Biology subjects

Attinger, A.

Publications and source records attributed to Attinger, A..

4 recordsLinked to original sources

bioRxiv2025

Environmental Novelty Modulates Rapid Cortical Plasticity During Navigation

In novel environments, animals quickly learn to navigate, and position-correlated spatial representations rapidly emerge in both the retrosplenial cortex (RSC) and primary visual cortex (V1). However, the role of plasticity in building these spatial representations, and how experience modulates this process, are not well understood. Here, we investigated the plasticity of spatial representations with real-time, cellular-resolution read and write control of neural activity using two-photon calcium imaging combined with holographic optogenetic stimulation in mice navigating virtual reality environments. Targeted stimulation of individual layer 2/3 neurons rapidly biased neural activity towards stimulation-paired locations in novel, but not familiar, environments. In contrast, RSC layer 5 neurons exhibited stimulation-induced plasticity regardless of environmental familiarity. These findings reveal a layer-specific, experience-dependent modulation of plasticity and offer a framework for how neocortical spatial representations strike a balance between stability of familiar environments with flexibility for continuous updates of relevant context information. HighlightsO_LIAll optical read-write approach reveals experience-dependent plasticity in cortex C_LIO_LIStimulating layer 2/3 neurons biases activity in novel but not familiar environments C_LIO_LILayer 5 neurons exhibit plasticity regardless of environmental familiarity C_LI

neuroscience↗

bioRxiv2025

Large-scale cellular-resolution read/write of activity enables discovery of cell types defined by complex circuit properties

The complexity of the mammalian brains vast population of interconnected neurons poses a formidable challenge to elucidate its underlying mechanisms of coordination and computation. A key step forward will be technologies that can perform large-scale, cellular-resolution monitoring and interrogation of distributed brain circuit activity in behaving animals. Here, we present an all-optical strategy for precise optogenetic activity control of [~]103 neurons and simultaneous activity monitoring of [~]104 neurons within and across areas of mouse cortex--an order-of-magnitude leap beyond previous capabilities. Tracking population responses following delivery of precisely-defined widely-distributed activity patterns to the visual cortex of awake mice, we were surprised to identify neurons robustly responsive to stimulation of diverse ensembles, defying conventional like-to-like wiring rules. These cells were primarily deep L2/3 somatostatin-positive (SST) interneurons with functional properties distinct from other SST neurons, and appeared to play a role in brain dynamics that could only have been identified through broad cellular-resolution circuit interrogation. Our work reveals the value of measuring large-scale circuit-dynamical properties of functionally-resolved single cells, beyond genetic and anatomical classification, to define and explore the roles of cell types in brain function.

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↗

bioRxiv2020

Distinct algorithms for combining landmarks and path integration in medial entorhinal, visual andretrosplenial cortex

During navigation, animals estimate their position using path integration and landmarks, engaging many brain areas. Whether these areas follow specialized or universal cue integration principles remains unknown. Here, we combined electrophysiology with virtual reality to quantify cue integration across thousands of neurons in three areas that support navigation: primary visual (V1), retrosplenial (RSC) and medial entorhinal cortex (MEC). Path integration influenced position estimates in MEC more than in V1 and RSC. V1 coded position retrospectively, likely reflecting delays in sensory processing, whereas MEC coded position prospectively, and RSC was intermediate between the two. In combining path integration with landmarks, MEC showed signatures of Kalman filtering, and we report a distance-tuned neural population that could implement such filtering through attractor dynamics. Our results show that during navigation, MEC serves as a specialized cortical hub for reconciling path integration and landmarks to estimate position and suggest an algorithm for calculating these estimates.

neuroscience↗