Biology subjects

Sorscher, B.

Publications and source records attributed to Sorscher, B..

5 recordsLinked to original sources

bioRxiv2023

One-shot entorhinal maps enable flexible navigation in novel environments

Animals face the substantial challenge of navigating novel environments to find food, shelter, or mates. In mammals, hexagonal grid cells in the medial entorhinal cortex create a map-like population representation of the external environment 1-7. However, it remains unclear how the grid map can adapt to novel environmental features on a rapid, behaviorally relevant time scale. By recording over fifteen thousand grid cells in mice navigating virtual environments, we found grid cell activity was weakly anchored to landmark inputs through a fixed circuit relationship. A computational model based on this fixed circuit assumption accurately predicted grid spatial patterns in environments with novel landmark rearrangements. Finally, a medial entorhinal cortex-dependent task revealed that while grid cell firing patterns remain anchored to landmarks, behavior can adapt to changes in landmark location via a downstream region implementing behavioral time scale synaptic plasticity 8. This fixed but weak anchoring of grid cells to landmarks endows the grid map with powerful computational properties. The fixed nature allows the generation of rapid stable maps for novel environments after a single exposure. The weak nature allows these rapidly formed maps to incur only small distortions between distances traveled in real versus neural space. Overall, such rapid low distortion mapping can then mediate accurate navigational behavior in rapidly changing environments through downstream plasticity.

neuroscience↗

bioRxiv2022

When and why grid cells appear or not in trained path integrators

Recent work has claimed that the emergence of grid cells from trained path-integrator circuits is a more fragile phenomenon than previously reported. In this note we critically assess the main analysis and simulation results underlying this claim, within the proper context of previously published theoretical work. Our assessment reveals that the emergence of grid cells is entirely consistent with this prior theory: hexagonal grid cells robustly emerge precisely when prior theory predicts they should, and dont when prior theory predicts they should not.

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↗

bioRxiv2021

The Geometry of Concept Learning

Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and computationally powerful neural mechanism for few-shot learning of naturalistic concepts. We posit that the concepts that can be learnt from few examples are defined by tightly circumscribed manifolds in the neural firing rate space of higher order sensory areas. We further posit that a single plastic downstream readout neuron learns to discriminate new concepts based on few examples using a simple plasticity rule. We demonstrate the computational power of our proposal by showing it can achieve high few-shot learning accuracy on natural visual concepts using both macaque inferotemporal cortex representations and deep neural network models of these representations, and can even learn novel visual concepts specified only through linguistic descriptors. Moreover, we develop a mathematical theory of few-shot learning that links neurophysiology to behavior by delineating several fundamental and measurable geometric properties of high-dimensional neural representations that can accurately predict the few-shot learning performance of naturalistic concepts across all our numerical simulations. We discuss testable predictions of our theory for psychophysics and neurophysiological experiments.

neuroscience↗

bioRxiv2020

A unified theory for the computational and mechanistic origins of grid cells

The discovery of entorhinal grid cells has generated considerable interest in how and why hexagonal firing fields might mechanistically emerge in a generic manner from neural circuits, and what their computational significance might be. Here we forge an intimate link between the computational problem of path-integration and the existence of hexagonal grids, by demonstrating that such grids arise generically in biologically plausible neural networks trained to path integrate. Moreover, we develop a unifying theory for why hexagonal grids are so ubiquitous in path-integrator circuits. Such trained networks also yield powerful mechanistic hypotheses, exhibiting realistic levels of biological variability not captured by hand-designed models. We furthermore develop methods to analyze the connectome and activity maps of our trained networks to elucidate fundamental mechanisms underlying path integration. These methods provide an instructive roadmap to go from connectomic and physiological measurements to conceptual understanding in a manner that might be generalizable to other settings.

neuroscience↗