Biology subjects

Voloh, B.

Publications and source records attributed to Voloh, B..

7 recordsLinked to original sources

bioRxiv2022

Prefrontal control of actions in freely moving macaques

Our natural behavioral repertoires include complex coordinated actions of characteristic types. To better understand the organization of action and its neural underpinnings, we examined behavior and neural activity in rhesus macaques performing a freely moving foraging task in an open environment. We developed a novel analysis pipeline that can identify meaningful units of behavior, corresponding to recognizable actions such as sitting, walking, jumping, and climbing. On the basis of action transition probabilities, we found that behavior was organized in a modular and hierarchical fashion. We found that, after regressing out many potential confounders, actions are associated with specific patterns of firing in each of six prefrontal brain regions and that, overall, representation of actions is progressively stronger in more dorsal and more caudal prefrontal regions. Conversely, we found that switching between actions resulted in changed firing rates, with more rostral and more ventral regions showing stronger effects. Together, these results establish a link between control of action state and neuronal activity in prefrontal regions in the primate brain.

neuroscience↗

bioRxiv2022

Widespread coding of navigational variables in prefrontal cortex

To navigate, we must represent information about our place in the environment. Traditional research highlights the role of the hippocampal complex in this process. Spurred by recent research highlighting the widespread cortical encoding of cognitive and motor variables previously thought to have localized function, we hypothesized that navigational variables would be likewise encoded widely, especially in the prefrontal cortex, which is often associated with control of volitional behavior. We recorded neural activity from six prefrontal structures while macaques performed a foraging task in an open enclosure. In all six regions, we found strong encoding of allocentric position, head direction, egocentric boundary distance, and linear and angular velocity. These encodings were not accounted for by distance or time to reward. Strength of coding of all variables increase along a ventral-to-dorsal gradient. Together these results argue that encoding of navigational variables is not localized to the hippocampal complex and support the hypothesis that navigation is continuous with other forms of flexible cognition in the service of action.

neuroscience↗

bioRxiv2021

Hierarchical organization of rhesus macaque behavior

Primatologists, psychologists and neuroscientists have long hypothesized that primate behavior is highly structured. However, fully delineating that structure has been impossible due to the difficulties of precision behavioral tracking. Here we analyzed a dataset consisting of continuous measures of the 3D position of fifteen body landmarks from two male rhesus macaques (Macaca mulatta) performing three different tasks in a large unrestrained environment over many hours. Using an unsupervised embedding approach on the tracked joints, we identified commonly repeated pose patterns, which we call postures. We found that macaques behavior is characterized by 49 distinct identifiable postures, lasting an average of 0.6 seconds each. We found evidence that behavior is hierarchically organized, in that transitions between poses tend to occur within larger modules, which correspond to intuitively identifiably actions; these actions are in turn organized hierarchically. Our behavioral decomposition allows us to identify universal (cross-individual and cross-task) and unique (specific to each individual and task) principles of behavior. These results demonstrate the hierarchical nature of primate behavior and provide a method for the automated "ethogramming" of primate behavior.

animal behavior and cognition↗

bioRxiv2019

Phase of Firing Coding of Learning Variables across Prefrontal Cortex, Anterior Cingulate Cortex and Striatum during Feature Learning

The prefrontal cortex and striatum form a recurrent network whose spiking activity encodes multiple types of learning-relevant information. This spike-encoded information is evident in average firing rates, but finer temporal coding might allow multiplexing and enhanced readout across the connected the network. We tested this hypothesis in the fronto-striatal network of nonhuman primates during reversal learning of feature values. We found that neurons encoding current choice outcomes, outcome prediction errors, and outcome history in their firing rates also carried significant information in their phase-of-firing at a 10-25 Hz beta frequency at which they synchronized across lateral prefrontal cortex, anterior cingulate cortex and striatum. The phase-of-firing code exceeded information that could be obtained from firing rates alone, was strong for inter-areal connections, and multiplexed information at three different phases of the beta cycle that were offset from the preferred spiking phase of neurons. Taken together, these findings document the multiplexing of three different types of information in the phase-of-firing at an interareally shared beta oscillation frequency during goal-directed behavior. HighlightsO_LILateral prefrontal cortex, anterior cingulate cortex and striatum show phase-of-firing encoding for outcome, outcome history and reward prediction errors. C_LIO_LINeurons with phase-of-firing code synchronize long-range at 10-25 Hz. C_LIO_LISpike phases encoding reward prediction errors deviate from preferred synchronization phases. C_LIO_LIAnterior cingulate cortex neurons show strongest long-range effects. C_LI

neuroscience↗

bioRxiv2018

USE: An integrative suite for temporally-precise psychophysical experiments in virtual environments

11.1 BackgroundThere is a growing interest in complex, active, and immersive behavioral neuroscience tasks. However, the development and control of such tasks present unique challenges.\n\n1.2 New MethodThe Unified Suite for Experiments (USE) is an integrated set of hardware and software tools for the design and control of behavioral neuroscience experiments. The software, developed using the Unity video game engine, supports both active tasks in immersive 3D environments and static 2D tasks used in more traditional visual experiments. The custom USE SyncBox hardware, based around an Arduino Mega2560 board, integrates and synchronizes multiple data streams from different pieces of experimental hardware. The suite addresses three key issues with developing cognitive neuroscience experiments in Unity: tight experimental control, accurate sub-ms timing, and accurate gaze target identification.\n\n1.3 ResultsUSE is a flexible framework to realize experiments, enabling (i) nested control over complex tasks, (ii) flexible use of 3D or 2D scenes and objects, (iii) touchscreen-, button-, joystick- and gaze-based interaction, and (v) complete offline reconstruction of experiments for post-processing and temporal alignment of data streams.\n\n1.4 Comparison with Existing MethodsMost existing experiment-creation tools are not designed to support the development of video-game-like tasks. Those that do use older or less popular video game engines as their base, and are not as feature-rich or enable as precise control over timing as USE.\n\n1.5 ConclusionsUSE provides an integrated, open source framework for a wide variety of active behavioral neuroscience experiments using human and nonhuman participants, and artificially-intelligent agents.\n\n2 GlossaryO_LIActive task: Experimental tasks which involve some combination of realistic, usually moving, stimuli, continuous opportunities for action, ecologically valid tasks, complex behaviours, etc. Here, they are contrasted with static tasks (see below)\nC_LIO_LIArduino: A multi-purpose generic micro-processor, here used to control inter-device communication and time synchronization.\nC_LIO_LIRaycast: A game-engine method that sends a vector between two points in a virtual three-dimensional environment, and returns the first object in that environment it hits. Often used to determine if a character in a game can see or shoot another character.\nC_LIO_LIState Machine (also Finite State Machine): A way of conceptualizing and implementing control in software, such that at any one moment the software is in one, and only one, state. In hierarchical state machines, as used in the present software suite, these can be organized into different levels, such that each level can only be in one state, but a state can pass control to a lower level.\nC_LIO_LIStatic task: Experimental tasks like those traditionally used in the cognitive neurosciences. Simple, usually stationary, stimuli, limited opportunities for action, simple behaviours, etc. Here, they are contrasted with active tasks (see above).\nC_LIO_LIUnity: One of the most popular video game engines. Freely available.\nC_LIO_LIVideo game engine: A software development kit designed to handle many of the common issues involved in creating video games, such as interfacing with controllers, simulating physical collisions and lighting, etc.\nC_LI

neuroscience↗

bioRxiv2017

Quaddles: A multidimensional 3D object set with parametrically-controlled and customizable features

Many studies of vision and cognition require novel three-dimensional object sets defined by a parametric feature space. Creating such sets and verifying that they are suitable for a given task, however, can be extremely time-consuming and effortful. Here we present a new set of multidimensional objects, Quaddles, designed for a study of feature-based learning and attention, but adaptable for many research purposes. Quaddles have features that are all equally-visible from any angle around the vertical axis, and which all have similar response biases on a feature detection task, thus removing one potential source of bias in object selection. They are available as two-dimensional images, rotating videos, and FBX object files suitable for use with any modern video game engine. We also provide examples and tutorials for modifying Quaddles or creating completely new object sets from scratch, hopefully greatly speeding up the development time of future novel object studies.\n\nAuthors noteThis work was supported by grant MOP 102482 from the Canadian Institutes of Health Research (TW) and by the Natural Sciences and Engineering Research Council of Canada Brain in Action CREATE-IRTG program (MRW, TW). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of this manuscript. Authors would like to thank Hongying Wang for technical support, and Isabel Gauthier for comments on a draft version of the manuscript.\n\nThe study described herein was approved by the York University Office of Research Ethics (Certificate # 2016-214).\n\nA draft version of this manuscript is available online at BioR{chi}iv:

neuroscience↗

bioRxiv2017

Cell-Type Specific Burst Firing Interacts With Theta And Beta Activity In Prefrontal Cortex During Attention States

Population-level theta and beta band activity in anterior cingulate and prefrontal cortex (ACC/PFC) are prominent signatures of endogenously controlled, adaptive behaviors. But how these rhythmic activities are linked to cell-type specific activity has remained unclear. Here, we suggest such a cell-to-systems level linkage. We found that the rate of burst spiking events is enhanced particularly during attention states and that attention-specific burst spikes have a unique temporal relationship to local theta and beta band population level activities. For the 5-10Hz theta frequency range, bursts coincided with transient increases of local theta power relative to non-bursts, particularly for bursts of putative interneurons. For the 16-30Hz beta frequency, bursts of putative interneurons phase synchronized stronger than nonbursts, and were associated with larger beta power modulation. In contrast, burst of putative pyramidal cells were overall similarly beta-synchronized than nonbursts, but were linked with stronger beta power only when they occurred early in the beta cycle. These findings suggests that in the ACC/PFC during attention states, mechanisms underlying burst firing are intimately linked to narrow band population level activities, providing a cell-type specific window into the emergence, resetting, or termination of oscillatory activities.

neuroscience↗