Biology subjects

Mallory, C. S.

Publications and source records attributed to Mallory, C. S..

4 recordsLinked to original sources

bioRxiv2026

Spatial goals trap hippocampal replay

Hippocampal replay is proposed to support memory and planning by preferentially reactivating behaviorally significant locations. Although learning can alter which locations are reactivated and how frequently, whether it also alters the dynamics by which replay propagates through the cognitive map remains unknown. To address this, we recorded large-scale CA1 activity while rats collected identical rewards at multiple spatial locations, only one of which was consistently rewarded and served as the Goal. Replay events occurred nearly twice as often during reward consumption at the Goal compared to Non-Goal sites and were accompanied by reduced inhibition. Goal-associated replays were also slower, shorter, and more locally confined. Replays originating elsewhere in the arena slowed and preferentially terminated when nearing the Goal, indicating that the Goal locally resists replay propagation regardless of where the replay was initiated. Increasing evidence suggests that hippocampal sequences arise from adaptation-like mechanisms that propagate neural activity away from recently active states. We hypothesized that the slowing and spatial confinement of Goal-associated replay arises from reduced effective adaptation near the Goal. A recurrent network model showed that reducing adaptation strength reproduces these effects and correctly predicted increased population firing rates and weaker avoidance of recently traveled or reactivated paths. Finally, we found evidence for reduced interneuron-mediated feedback inhibition near the Goal, providing a potential circuit mechanism for locally modulating adaptation. Our findings show that learning locally reshapes the dynamics governing internally generated activity, causing replay to linger near behaviorally significant locations and providing a new mechanism for their preferential reactivation.

neuroscience↗

bioRxiv2024

Self-avoidance dominates the selection of hippocampal replay

Spontaneous neural activity sequences are generated by the brain in the absence of external input1-12, yet how they are produced remains unknown. During immobility, hippocampal replay sequences depict spatial paths related to the animals past experience or predicted future13. By recording from large ensembles of hippocampal place cells14 in combination with optogenetic manipulation of cortical input in freely behaving rats, we show here that the selection of hippocampal replay is governed by a novel self-avoidance principle. Following movement cessation, replay of the animals past path is strongly avoided, while replay of the future path predominates. Moreover, when the past and future paths overlap, early replays avoid both and depict entirely different trajectories. Further, replays avoid self-repetition, on a shorter timescale compared to the avoidance of previous behavioral trajectories. Eventually, several seconds into the stopping period, replay of the past trajectory dominates. This temporal organization contrasts with established and recent predictions9,10,15,16 but is well-recapitulated by a symmetry-breaking attractor model of sequence generation in which individual neurons adapt their firing rates over time26-35. However, while the model is sufficient to produce avoidance of recently traversed or reactivated paths, it requires an additional excitatory input into recently activated cells to produce the later window of past-dominance. We performed optogenetic perturbations to demonstrate that this input is provided by medial entorhinal cortex, revealing its role in maintaining a memory of past experience that biases hippocampal replay. Together, these data provide specific evidence for how hippocampal replays are generated.

neuroscience↗

bioRxiv2021

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks

Medial entorhinal cortex (MEC) supports a wide range of navigational and memory related behaviors. Well-known experimental results have revealed specialized cell types in MEC -- e.g. grid, border, and head-direction cells -- whose highly stereotypical response profiles are suggestive of the role they might play in supporting MEC functionality. However, the majority of MEC neurons do not exhibit stereotypical firing patterns. How should the response profiles of these more "heterogeneous" cells be described, and how do they contribute to behavior? In this work, we took a computational approach to addressing these questions. We first performed a statistical analysis that shows that heterogeneous MEC cells are just as reliable in their response patterns as the more stereotypical cell types, suggesting that they have a coherent functional role. Next, we evaluated a spectrum of candidate models in terms of their ability to describe the response profiles of both stereotypical and heterogeneous MEC cells. We found that recently developed task-optimized neural network models are substantially better than traditional grid cell-centric models at matching most MEC neuronal response profiles -- including those of grid cells themselves -- despite not being explicitly trained for this purpose. Specific choices of network architecture (such as gated nonlinearities and an explicit intermediate place cell representation) have an important effect on the ability of the model to generalize to novel scenarios, with the best of these models closely approaching the noise ceiling of the data itself. We then performed in silico experiments on this model to address questions involving the relative functional relevance of various cell types, finding that heterogeneous cells are likely to be just as involved in downstream functional outcomes (such as path integration) as grid and border cells. Finally, inspired by recent data showing that, going beyond their spatial response selectivity, MEC cells are also responsive to non-spatial rewards, we introduce a new MEC model that performs reward-modulated path integration. We find that this unified model matches neural recordings across all variable-reward conditions. Taken together, our results point toward a conceptually principled goal-driven modeling approach for moving future experimental and computational efforts beyond overly-simplistic single-cell stereotypes.

neuroscience↗

bioRxiv2019

Entorhinal velocity signals reflect environmental geometry

The entorhinal cortex contains neural signals for representing self-location, including grid cells that fire in periodic locations and velocity signals that encode an animals speed and head direction. Recent work revealed that the size and shape of the environment influences grid patterns. Whether entorhinal velocity signals are equally influenced or provide a universal metric for self-motion across environments remains unknown. Here, we report that changes to the size and shape of the environment result in re-scaling in entorhinal speed codes. Moreover, head direction cells re-organize in an experience-dependent manner to align with the axis of environmental change. A knockout mouse model allows a dissociation of the coordination between cell types, with grid and speed, but not head direction, cells responding in concert to environmental change. These results align with predictions of grid cell attractor models and point to inherent flexibility in the coding features of multiple functionally-defined entorhinal cell types.

neuroscience↗