Biology subjects

Redman, W. T.

Publications and source records attributed to Redman, W. T..

7 recordsLinked to original sources

bioRxiv2026

Predictive Grid Cell Coding Does Not Require Explicit Planning

Predictive grid cells, a recently discovered functional class of neurons in medial entorhinal cortex (MEC), exhibit grid-like tuning, not with respect to where an animal currently is, but where it will be. Such coding, which is disjoint from standard grid cell coding, appears consistent with the hypothesis that MEC supports predictive computations for goal-directed navigation. However, given that existing predictive coding theories have been used to understand standard grid cells, it remains unclear what the functional significance of predictive grid cells is. Furthermore, the extent to which predictive grid cells are generally present in MEC is unknown. To address these open questions, we analyze data from rodent electrophysiology recordings and recurrent neural networks (RNNs). In an independent, previously recorded MEC dataset, we find evidence of predictive grid cell coding when the animal is awake and exploring an open field environment, suggesting that they are a general feature of rodent MEC. In RNNs trained to path-integrate, which have previously been shown to develop units with grid-like representations, we also find units with predictive grid-like representations, even though the RNNs are not trained with a predictive objective. We validate the predictive grid tuning of these units by finding that they exhibit significant future path-dependence, even when controlling for the same allocentric spatial position. In addition, we find that band-like units, which have been found to play an important role in maintaining path-integration in the RNNs, exhibit greater predictive grid tuning than expected by chance. These results lead us to hypothesize that predictive grid cells are a property of the underlying grid cell circuit. We test this by looking for signatures of predictive grid coding in the MEC electrophysiological data collected during sleep, finding evidence in one recording. Collectively, our results challenge the idea that predictive grid cells must necessarily be used for explicit prediction and planning, and instead may be involved in shaping the population level activity of grid cells.

neuroscience↗

bioRxiv2026

A metric for comparing complex systems by their dynamics

Comparisons are fundamental to science: experiment against model, one organism against another, a system against itself across time. Because many systems, from brains to climate, are characterized by how they evolve in time, it is a natural goal to compare their dynamics. Dynamical systems comparison is well defined, but has been intractable for nonlinear, high-dimensional, noisy, and partially observed data. As a result, standard comparison methods have focused on the geometry or topology of data. Here we present Dynamical Similarity Analysis (DSA), a class of metrics to compare systems by their temporal evolution. Its foundation is Koopman Operator theory, which recasts nonlinear systems as linear operators. We estimate these operators from data, then compare the operators across systems. The computation is fast, scalable, and robust to noise and partial observation. It is also differentiable. DSA identifies dynamical structure that geometric and topological methods miss. It matches recordings from the head direction circuit to ring attractor models. It shows that macaque motor cortex dynamics for two reaching tasks drift apart across years despite preserved behavior, and that primary motor cortex breaks from premotor cortex as movement begins. As an optimization objective, it induces neural networks to learn never-before hypothesized solutions that run counter to their inductive biases. Thus, DSA transforms the dynamics of a system into an object that can be measured, compared, and optimized.

neuroscience↗

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↗

bioRxiv2024

The estrous cycle modulates hippocampal spine dynamics, dendritic processing, and spatial coding

AbstractHistological evidence suggests that the estrous cycle exerts a powerful effect on CA1 neurons in mammalian hippocampus. Decades have passed since this landmark observation, yet how the estrous cycle shapes dendritic spine dynamics and hippocampal spatial coding in vivo remains a mystery. Here, we used a custom hippocampal microperiscope and two-photon calcium imaging to track CA1 pyramidal neurons in female mice over multiple cycles. Estrous cycle stage had a potent effect on spine dynamics, with heightened density during periods of greater estradiol (proestrus). These morphological changes were accompanied by greater somatodendritic coupling and increased infiltration of back-propagating action potentials into the apical dendrite. Finally, tracking CA1 response properties during navigation revealed enhanced place field stability during proestrus, evident at the single-cell and population level. These results establish the estrous cycle as a driver of large-scale structural and functional plasticity in hippocampal circuits essential for learning and memory.

neuroscience↗

bioRxiv2024

Robust variability of grid cell properties within individual grid modules enhances encoding of local space

Although grid cells are one of the most well studied functional classes of neurons in the mammalian brain, whether there is a single orientation and spacing value per grid module has not been carefully tested. We analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the encoding of local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that grid property variability of a similar magnitude to the analyzed data leads to significantly decreased decoding error. This holds even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

neuroscience↗

bioRxiv2021

Long-term Transverse Imaging of the Hippocampus with Glass Microperiscopes

The hippocampus consists of a stereotyped neuronal circuit repeated along the septal-temporal axis. This transverse circuit contains distinct subfields with stereotyped connectivity that support crucial cognitive processes, including episodic and spatial memory. However, comprehensive measurements across the transverse hippocampal circuit in vivo are intractable with existing techniques. Here, we developed an approach for two-photon imaging of the transverse hippocampal plane in awake mice via implanted glass microperiscopes, allowing optical access to the major hippocampal subfields and to the dendritic arbor of pyramidal neurons. Using this approach, we tracked dendritic morphological dynamics on CA1 apical dendrites and characterized spine turnover. We then used calcium imaging to quantify the prevalence of place and speed cells across subfields. Finally, we measured the anatomical distribution of spatial information, finding a non-uniform distribution of spatial selectivity along the DG-to-CA1 axis. This approach extends the existing toolbox for structural and functional measurements of hippocampal circuitry.

neuroscience↗

bioRxiv2021

A neuronal code for space in hippocampal coactivity dynamics independent of place fields

Hippocampus is comprised of [~]20% place cells, discharging in cell-specific locations ("place fields"), standardly interpreted as a dedicated neuronal code for space. However, place cell discharge is temporally unreliable across seconds and days, and place fields are multimodal, suggesting an alternative "ensemble cofiring" spatial code with manifold dynamics that does not require reliable spatial tuning. We evaluated these hypotheses using GCaMP6f and miniature microscopes to image mouse CA1 ensemble activity in two environments, across 3 weeks. Both place fields and ensemble coactivity relationships appear to "remap," being distinct between, and (weakly) similar within environments. Decoding location as well as environment from 1-s ensemble location-specific discharge is effective and improves with experience. Decoding the environment (but not location) from cell-pair coactivity relationships is also effective and improves with experience, even after removing place tuning. Discriminating environments from 1-s ensemble coactivity relies crucially on the cells with the most anti-cofiring cell-pair relationships because ensemble activity is internally-organized on a low-dimensional manifold of non-linear cofiring relationships that intermittently reregisters to environments according to the anti-cofiring subpopulation activity.

neuroscience↗