Biology subjects

Womelsdorf, T.

Publications and source records attributed to Womelsdorf, T..

8 recordsLinked to original sources

bioRxiv2018

Interactions of medial and lateral prefrontal cortex in hierarchical predictive coding

Cognitive control and decision-making relies on the interplay of medial and lateral prefrontal cortex (mPFC/LPFC), particularly for circumstances in which correct behavior requires integrating and selecting among multiple sources of interrelated information. While the interaction between mPFC and LPFC is generally acknowledged as a crucial circuit in adaptive behavior, the nature of this interaction remains open to debate, with various proposals suggesting complementary roles in (i) signaling the need for and implementing control, (ii) identifying and selecting appropriate behavioral policies from a candidate set, and (iii) constructing behavioral schemata for performance of structured tasks. Although these proposed roles capture salient aspects of conjoint mPFC/LPFC function, none are sufficiently well-specified to provide a detailed account of the continuous interaction of the two regions during ongoing behavior. A recent computational model of mPFC and LPFC, the Hierarchical Error Representation (HER) model, places the regions within the framework of hierarchical predictive coding, and suggests how they interact during behavioral periods preceding and following salient events. In this manuscript, we extend the HER model to incorporate real-time temporal dynamics and demonstrate how the extended model is able to capture single-unit neurophysiological, behavioral, and network effects previously reported in the literature. Our results add to the wide range of results that can be accounted for by the HER model, and provide further evidence for predictive coding as a unifying framework for understanding PFC function and organization.

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↗

bioRxiv2018

Asymmetric effective connectivity between primate anterior cingulate and lateral prefrontal cortex revealed by electrical microstimulation.

The anterior cingulate cortex (ACC) and lateral prefrontal cortex (IPFC) of the non-human primate show neural firing correlations and synchronize at theta and beta frequencies during the monitoring and shifting of attention. These functional interactions might be based on synaptic connectivity that is equally efficacious in both directions, but it might be that there are systematic asymmetries in connectivity consistent with reports of more effective inhibition within the ACC than IPFC, or with a preponderance of ACC projections synapsing onto inhibitory neurons in the IPFC. Here, we tested effective ACC-IPFC connectivity in awake monkeys and report systematic asymmetries in the temporal patterning and latencies of effective connectivity as measured using electrical microstimulation. We found that ACC stimulation triggered evoked fields (EFPs) were more likely to be multiphasic in the IPFC than in the reverse direction, with a large proportion of connections showing 2-4 inflection points resembling resonance in the 20-30 Hz beta frequency range. Stimulation of ACC [->] IPFC resulted, on average, in shorter-latency EFPs than IPFC [->] ACC. Overall, latencies and connectivity strength varied more than two-fold depending on the precise anterior-to-posterior location of the connections. These findings reveal systematic asymmetries in effective connectivity between ACC and IPFC in the awake non-human primate and document the spatial and temporal patchiness of effective synaptic connections. We speculate that measuring effective connectivity profiles will be essential for understanding how local synaptic efficacy and synaptic connectivity translates into functional neuronal interactions to support adaptive behaviors.

neuroscience↗

bioRxiv2018

Double Dissociation of Nicotinic alpha-7 and alpha-4/beta-2 Sub-receptor Agonists for Enhancing Learning and Attentional Filtering of Distraction

Nicotinic acetylcholine receptors (nAChR) modulate attention, memory, and higher executive functioning, but it has remained unclear whether nAChR sub-receptors tap into different neural mechanisms of these functions. We therefore set out to contrast the contributions of selective alpha-7 nAChR and alpha-4/beta-2 nAChR agonists in mediating value learning and attentional filtering of distractors in the nonhuman primate. We found that the alpha-7 nAChR agonist PHA-543613 selectively enhanced the learning speed of feature values but did not modulate how salient distracting information was filtered from ongoing choice processes. In contrast, the selective alpha-4/beta-2 nAChR agonist ABT-089 did not affect learning speed but reduced distractibility. This double dissociation was dose-dependent and evident in the absence of systematic changes in overall performance, reward intake, motivation to perform the task, perseveration tendencies, or reaction times. These results suggest nicotinic sub-receptor-specific mechanisms consistent with (1) alpha-4/beta-2 nAChR specific amplification of cholinergic transients in prefrontal cortex linked to enhanced cue detection in light of interferences, and (2) alpha-7 nAChR specific activation prolonging cholinergic transients, which could facilitate subjects to follow-through with newly established attentional strategies when outcome contingencies change. These insights will be critical for developing function-specific drugs alleviating attention and learning deficits in neuro-psychiatric diseases.

neuroscience↗

bioRxiv2018

Multiscale Adaptive Gabor Expansion (MAGE): Improved Detection of Transient Oscillatory Burst Amplitude and Phase

Since Denis Gabors pioneering paper on the discrete Gabor Expansion (Gabor, 1946), time-frequency signal analysis has proven to be an important tool for many fields. In neurophysiology, time-frequency analysis has often been used to characterize and describe transient bursts in local field potential data. However, these transient bursts have a wide range of variable durations, suggesting that a time-frequency-scale dictionary composed of elementary signal "atoms" may prove useful to more accurately match recorded bursts. While overcomplete multiscale dictionaries are useful, generating a sparse code over such dictionaries is a difficult computational problem. Existing adaptive algorithms for discovering a sparse description are slow and computationally intensive. Here we describe the Multiscale Adaptive Gabor Expansion (MAGE), which uses an implicit dictionary of parametric time-frequency-scale Gabor atoms to perform fast parameter reassignment to accelerate discovery of a sparse decomposition. Using analytic expressions together with numerical computation, MAGE is a greedy pursuit algorithm similar to Matching Pursuit, restricted to a dictionary of multiscale Gaussian-envelope Gabor atoms. MAGE parameter reassignment is robust in the presence of moderate noise. By expressing a unknown signal as a weighted sum of Gabor atoms, MAGE permits a more accurate estimate of the amplitude and phase of transient bursts than existing methods.

neuroscience↗

bioRxiv2018

Feature Specific Prediction Errors and Surprise across Macaque Fronto-Striatal Circuits during Attention and Learning

Prediction errors signal unexpected outcomes indicating that expectations need to be adjusted. For adjusting expectations efficiently prediction errors need to be associated with the precise features that gave rise to the unexpected outcome. For many visual tasks this credit assignment proceeds in a multidimensional feature space that makes it ambiguous which object defining features are relevant. Here, we report of a potential solution by showing that neurons in all areas of the medial and lateral fronto-striatal networks encode prediction errors that are specific to separate features of attended multidimensional stimuli, with the most ubiquitous prediction error occurring for the reward relevant features. These feature specific prediction error signals (1) are different from a non-specific prediction error signal, (2) arise earliest in the anterior cingulate cortex and later in lateral prefrontal cortex, caudate and ventral striatum, and (3) contribute to feature-based stimulus selection after learning. These findings provide strong evidence for a widely-distributed feature-based eligibility trace that can be used to update synaptic weights for improved feature-based attention.\n\nHighlightsO_LINeural reward prediction errors carry information for updating feature-based attention in all areas of the fronto-striatal network.\nC_LIO_LIFeature specific neural prediction errors emerge earliest in anterior cingulate cortex and later in lateral prefrontal cortex.\nC_LIO_LIVentral striatum neurons encode feature specific surprise strongest for the goal-relevant feature.\nC_LIO_LINeurons encoding feature-specific prediction errors contribute to attentional selection after learning.\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↗