzed patients, neural prosthetic systems must accurately decode patients' intentions from neural signals. Despite significant advancements, current systems are unable to restore complex movements. Decoding reward-related signals from the medial intraparietal area (MIP) could enhance prosthetic performance. However, the dynamics of reward sensitivity in MIP is not known. Furthermore, reward-related modulation in premotor areas has been attributed to behavioral confounds. Here we investigated the stability of reward encoding in MIP by assessing the effect of reward history on reward sensitivity. We recorded from neurons in MIP while monkeys performed a delayed-reach task under two reward schedules. In the variable schedule, an equal number of small- and large-rewards trials were randomly interleaved. In the constant schedule, one reward size was delivered for a block of trials. The memory period firing rate of most neurons in response to identical rewards varied according to schedule. Using systems identification tools, we attributed the schedule sensitivity to the dependence of neural activity on the history of reward. We did not find schedule-dependent behavioral changes, suggesting that reward modulates neural activity in MIP. Neural discrimination between rewards was less in the variable than in the constant schedule, degrading our ability to decode reach target and reward simultaneously. The effect of schedule was mitigated by adding Haar wavelet coefficients to the decoding model. This raises the possibility of multiple encoding schemes at different timescales and reinforces the potential utility of reward information for prosthetic performance.