Biology subjects

Dunn, B. A.

Publications and source records attributed to Dunn, B. A..

10 recordsLinked to original sources

bioRxiv2026

Toroidal topology of grid-cell activity precedes spatial navigation during development

The medial entorhinal cortex (MEC) is a central component of the mammalian navigation system1-5, in which spatially and directionally tuned neurons, including grid cells and head-direction cells, encode an animals position and orientation6-8. These cells form internal maps with periodic topologies: head-direction cells traverse ring-like manifolds9,10, while grid cells are organized on toroidal manifolds11. The persistence of these topologies across behavioral states and environments11,12 raises the possibility that they arise intrinsically from network architecture rather than through sensory experience4,5,13-17. Consistent with this view, observations in juvenile rats have shown that rudimentary spatial tuning appears in place cells, head direction cells and grid cells almost as soon as pups begin to explore their surroundings at 2-3 weeks of age18-20. However, it remains unclear whether spatial experience is required for the initial emergence of positional tuning and for the organization of tuned cells into periodic maps. Here we show, using large-scale ensemble recordings in rat pups, that toroidal manifolds emerge in MEC subnetworks as early as postnatal day 10 (P10), preceding eye and ear opening, upright posture, quadrupedal gait, and active exploration21,22. These toroidal networks were modular from the beginning, with increasing differentiation of their dynamics appearing on P11-12. The onset of toroidal topology coincided with a transition in MEC network activity characterized by desynchronization and increased inhibitory connectivity, a developmental shift observed broadly across cortical regions at this age23,24. In contrast, ring-like manifolds were already detectable by P9, with traces of directional tuning appearing in individual cells at P8 -- consistent with an earlier maturation of subcortical circuitry25. As pups subsequently began to explore the environment around P15-16, these internally generated maps progressively aligned with external landmarks, culminating in stable, periodic firing fields by three weeks of age. Taken together, these findings identify ring-like and toroidal manifolds as instinctive computational motifs of the developing brain. Their early emergence, preceding major sensory input and navigation, supports the view that spatial representations are preconfigured and later anchored to the external world through experience-dependent plasticity.

neuroscience↗

bioRxiv2026

Active pursuit gates egocentric coding in Retrosplenial Cortex

Spatial navigation is commonly studied in static environments, but adaptive behavior frequently hinges on tracking moving goals in real time. Active pursuit exemplifies this challenge: it is an inherently egocentric spatial behavior requiring continuous localization of a moving target, yet the neural coding schemes supporting it remain poorly understood. We therefore performed Neuropixels recordings in the retrosplenial cortex (RSC), a hub for egocentric-to-allocentric reference frame transformations, during naturalistic bait-chasing. We identified head-centered target-encoding neurons that were functionally distinct from cells encoding environmental boundaries or static objects. Whereas egocentric boundary encoding was task-invariant, target-coding cells were specific to chasing and exhibited task-dependent retuning, characterized by enhanced ego-centric representation and reduced allocentric head-direction signaling during pursuit. Together, these findings demonstrate that classical egocentric and allocentric codes coexist with novel target tuning in the RSC, where target-coding neurons dynamically reweight their reference frames according to behavioral demands during pursuit.

neuroscience↗

bioRxiv2024

Prefrontal cortex encodes behavior states decoupled from movement

Prefrontal cortex is often viewed as an extension of the motor system, but little is understood of how it relates to natural motor behavior. We therefore tracked the kinematics of freely moving rats performing minimally structured tasks and measured which aspects of behavior were read out in prefrontal neural populations. Naturalistic behaviors such as rearing or chasing a bait were each encoded by unique neural ensembles, but the behavioral representations were not anchored to posture or movement. Rather, the coding of kinematic features depended on their relevance to the animals current behavior or which task the animal performed. Behavior-specific ensembles often preceded and outlasted physical actions and, accordingly, prefrontal population activity evolved at slower timescales than in motor cortex. These findings argue that prefrontal coding of behavior is not locked to motor output, and may instead reflect motivations to perform certain actions rather than the actions themselves. HighlightsO_LIPrefrontal neural ensembles uniquely encode different naturalistic actions C_LIO_LIBehavioral tuning is not explained by movement kinematics C_LIO_LIPopulation activity in prefrontal cortex evolves slower than in M1 C_LIO_LISingle-cell coding of behavior varies across tasks yet ensemble coding is stable C_LI

neuroscience↗

bioRxiv2023

Investigating egocentric tuning in hippocampal CA1 neurons using simulated data

Navigation requires integrating sensory information with a stable schema to create a dynamic map of an animals position using egocentric and allocentric coordinate systems. In the hippocampus, place cells encode allocentric space, but their firing rates may also exhibit directional tuning within egocentric or allocentric reference frames. We compared experimental and simulated data to assess the prevalence of tuning to egocentric bearing (EB) among hippocampal cells in rats foraging in an open field. Using established procedures, we confirmed egocentric modulation of place cell activity in recorded data; however, simulated data revealed a high false positive rate. When we accounted for false positives by comparing with shuffled data that retain correlations between the animals direction and position, only a very low number of hippocampal neurons appeared modulated by EB. Our study highlights biases affecting false positive rates and provides insights into the challenges of identifying egocentric modulation in hippocampal neurons.

neuroscience↗

bioRxiv2023

Bayesian inference of spike-time dependent learning rules from single neuron recordings in humans

Spike-timing dependent plasticity (STDP) learning rules are popular in both neuroscience and artificial neural networks due to their ability to capture the change in neural connections arising from the correlated activity of neurons. Recent technological advances have made large neural recordings common, substantially increasing the probability that two connected neurons are simultaneously observed, which we can use to infer functional connectivity and associated learning rules. We use a Bayesian framework and assume neural spike recordings follow a binary data model to infer the connections and their evolution over time from data using STDP rules. We test the resulting method on simulated and real data, where the real case study consists of human electrophysiological recordings. The simulated case study allows validation of the model, and the real case study shows that we are able to infer learning rules from awake human data.

neuroscience↗

bioRxiv2023

A novel paradigm for operant social learning in rats

The ability to learn by observing the behavior of others is both energy efficient and brings high survival value, making it an important learning tool for many species in the animal kingdom. As such, several forms of observational learning have been documented in a myriad of species. In the laboratory, rodents have proven useful models for studying different forms of observational learning, however, the most robust learning paradigms typically rely on aversive stimuli, like foot shocks, to drive the social acquisition of fear. Non-fear-based tasks have also been developed, but these rarely succeed in having observer animals perform a new behavior de novo. Consequently, much less is known regarding the cellular mechanisms supporting non-fear-based types of learning, such as visuomotor skill acquisition. To address this we developed a reward-based social learning paradigm in adult rats, in which observer animals learn to tap lit spheres in a specific sequence by watching skilled demonstrators, with successful trials leading to rewarding intracranial stimulation in both observers and performers. Following three days of observation and a 24-hour delay, observer animals outperformed control animals on several metrics of task performance and efficiency, with a subset of observers demonstrating correct performance immediately when tested. This paradigm thus introduces a novel tool to investigate the neural circuits supporting observational learning and memory for visuomotor behavior, a phenomenon about which little is understood, particularly in rodents.

animal behavior and cognition↗

bioRxiv2022

Uncovering 2-D toroidal representations in grid cell ensemble activity during 1-D behavior

Neuroscience is pushing toward studying the brain during naturalistic behaviors with open-ended tasks. Grid cells are a classic example, where free behavior was key to observing their characteristic spatial representations in two-dimensional environments [1]. In contrast, it has been difficult to identify grid cells and study their computations in more restrictive experiments, such as head-fixed wheel running [2-6]. Here, we challenge this view by showing that shifting the focus from single neurons to the population level changes the minimal experimental complexity required to study grid cell representations. Specifically, we combine the manifold approximation in UMAP [7] with persistent homology [8] to study the topology of the population activity. With these methods, we show that the population activity of grid cells covers a similar two-dimensional toroidal state space during wheel running as in open field foraging [9, 10], with and without a virtual reality setup. Trajectories on the torus correspond to single trial runs in virtual reality and changes in experimental conditions are reflected in the internal representation, while the toroidal representation undergoes occasional shifts in its alignment to the environment. These findings show that our method can uncover latent topologies that go beyond the complexity of the task, allowing us to investigate internal dynamics in simple experimental settings in which the analysis of grid cells has so far remained elusive.

neuroscience↗

bioRxiv2022

Behavioral decomposition reveals rich encoding structure employed across neocortex

AO_SCPLOWBSTRACTC_SCPLOWThe Cortical population code is pervaded by activity patterns evoked by movement, but how such signals relate to the natural actions of unrestrained animals remains largely unknown, particularly in sensory areas. To address this we compared high-density neural recordings across four cortical regions (visual, auditory, somatosensory, motor) in relation to sensory modulation, posture, movement, and ethograms of freely foraging rats. Momentary actions, such as rearing or turning, were represented ubiquitously and could be decoded from all sampled structures. However, more elementary and continuous features, such as pose and movement, followed region-specific organization, with neurons in visual and auditory cortices preferentially encoding mutually distinct head-orienting features in world-referenced coordinates, and somatosensory and motor cortices principally encoding the trunk and head in egocentric coordinates. The tuning properties of synaptically coupled cells also exhibited connection patterns suggestive of different uses of behavioral information within and between regions. Together, our results speak for detailed, multi-level encoding of animal behavior subserving its dynamic employment in local cortical computations.

neuroscience↗

bioRxiv2019

Action representation in the mouse parieto-frontal network

The posterior parietal cortex (PPC), along with anatomically linked frontal areas, form a cortical network which mediates several functions that support goal-directed behavior, including sensorimotor transformations and decision making. In primates, this network also links performed and observed actions via mirror neurons, which fire both when an individual performs an action and when they observe the same action performed by a conspecific. Mirror neurons are thought to be important for social learning and imitation, but it is not known whether mirror-like neurons occur in similar networks in other species that can learn socially, such as rodents. We therefore imaged Ca2+ responses in large neural ensembles in PPC and secondary motor cortex (M2) while mice performed and observed several actions in pellet reaching and wheel running tasks. In all animals, we found spatially overlapping neural ensembles in PPC and M2 that robustly encoded a variety of naturalistic behaviors, and that subsets of cells could stably encode multiple actions. However, neural responses to the same set of observed actions were absent in both brain areas, and across animals. Statistical modeling analyses also showed that performed actions, especially those that were task-specific, outperformed observed actions in predicting neural responses. Overall, these findings show that performed and observed actions do not drive the same cells in the parieto-frontal network in mice, and suggest that sensorimotor mirroring in the mammalian cortex may have evolved more recently, and only in certain species.

neuroscience↗

bioRxiv2018

Efficient cortical coding of 3D posture in freely behaving rats

In order to meet physical and behavioral demands of their environments animals constantly update their body posture, but little is known about the neural signals on which this ability depends. To better understand the role of cortex in coordinating natural pose and movement, we tracked the heads and backs of freely foraging rats in 3D while recording simultaneously from posterior parietal cortex (PPC) and frontal motor cortex (M2), areas critical for spatial movement planning and navigation. Single units in both regions were tuned mainly to postural features of the head, back and neck, and much less so to their movement. Representations of the head and back were organized topographically across PPC and M2, and the tuning peaks of the cells were distributed in an efficient manner, where substantially fewer cells encoded postures that occurred more often. Postural signals in both areas were sufficiently robust to allow reconstruction of ongoing behavior with 90% accuracy. Together, these findings demonstrate that both parietal and frontal motor cortices maintain an efficient, organized representation of 3D posture during unrestrained behavior.

neuroscience↗