Biology subjects

Alexander, A. S.

Publications and source records attributed to Alexander, A. S..

4 recordsLinked to original sources

bioRxiv2026

Predictive pursuit emerges in high-dimensional recurrent neural networks

Tracking dynamic moving objects in the external world is ethologically important for many organisms. Recent experiments have examined neural dynamics supporting such behaviors by employing visually-guided pursuit in freely moving rodents, yet computational principles underlying this cognitive process are not well understood. To address this, we developed a recurrent neural network model for examining the predictive behaviors and computations that emerge during pursuit. We demonstrate that the model generates internal predictions of the targets future locations, with anticipatory behaviors increasing with exposure to stereotyped trajectories of the target. These internal predictions can be used by the model to pursue a target in a complex environment, and the models emergent strategy is aligned with behavior when tested in rodents. In investigating the computations that underlie the models ability to perform predictive pursuit, we found units sensitive to the position of the target relative to the artificial agent, a representation analogous to egocentric target neurons observed in animals performing pursuit tasks. Ablating these units significantly reduced model performance, establishing a causal role of this functional response type in efficient pursuit. Given the complexity of the task and agent behavior, we hypothesized that RNN models may use high-dimensional neural codes to support predictive pursuit. To test this, we trained models of varying rank and found that anticipatory behavior emerged only when the rank was sufficiently high, despite strong pursuit performance in lower rank models. All RNNs encoded the egocentric location of the target, whereas allocentric self and target locations emerged only in high-dimensional networks. Overall, our results suggest that, unlike commonly studied vision, motor, or memory tasks, predictive pursuit emerges in high-dimensional networks with sufficient resources.

neuroscience↗

bioRxiv2022

Learning the Vector Coding of Egocentric Boundary Cells from Visual Data

The use of spatial maps to navigate through the world requires a complex ongoing transformation of egocentric views of the environment into position within the allocentric map. Recent research has discovered neurons in retrosplenial cortex and other structures that could mediate the transformation from egocentric views to allocentric views. These egocentric boundary cells respond to the egocentric direction and distance of barriers relative to an animals point of view. This egocentric coding based on the visual features of barriers would seem to require complex dynamics of cortical interactions. However, computational models presented here show that egocentric boundary cells can be generated with a remarkably simple synaptic learning rule that forms a sparse representation of visual input as an animal explores the environment. Simulation of this simple sparse synaptic modification generates a population of egocentric boundary cells with distributions of direction and distance coding that strikingly resemble those observed within the retrosplenial cortex. This provides a framework for understanding the properties of neuronal populations in the retrosplenial cortex that may be essential for interfacing egocentric sensory information with allocentric spatial maps of the world formed by neurons in downstream areas including the grid cells in entorhinal cortex and place cells in the hippocampus.

neuroscience↗

bioRxiv2020

Adaptive integration of self-motion and goals in posterior parietal cortex

Animals engage in a variety of navigational behaviors that require different regimes of behavioral control. In the wild, rats readily switch between foraging and more complex behaviors such as chase, wherein they pursue other rats or small prey. These tasks require vastly different tracking of multiple behaviorally-significant variables including self-motion state. It is unknown whether changes in navigational context flexibly modulate the encoding of these variables. To explore this possibility, we compared self-motion processing in the multisensory posterior parietal cortex while rats performed alternating blocks of free foraging and visual target pursuit. Animals performed the pursuit task and demonstrated predictive processing by anticipating target trajectories and intercepting them. Relative to free exploration, pursuit sessions yielded greater proportions of parietal cortex neurons with reliable sensitivity to self-motion. Multiplicative gain modulation was observed during pursuit which increased the dynamic range of tuning and led to enhanced decoding accuracy of self-motion state. We found that self-motion sensitivity in parietal cortex was history-dependent regardless of behavioral context but that the temporal window of self-motion tracking was extended during target pursuit. Finally, many self-motion sensitive neurons conjunctively tracked the position of the visual target relative to the animal in egocentric coordinates, thus providing a potential coding mechanism for the observed gain changes to self-motion signals. We conclude that posterior parietal cortex dynamically integrates behaviorally-relevant information in response to ongoing task demands.

neuroscience↗

bioRxiv2017

Spatially periodic activation patterns of retrosplenial cortex encode route sub-spaces and distance travelled

Traversal of a complicated route is often facilitated by considering it as a set of related sub-spaces. Such compartmentalization processes could occur within retrosplenial cortex, a structure whose neurons simultaneously encode position within routes and other spatial coordinate systems. Here, retrosplenial cortex neurons were recorded as rats traversed a track having recurrent structure at multiple scales. Consistent with a major role in compartmentalization of complex routes, individual RSC neurons exhibited periodic activation patterns that repeated across route segments having the same shape. Concurrently, a larger population of RSC neurons exhibited single-cycle periodicity over the full route, effectively defining a framework for encoding of sub-route positions relative to the whole. The same population simultaneously provides a novel metric for distance from each route position to all others. Together, the findings implicate retrosplenial cortex in the extraction of path sub-spaces, the encoding of their spatial relationships to each other, and path integration.

neuroscience↗