Biology subjects

Whittington, J.

Publications and source records attributed to Whittington, J..

4 recordsLinked to original sources

bioRxiv2026

An Efficient Computing Theory of Prefrontal Structured Working Memory Representations

AO_SCPLOWBSTRACTC_SCPLOWThe efficient coding hypothesis presents a compelling success story for theoretical and systems neu-roscience. It marshals a unifying idea, that neural codes can be understood as efficient encodings of natural stimuli, to explain phenomena from across sensory systems, sometimes with exquisite precision. However, similar normative assaults on cognitive representations, such as those in prefrontal or entorhinal cortex, have been less comprehensively successful. We argue that this reflects efficient codings focus on encoded variables, overlooking the computations that neural circuits implement. Here, instead, we develop an efficient computing theory that studies optimal implementations of recurrent cognitive computations. We apply this framework to the prefrontal cortex, in particular, to a rich vein of neural recordings: structured working memory tasks, such as recalling a sequence. Despite using near-identical tasks, the literature has reported two distinct coding schemes: one contextual, with neurons active only in a particular sequence, the other compositional, with neurons tuned to a single sequence element. Just as efficient coding links stimuli statistics to optimal representation, our theory relates task structure and statistics to optimal representation. In so doing, we find that compositional and contextual codes can be understood as two extremes of a spectrum of optimal representations, with the correlations amongst sequence elements determining a tasks position on this spectrum. Our theory highlights previously underappreciated discrepancies between measured representations, explaining them via subtle task differences; and allows us to infer the algorithm underlying otherwise ambiguous neural data. In sum, we demonstrate an efficient computing approach that makes normative statements about cognitive representations, and serves as a tool for understanding a swathe of neural data.

neuroscience↗

bioRxiv2025

Inferential planning in the frontal cortex

How the brain plans and maintains sequences of future actions remains a central question in systems neuroscience. Recent studies in the frontal cortex have revealed that multiple elements of a sequence are represented simultaneously in separable neural subspaces, challenging classical serial models of sequential planning. Here, we show that these representations emerge naturally under inferential planning in which sequential actions are inferred from sensory evidence and goals. Using a hierarchical generative model, we reproduce key neural phenomena observed in primate frontal cortex, including the simultaneous activation of multiple plan elements, the emergence of (almost) orthogonal memory subspaces, and their reuse across forward and backward sequence tasks. Our approach provides a mechanistic account of how probabilistic inference over control states gives rise to distributed and dynamic neural representations of plans. This framework not only unifies previously disparate findings on planning, working memory, and motor preparation, but also generates novel, testable predictions about the dynamics of active inference, the role of sensory subspaces, and the impact of uncertainty on sequence processing.

neuroscience↗

bioRxiv2021

Towns and Trails Drive Carnivore Connectivity using a Step Selection Approach

Global increases in human activity threaten connectivity of animal populations. Protection and restoration of animal movement corridors requires robust models to forecast the effects of human activity on connectivity. Recent advances in the field of animal movement ecology and step selection functions offer new approaches for estimating connectivity. We show how a combination of hidden Markov movement models and step selection functions can be used to simulate realistic movement paths with multiple behavioral states. Simulated paths can be used to generate utilization distributions and estimate changes in connectivity for multiple land use scenarios. We applied movement models to 20 years of grizzly bear (Ursus arctos) and gray wolf (Canis lupus) data collected in and around Banff National Park, Canada. These carnivores avoided areas near towns in all seasons, avoided areas of high trail density in most seasons, and campgrounds during summer and fall. We simulated movement paths for three landscape scenarios: reference conditions with no anthropogenic development, current conditions, and future conditions with expanded town footprints and trail networks. We counted the number of paths that crossed valley-wide, digital transects through mountain tourist towns of Banff and Canmore, Alberta. We divided current and future crossing rates by the reference crossing rates to estimate connectivity. Current connectivity rates ranged between 7 and 45% of reference values with an average of 21% for grizzly bears and 25% for wolves. Potential town expansion and increased development of trails further decreased connectivity an average of 6% in future scenarios. Anthropogenic developments reduced the amount of available high quality large carnivore habitat in the Bow Valley by an average of 14% under current conditions and 16% under future conditions. Our approach for estimating connectivity provides a robust and flexible method for combining movement models with step selection analyses to estimate connectivity for a variety of species.

ecology↗

bioRxiv2017

Classifying smartphone-based accelerometer data to obtain validated measures of subject activity status, step count, and gait speed

BackgroundThe ubiquitous spread of smartphone technology throughout global societies offers an unprecedented opportunity to ethically obtain long-term, highly accurate measurements of individual physical activity. For example, the smartphone intrinsic 3-D accelerometer can be queried during normal phone operation to save time series of acceleration magnitudes (in each of the component directions) for near-real time or post processing.\n\nObjectiveWe describe simple, straightforward algorithms (based on windowed Fourier analysis) for accelerometer data quality control and behavioral classification.\n\nMethodsTo maximize the clinical utility of our classifications, we focused on differentiating the following conditions: forgotten phone, subject resting, low physical activity, high physical activity. We further differentiated high physical activity into epochs of walking and climbing stairs, and further quantified walking to infer step count and gait speed.\n\nResultsWe validated these algorithms in 75 individuals, in both laboratory (treadmill) and naturalistic settings. Our algorithm performance was quite satisfactory, with accuracies of 92-99% for all behavioral categories, and 87-90% for gait metrics in naturalistic settings.\n\nConclusionsWe conclude that smartphones are valid and accurate platforms for measuring day-to-day physical activity in ambulatory, community dwelling individuals.

bioengineering↗