Development of a Marmoset Apparatus for Automated Pulling (MarmoAAP) to Study Cooperative Behaviors.

In recent years, the field of neuroscience has increasingly recognized the importance of studying animal behaviors in naturalistic environments to gain deeper insights into ethologically relevant behavioral processes and neural mechanisms. The common marmoset (Callithrix jacchus), due to its small size, prosocial nature, and genetic proximity to humans, has emerged as a pivotal model toward this effort. However, traditional research methodologies often fail to fully capture the nuances of marmoset social interactions and cooperative behaviors. To address this critical gap, we developed the Marmoset Apparatus for Automated Pulling (MarmoAAP), a novel behavioral apparatus designed for studying cooperative behaviors in common marmosets. MarmoAAP addresses the limitations of traditional behavioral research methods by enabling high-throughput, detailed behavior outputs that can be integrated with video and audio recordings, allowing for more nuanced and comprehensive analyses even in a naturalistic setting. We also highlight the flexibility of MarmoAAP in task parameter manipulation which accommodates a wide range of behaviors and individual animal capabilities. Furthermore, MarmoAAP provides a platform to perform investigations of neural activity underlying naturalistic social behaviors. MarmoAAP is a versatile and robust tool for advancing our understanding of primate behavior and related cognitive processes. This new apparatus bridges the gap between ethologically relevant animal behavior studies and neural investigations, paving the way for future research in cognitive and social neuroscience using marmosets as a model organism.

Automated 3D analysis of social head-gaze behaviors in freely moving marmosets.

Social communication relies on the ability to perceive and interpret the direction of others' attention, which is commonly conveyed through head orientation and gaze direction in both humans and non-human primates. However, traditional social gaze experiments in non-human primates require restraining head movements, which significantly limit their natural behavioral repertoire. Here, we developed a novel framework for accurately tracking facial features and three-dimensional head gaze orientations of multiple freely moving common marmosets (Callithrix jacchus). To accurately track the facial features of marmoset dyads in an arena, we adapted computer vision tools using deep learning networks combined with triangulation algorithms applied to the detected facial features to generate dynamic geometric facial frames in 3D space, overcoming common occlusion challenges. Furthermore, we constructed a virtual cone, oriented perpendicular to the facial frame, to model the head gaze directions. Using this framework, we were able to detect different types of interactive social gaze events, including partner-directed gaze and jointly-directed gaze to a shared spatial location. We observed clear effects of sex and familiarity on both interpersonal distance and gaze dynamics in marmoset dyads. Unfamiliar pairs exhibited more stereotyped patterns of arena occupancy, more sustained levels of social gaze across inter-animal distance, and increased gaze monitoring. On the other hand, familiar pairs exhibited higher levels of joint gazes. Moreover, males displayed significantly elevated levels of gazes toward females' faces and the surrounding regions irrespective of familiarity. Our study lays the groundwork for a rigorous quantification of primate behaviors in naturalistic settings.

Brain-state mediated modulation of inter-laminar dependencies in visual cortex.

Spatial attention is a quintessential example of adaptive information processing in the brain and is critical for recognizing behaviorally relevant objects in a cluttered environment. Object recognition is mediated by neural encoding along the ventral visual hierarchy. How the deployment of spatial attention aids these hierarchical computations is unclear. Prior studies point to two distinct mechanisms: an improvement in the efficacy of information directed from one encoding stage to another, and/or a suppression of shared information within encoding stages. To test these proposals, it is crucial to estimate the attentional modulation of unique information flow across and shared information within the encoding stages of the visual hierarchy. We investigated this in the multi-stage laminar network of visual area V4, an area strongly modulated by attention. Using network-based dependency estimation from multivariate data, we quantified the modulation of inter-layer information flow during a change detection task and found that deployment of attention indeed strengthened unique dependencies between the input and superficial layers. Using the partial information decomposition framework, we estimated the modulation of shared dependencies and found that they are reduced specifically in the putative excitatory subpopulations within a layer. Surprisingly, we found a strengthening of unique dependencies within the laminar populations, a finding not previously predicted. Crucially, these modulation patterns were also observed during successful behavioral outcomes (hits) that are thought to be mediated by endogenous brain state fluctuations, and not by experimentally imposed attentive states. Finally, phases of endogenous fluctuations that were optimal for 'hits' were associated with reduced neural excitability. A reduction in neural excitability, potentially mediated by diminished shared inputs, suggests a novel mechanism for enhancing unique information transmission during optimal states. By decomposing the modulation of multivariate information, and combined with prior theoretical work, our results suggest common computations of optimal sensory states that are attained by either task demands or endogenous fluctuations.

Laminar compartmentalization of attention modulation in area V4 aligns with the demands of visual processing hierarchy in the cortex.

Attention selectively enhances neural responses to low contrast stimuli in visual area V4, a critical hub that sends projections both up and down the visual hierarchy. Veridical encoding of contrast information is a key computation in early visual areas, while later stages encoding higher level features benefit from improved sensitivity to low contrast. How area V4 meets these distinct information processing demands in the attentive state is unknown. We found that attentional modulation in V4 is cortical layer and cell-class specific. Putative excitatory neurons in the superficial layers show enhanced boosting of low contrast information, while those of deep layers exhibit contrast-independent scaling. Computational modeling suggested the extent of spatial integration of inhibitory neurons as the mechanism behind such laminar differences. Considering that superficial neurons are known to project to higher areas and deep layers to early visual areas, our findings suggest that the interactions between attention and contrast in V4 are compartmentalized, in alignment with the demands of the visual processing hierarchy.

Laminar mechanisms of saccadic suppression in primate visual cortex.

Saccadic eye movements are known to cause saccadic suppression, a temporary reduction in visual sensitivity and visual cortical firing rates. While saccadic suppression has been well characterized at the level of perception and single neurons, relatively little is known about the visual cortical networks governing this phenomenon. Here we examine the effects of saccadic suppression on distinct neural subpopulations within visual area V4. We find subpopulation-specific differences in the magnitude and timing of peri-saccadic modulation. Input-layer neurons show changes in firing rate and inter-neuronal correlations prior to saccade onset, and putative inhibitory interneurons in the input layer elevate their firing rate during saccades. A computational model of this circuit recapitulates our empirical observations and demonstrates that an input-layer-targeting pathway can initiate saccadic suppression by enhancing local inhibitory activity. Collectively, our results provide a mechanistic understanding of how eye movement signaling interacts with cortical circuitry to enforce visual stability.

The orbitofrontal cortex: A goal-directed cognitive map framework for social and non-social behaviors.

The orbitofrontal cortex (OFC) is regarded as one of the core brain areas in a variety of value-based behaviors. Over the past two decades, tremendous knowledge about the OFC function was gained from studying the behaviors of single subjects. As a result, our previous understanding of the OFC's function of encoding decision variables, such as the value and identity of choices, has evolved to the idea that the OFC encodes a more complex representation of the task space as a cognitive map. Accumulating evidence also indicates that the OFC importantly contributes to behaviors in social contexts, especially those involved in cooperative interactions. However, it remains elusive how exactly OFC neurons contribute to social functions and how non-social and social behaviors are related to one another in the computations performed by OFC neurons. In this review, we aim to provide an integrated view of the OFC function across both social and non-social behavioral contexts. We propose that seemingly complex functions of the OFC may be explained by its role in providing a goal-directed cognitive map to guide a wide array of adaptive reward-based behaviors in both social and non-social domains.

Optogenetically induced low-frequency correlations impair perception.

Deployment of covert attention to a spatial location can cause large decreases in low-frequency correlated variability among neurons in macaque area V4 whose receptive-fields lie at the attended location. It has been estimated that this reduction accounts for a substantial fraction of the attention-mediated improvement in sensory processing. These estimates depend on assumptions about how population signals are decoded and the conclusion that correlated variability impairs perception, is purely hypothetical. Here we test this proposal directly by optogenetically inducing low-frequency fluctuations, to see if this interferes with performance in an attention-demanding task. We find that low-frequency optical stimulation of neurons in V4 elevates correlations among pairs of neurons and impairs the animal's ability to make fine sensory discriminations. Stimulation at higher frequencies does not impair performance, despite comparable modulation of neuronal responses. These results support the hypothesis that attention-dependent reductions in correlated variability contribute to improved perception of attended stimuli.

Cortical gamma band synchronization through somatostatin interneurons.

Gamma band rhythms may synchronize distributed cell assemblies to facilitate information transfer within and across brain areas, yet their underlying mechanisms remain hotly debated. Most circuit models postulate that soma-targeting parvalbumin-positive GABAergic neurons are the essential inhibitory neuron subtype necessary for gamma rhythms. Using cell-type-specific optogenetic manipulations in behaving animals, we show that dendrite-targeting somatostatin (SOM) interneurons are critical for a visually induced, context-dependent gamma rhythm in visual cortex. A computational model independently predicts that context-dependent gamma rhythms depend critically on SOM interneurons. Further in vivo experiments show that SOM neurons are required for long-distance coherence across the visual cortex. Taken together, these data establish an alternative mechanism for synchronizing distributed networks in visual cortex. By operating through dendritic and not just somatic inhibition, SOM-mediated oscillations may expand the computational power of gamma rhythms for optimizing the synthesis and storage of visual perceptions.

Modulatory compartments in cortex and local regulation of cholinergic tone.

Neuromodulatory signaling is generally considered broad in its impact across cortex. However, variations in the characteristics of cortical circuits may introduce regionally-specific responses to diffuse modulatory signals. Features such as patterns of axonal innervation, tissue tortuosity and molecular diffusion, effectiveness of degradation pathways, subcellular receptor localization, and patterns of receptor expression can lead to local modification of modulatory inputs. We propose that modulatory compartments exist in cortex and can be defined by variation in structural features of local circuits. Further, we argue that these compartments are responsible for local regulation of neuromodulatory tone. For the cholinergic system, these modulatory compartments are regions of cortical tissue within which signaling conditions for acetylcholine are relatively uniform, but between which signaling can vary profoundly. In the visual system, evidence for the existence of compartments indicates that cholinergic modulation likely differs across the visual pathway. We argue that the existence of these compartments calls for thinking about cholinergic modulation in terms of finer-grained control of local cortical circuits than is implied by the traditional view of this system as a diffuse modulator. Further, an understanding of modulatory compartments provides an opportunity to better understand and perhaps correct signal modifications that lead to pathological states.

Neurons in Macaque Area V4 Are Tuned for Complex Spatio-Temporal Patterns.

To deepen our understanding of object recognition, it is critical to understand the nature of transformations that occur in intermediate stages of processing in the ventral visual pathway, such as area V4. Neurons in V4 are selective to local features of global shape, such as extended contours. Previously, we found that V4 neurons selective for curved elements exhibit a high degree of spatial variation in their preference. If spatial variation in curvature selectivity was also marked by distinct temporal response patterns at different spatial locations, then it might be possible to untangle this information in subsequent processing based on temporal responses. Indeed, we find that V4 neurons whose receptive fields exhibit intricate selectivity also show variation in their temporal responses across locations. A computational model that decodes stimulus identity based on population responses benefits from using this temporal information, suggesting that it could provide a multiplexed code for spatio-temporal features.

Abnormal Gamma Oscillations in N-Methyl-D-Aspartate Receptor Hypofunction Models of Schizophrenia.

N-methyl-D-aspartate receptor (NMDAR) hypofunction in parvalbumin-expressing (PV+) inhibitory neurons (INs) may contribute to symptoms in patients with schizophrenia (SZ). This hypothesis was inspired by studies in humans involving NMDAR antagonists that trigger SZ symptoms. Animal models of SZ using neuropharmacology and genetic knockouts have successfully replicated some of the key observations in human subjects involving alteration of gamma band oscillations (GBO) observed in electroencephalography and magnetoencephalography signals. However, it remains to be seen if NMDAR hypofunction in PV+ neurons is fundamental to the phenotype observed in these models. In this review, we discuss some of the key computational models of GBO and their predictions in the context of NMDAR hypofunction in INs. While PV+ INs have been the main focus of SZ studies in animal models, we also discuss the implications of NMDAR hypofunction in other types of INs using computational models for GBO modulation in the visual cortex.

Regulating Cortical Oscillations in an Inhibition-Stabilized Network.

Understanding the anatomical and functional architecture of the brain is essential for designing neurally inspired intelligent systems. Theoretical and empirical studies suggest a role for narrowband oscillations in shaping the functional architecture of the brain through their role in coding and communication of information. Such oscillations are ubiquitous signals in the electrical activity recorded from the brain. In the cortex, oscillations detected in the gamma range (30-80 Hz) are modulated by behavioral states and sensory features in complex ways. How is this regulation achieved? Although several underlying principles for the genesis of these oscillations have been proposed, a unifying account for their regulation has remained elusive. In a network of excitatory and inhibitory neurons operating in an inhibition-stabilized regime, we show that strongly superlinear responses of inhibitory neurons facilitate bidirectional regulation of oscillation frequency and power. In such a network, the balance of drives to the excitatory and inhibitory populations determines how the power and frequency of oscillations are modulated. The model accounts for the puzzling increase in their frequency with the salience of visual stimuli, and a decrease with their size. Oscillations in our model grow stronger as the mean firing level is reduced, accounting for the size dependence of visually evoked gamma rhythms, and suggesting a role for oscillations in improving the signal-to-noise ratio (SNR) of signals in the brain. Empirically testing such predictions is still challenging, and implementing the proposed coding and communication strategies in neuromorphic systems could assist in our understanding of the biological system.

Cortical oscillations arise from contextual interactions that regulate sparse coding.

Precise spike times carry information and are important for synaptic plasticity. Synchronizing oscillations such as gamma bursts could coordinate spike times, thus regulating information transmission in the cortex. Oscillations are driven by inhibitory neurons and are modulated by sensory stimuli and behavioral states. How their power and frequency are regulated is an open question. Using a model cortical circuit, we propose a regulatory mechanism that depends on the activity balance of monosynaptic and disynaptic pathways to inhibitory neurons: Monosynaptic input causes more powerful oscillations whereas disynaptic input increases the frequency of oscillations. The balance of stimulation to the two pathways modulates the overall distribution of spikes, with stronger disynaptic stimulation (e.g., preferred stimuli inside visual receptive fields) producing high firing rates and weak oscillations; in contrast, stronger monosynaptic stimulation (e.g., suppressive contextual stimulation from outside visual receptive fields) generates low firing rates and strong oscillatory regulation of spike timing, as observed in alert cortex processing complex natural stimuli. By accounting for otherwise paradoxical experimental findings, our results demonstrate how the frequency and power of oscillations, and hence spike times, can be modulated by both sensory input and behavioral context, with powerful oscillations signifying a cortical state under inhibitory control in which spikes are sparse and spike timing is precise.

An Augmented Two-Layer Model Captures Nonlinear Analog Spatial Integration Effects in Pyramidal Neuron Dendrites.

In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based "technology" that underlies the brain's remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or "neuron," yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees.