Expectations or prior beliefs about the world modulate sensory processing at the behavioral and neural levels. Bayesian models predict that such priors compensate for input uncertainty to optimize sensory judgments. Although Bayesian behavior is prevalent across sensorimotor systems, the relationship between priors and Bayesian inference is not obligatory. Priors may simply shift one's internal decision boundaries without interacting with sensory uncertainty at all. We recently showed that humans and monkeys use both Bayesian and non-Bayesian strategies when reporting judgments of visual stability across saccades, despite using priors in both cases. While they increased prior use to compensate for internal, movement-driven sensory uncertainty in a Bayesian manner, they decreased prior use when faced with external, visual image uncertainty. The latter, "anti-Bayesian" pattern was best explained by a model in which category boundaries were adjusted by the prior but susceptible to image noise. Here, in two male rhesus macaques, we recorded neural activity in the frontal eye field (FEF), a prefrontal region important for visuosaccadic behavior. We toggled between subjects' prior use for Bayesian and anti-Bayesian behavior via trial-by-trial manipulation of the two uncertainty conditions. First, we found that FEF activity signaled the priors in both conditions. The prior-related modulation of activity, however, predicted only the anti-Bayesian, categorization behavior. The results suggest that neural activity in the FEF reflects the use of a flexible decision boundary for the perception of visual stability and, more generally, that neural mechanisms for Bayesian inference and visual categorization are dissociable and distributed in the primate brain.

Understanding the structure-function relationships across neurons is challenging, particularly when circuits are composed of dozens of distinct cell types. We refined an approach, called “projection targeting with phototagging”, that allows simultaneous elucidation of the projections, morphology, and visual response properties of diverse retinal ganglion cell (RGC) types in the mammalian retina. The approach combines retrograde virally mediated phototagging of RGCs, microscopy, and large-scale multi-electrode array (MEA) measurements. Importantly, the approach does not rely on transgenic animals and thus is potentially generalizable across species. We validated this approach in rats by targeting retinal projections to the superior colliculus (SC). We showed that multiple RGC types project to the SC and that these results in rats align well with prior findings from transgenic mouse studies.

Understanding how the cerebral cortex communicates with subcortical areas to drive behavior remains a central question in system neuroscience. One key unresolved issue is whether prefrontal cortical outputs to motor-related subcortical regions carry predominantly motor commands1 or mixed sensory-motor signals.2,3 Retrograde optogenetics offers a powerful way to interrogate such projection-defined circuits,4,5,6,7 but its use in non-human primates has been limited.8,9,10,11 Here, we applied retrograde optogenetics in awake macaques to directly test the functional organization of the corticotectal projection from the frontal eye field (FEF) to the superior colliculus (SC). We asked whether the FEF output signals to SC are motor-dominant or broadly sensory-motor. Optical activation of this pathway evoked robust, contralateral saccades and selectively modulated reaction times, demonstrating its causal role in saccade generation. Optogenetically tagging FEF neurons projecting to SC revealed a heterogeneous population of visual, visuomotor, and motor neurons. This diverse output converged predominantly onto motor-related neurons in the SC. These findings support a visuomotor convergence model, in which diverse FEF outputs drive motor-selective SC neurons with activity sufficient for saccade generation, and thus resolve long-standing questions over the composition of FEF outputs. Additionally, our results establish retrograde optogenetics as a tool for dissecting projection-defined circuits in primates and for precisely probing the neural pathways that link perception to action.

Bayesian models, in which priors are used to optimally compensate for sensory uncertainty, have had wide-ranging success in explaining behavior across sensorimotor contexts. We recently reported, however, that humans and monkeys use a combination of Bayesian and non-Bayesian strategies when making categorical judgments of visual stability across saccades. While they used priors to compensate for internal, movement-driven sensory uncertainty, consistent with Bayesian predictions, they decreased their use of priors when faced with external, visual image uncertainty, an "anti-Bayesian" adjustment consistent with the use of a simple classifier. Here, we tested for neural correlates of these Bayesian and classifier-based strategies in the frontal eye field (FEF), a prefrontal region shown to be important for the perception of visual stability across saccades. We recorded from single FEF neurons while two rhesus macaques performed the internal (motor) and external (image) noise tasks in each session, interleaved trial by trial. FEF activity correlated with and predicted the anti-Bayesian, but not the Bayesian, behavior. These results suggest that the two computational strategies for visual stability are implemented by distinct neural circuits and provide a first step toward an integrated understanding of the computational and neural mechanisms underlying visual perception across saccades. ### Competing Interest Statement The authors have declared no competing interest.

Optogenetics affords new opportunities to interrogate neuronal circuits that control behavior. In primates, the usefulness of optogenetics in studying cognitive functions remains a challenge. The technique has been successfully wielded, but behavioral effects have been demonstrated primarily for sensorimotor processes. Here, we tested whether brief optogenetic suppression of primate superior colliculus can change performance in a covert attention task, in addition to previously reported optogenetic effects on saccadic eye movements. We used an attention task that required the monkey to detect and report a stimulus change at a cued location via joystick release, while ignoring changes at an uncued location. When the cued location was positioned in the response fields of transduced neurons in the superior colliculus, transient light delivery coincident with the stimulus change disrupted the monkey's detection performance, significantly lowering hit rates. When the cued location was elsewhere, hit rates were unaltered, indicating that the effect was spatially specific and not a motor deficit. Hit rates for trials with only one stimulus were also unaltered, indicating that the effect depended on selection among distractors rather than a low-level visual impairment. Psychophysical analysis revealed that optogenetic suppression increased perceptual threshold, but only for locations matching the transduced site. These data show that optogenetic manipulations can cause brief and spatially specific deficits in covert attention, independent of sensorimotor functions. This dissociation of effect, and the temporal precision provided by the technique, demonstrates the utility of optogenetics in interrogating neuronal circuits that mediate cognitive functions in the primate.

Electrical stimulation of the brain is being developed as a treatment for an increasing number of neurological disorders. Technologies for delivering electrical stimulation are advancing rapidly and vary in specificity, coverage, and invasiveness. Supracortical microstimulation (SCMS), characterized by microelectrode contacts placed on the epidural or subdural cortical surface, achieves a balance between the advantages and limitations of other electrical stimulation technologies by delivering spatially precise activation without disrupting the integrity of the cortex. However, in vivo experiments involving SCMS have not been comprehensively summarized. Here, we review the field of SCMS, focusing on recent advances, to guide the development of clinically translatable supracortical microelectrodes. We also highlight the gaps in our understanding of the biophysical effects of this technology. Future work investigating the unique electrochemical properties of supracortical microelectrodes and validating SCMS in nonhuman primate preclinical studies can enable rapid clinical translation of innovative treatments for humans with neurological disorders.

Understanding how the cerebral cortex communicates with subcortical areas to drive behavior remains a central question in system neuroscience. One key unresolved issue is whether prefrontal cortical outputs to motor-related subcortical regions carry predominantly motor commands or mixed sensory-motor signals. Retrograde optogenetics offers a powerful way to interrogate such projection-defined circuits, but its use in non-human primates has been limited. Here, we applied retrograde optogenetics in awake macaques to directly test the functional organization of the corticotectal projection from the frontal eye field (FEF) to the superior colliculus (SC). We asked whether the FEF output signals to SC are motor-dominant or broadly sensory-motor. Optical activation of this pathway evoked robust, contralateral saccades and selectively modulated reaction times, demonstrating its causal role in saccade generation. Optogenetically tagging FEF neurons projecting to SC revealed a heterogeneous population of visual, visuomotor, and motor neurons. This diverse output converged predominantly onto motor-related neurons in the SC. These findings support a visuomotor convergence model, in which diverse FEF outputs drive motor-selective SC neurons with activity sufficient for saccade generation, and thus resolve long-standing questions over the composition of FEF outputs. Additionally, our results establish retrograde optogenetics as a tool for dissecting projection-defined circuits in primates and for precisely probing the neural pathways that link perception to action.

Inferential decision-making algorithms typically assume that an underlying probabilistic model of decision alternatives and outcomes may be learned a priori or online. Furthermore, when applied to robots in real-world settings they often perform unsatisfactorily or fail to accomplish the necessary tasks because this assumption is violated and/or because they experience unanticipated external pressures and constraints. Cognitive studies presented in this and other papers show that humans cope with complex and unknown settings by modulating between near-optimal and satisficing solutions, including heuristics, by leveraging information value of available environmental cues that are possibly redundant. Using the benchmark inferential decision problem known as “treasure hunt”, this paper develops a general approach for investigating and modeling active perception solutions under pressure. By simulating treasure hunt problems in virtual worlds, our approach learns generalizable strategies from high performers that, when applied to robots, allow them to modulate between optimal and heuristic solutions on the basis of external pressures and probabilistic models, if and when available. The result is a suite of active perception algorithms for camera-equipped robots that outperform treasure-hunt solutions obtained via cell decomposition, information roadmap, and information potential algorithms, in both high-fidelity numerical simulations and physical experiments. The effectiveness of the new active perception strategies is demonstrated under a broad range of unanticipated conditions that cause existing algorithms to fail to complete the search for treasures, such as unmodelled time constraints, resource constraints, and adverse weather (fog).

BackgroundTranscranial magnetic stimulation (TMS) is used to treat a range of brain disorders by inducing an electric field (E-field) in the brain. However, the precise neural effects of TMS are not well understood. Nonhuman primates (NHPs) are used to model the impact of TMS on neural activity, but a systematic method of quantifying the induced E-field in the cortex of NHPs has not been developed.New MethodThe pipeline uses statistical parametric mapping (SPM) to automatically segment a structural MRI image of a rhesus macaque into five tissue compartments. Manual corrections are necessary around implants. The segmented tissues are tessellated into 3D meshes used in finite element method (FEM) software to compute the TMS induced E-field in the brain. The gray matter can be further segmented into cortical laminae using a volume preserving method for defining layers.ResultsModels of three NHPs were generated with TMS coils placed over the precentral gyrus. Two coil configurations, active and sham, were simulated and compared. The results demonstrated a large difference in E-fields at the target. Additionally, the simulations were calculated using two different E-field solvers and were found to not significantly differ.Comparison with Existing MethodsCurrent methods segment NHP tissues manually or use automated methods for only the brain tissue. Existing methods also do not stratify the gray matter into layers.ConclusionThe pipeline calculates the induced E-field in NHP models by TMS and can be used to plan implant surgeries and determine approximate E-field values around neuron recording sites.

Transcranial magnetic stimulation (TMS) is a non-invasive, FDA-cleared treatment for neuropsychiatric disorders with broad potential for new applications, but the neural circuits that are engaged during TMS are still poorly understood. Recordings of neural activity from the corticospinal tract provide a direct readout of the response of motor cortex to TMS, and therefore a new opportunity to model neural circuit dynamics. The study goal was to use epidural recordings from the cervical spine of human subjects to develop a computational model of a motor cortical macrocolumn through which the mechanisms underlying the response to TMS, including direct and indirect waves, could be investigated. An in-depth sensitivity analysis was conducted to identify important pathways, and machine learning was used to identify common circuit features among these pathways. Sensitivity analysis identified neuron types that preferentially contributed to single corticospinal waves. Single wave preference could be predicted using the average connection probability of all possible paths between the activated neuron type and L5 pyramidal tract neurons (PTNs). For these activations, the total conduction delay of the shortest path to L5 PTNs determined the latency of the corticospinal wave. Finally, there were multiple neuron type activations that could preferentially modulate a particular corticospinal wave. The results support the hypothesis that different pathways of circuit activation contribute to different corticospinal waves with participation of both excitatory and inhibitory neurons. Moreover, activation of both afferents to the motor cortex as well as specific neuron types within the motor cortex initiated different I-waves, and the results were interpreted to propose the cortical origins of afferents that may give rise to certain I-waves. The methodology provides a workflow for performing computationally tractable sensitivity analyses on complex models and relating the results to the network structure to both identify and understand mechanisms underlying the response to acute stimulation.

The brain interprets sensory inputs to guide behavior, but behavior itself disrupts sensory inputs. Perceiving a coherent world while acting in it constitutes active perception. For example, saccadic eye movements displace visual images on the retina and yet the brain perceives visual stability. Because this percept of visual stability has been shown to be influenced by prior expectations, we tested the hypothesis that it is Bayesian. The key prediction was that priors would be used more as sensory uncertainty increases. Humans and rhesus macaques reported whether an image moved during saccades. We manipulated both prior expectations and levels of sensory uncertainty. All psychophysical data were compared with the predictions of Bayesian ideal observer models. We found that humans were Bayesian for continuous judgments. For categorical judgments, however, they were anti-Bayesian: they used their priors less with greater uncertainty. We studied this categorical result further in macaques. The animals' judgments were similarly anti-Bayesian for sensory uncertainty caused by external, image noise, but Bayesian for uncertainty due to internal, motor-driven noise. A discriminative learning model explained the anti-Bayesian effects. We conclude that active vision uses both Bayesian and discriminative models depending on task requirements (continuous vs categorical) and the source of uncertainty (image noise vs motor-driven noise). In the context of previous knowledge about the saccadic system, our results provide an example of how the comparative analysis of Bayesian versus non-Bayesian models of perception offers novel insights into underlying neural organization.

Viral vector technologies are commonly used in neuroscience research to understand and manipulate neural circuits, but successful applications of these technologies in non-human primate models have been inconsistent. An essential component to improve these technologies is an impartial and accurate assessment of the effectiveness of different viral constructs in the primate brain. We tested a diverse array of viral vectors delivered to the brain and extraocular muscles of macaques and compared three methods for histological assessment of viral-mediated fluorescent transgene expression: epifluorescence (Epi), immunofluorescence (IF), and immunohistochemistry (IHC). Importantly, IF and IHC identified a greater number of transduced neurons compared to Epi. Furthermore, IF and IHC reliably provided enhanced visualization of transgene in most cellular compartments (i.e., dendritic, axonal, and terminal fields), whereas the degree of labeling provided by Epi was inconsistent and predominantly restricted to somas and apical dendrites. Because Epi signals are unamplified (in contrast to IF and IHC), Epi may provide a more veridical assessment for the amount of accumulated transgene and, thus, the potential to chemogenetically or optogenetically manipulate neuronal activity. The comparatively weak Epi signals suggest that the current generations of viral constructs, regardless of delivered transgene, are not optimized for primates. This reinforces an emerging viewpoint that viral vectors tailored for the primate brain are necessary for basic research and human gene therapy.

Abstract The oculomotor system is a network of neural circuitry that controls where one looks. Because the purpose of looking around a scene is to see details, the oculomotor system is closely tied to the visual system. A central hub for visuo-oculomotor coordination is the thalamus. Specific regions of the thalamus collect information from the rest of the central nervous system. A few salient examples are the pulvinar and mediodorsal thalamus (MD) that receive midbrain input, the ventrolateral thalamic nuclei that receive cerebellar input, and the lateral geniculate nucleus (LGN) that receives retinal input. In turn, these thalamic regions project to visual and oculomotor regions of cerebral cortex. A major signal conveyed by all these connections is neural activity related to eye movements. Broadly speaking, the oculomotor thalamus is the collection of nuclei that convey eye movement signals. The signals are used for both generating eye movements and monitoring them. The monitoring function, known as efference copy, allows the visual system to compensate for blur and image shifts caused by fast eye movements. This chapter outlines the current state of conceptual understanding about the role of the oculomotor thalamus in perception and behavior. The narrative approach is to consolidate the conclusions from many laboratories about the nuclei, connections, signals, and functions of the neural systems involved.

The brain interprets sensory inputs to guide behavior, but behavior itself disrupts sensory inputs. Perceiving a coherent world while acting in it constitutes active perception. For example, saccades constantly displace the retinal image and yet, we perceive a stable world. The visual system must compare the predicted sensory consequence of each saccade with the incoming sensory input to judge whether a mismatch occurred. This process is vulnerable to sensory uncertainty from two potential sources: external noise in the world (“image noise”) and internal uncertainty due to one’s own movements (“motor-driven noise”). Since Bayesian models have been influential in explaining how priors can compensate for sensory uncertainty, we tested whether they are used for visual stability across saccades. We found that non-human primates (2 rhesus macaques) used priors to compensate for internal, motor-driven noise in a Bayesian manner. For external, image noise, however, they were anti-Bayesian. Instead, they likely used a discriminative strategy, suggesting that vision across saccades is governed by both Bayesian and discriminative strategies. Next, we tested whether single neurons in the Frontal Eye Field (FEF), which receives both internal saccade-related and external visual information, support either the Bayesian or anti-Bayesian (discriminative) strategies. We found that FEF neurons predict the anti-Bayesian, but not Bayesian, behavior. Taken together, the results demonstrate that Bayesian and discriminative computations for visual stability are dissociable at the behavioral and neural levels and situate FEF along a pathway that selectively supports the discriminative contribution.

ABSTRACTTranscranial magnetic stimulation (TMS) has become an important technique in both scientific and clinical practices, and yet our understanding of how the brain responds to TMS is still limited. Concurrent neuroimaging during TMS may bridge this gap, and emerging evidence suggests widespread that modulatory effects of TMS may be best captured through changes in functional connectivity between distributed networks, rather than local changes in cortical activity. However, the relationship between TMS stimulation parameters and evoked changes in functional connectivity is unknown. In this study, 24 healthy volunteers received concurrent TMS-fMRI while performing a dot-motion direction discrimination task. An MR-compatible coil was used to apply trains of three pulses at 10 Hz rTMS over the primary visual cortex (V1) at the onset of the dot stimuli with four levels of stimulation intensity (20%, 40%, 80%, and 120% of resting motor threshold, RMT). Behavioral results demonstrated impairment of motion discrimination at 80% RMT. FMRI results yielded three findings. First, functional connectivity between visual and non-visual areas increased as a function of rTMS intensity. Second, connectivitywithinthe visual network was positively associated with motion accuracy, while the connectivitybetweenvisual and non-visual regions was negatively associated with motion accuracy. Lastly, we found that reductions in the similarity between functional and structural connectivity associated with increasing TMS intensity were constrained to the visual network. These findings demonstrate spatially dependent nonlinear effects of TMS intensity on brain functional connectivity that proceed beyond the site of stimulation and influence associated behavior.