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Schottdorf, M.

Publications and source records attributed to Schottdorf, M..

6 recordsLinked to original sources

The geometry of knowledge in the hippocampal-prefrontal system

Decision making is associated with frontal brain circuits and spatial navigation with the hippocampus. In addition, recent work in spatial decision making tasks found single neurons in both areas encoding space conjunctively with other task-relevant variables. However, circuit function is not determined by tuning alone, but also by representational geometry, i.e. the representation of task-relevant variables in neural state space. Here, using Neuropixel recordings in a complex spatial decision making task combined with nonlinear dimensionality reduction, we show an intrinsically low-dimensional neural manifold in medial prefrontal cortex (mPFC) on which key task variables were represented as smooth gradients. This geometry resembled the hippocampal (HPC) map. The mPFC and HPC manifolds from one mouse can predict the behavior across other mice and brain areas. A non-linear representational map between the mPFC and HPC manifolds demonstrates alignment in time. Our work suggests that the representational geometry in HPC and mPFC is distributed and time-aligned using low-dimensional neural codes.

neuroscience↗

Working memory expands shared task representations in cortex

Cognition is thought to emerge from the flexible organization of neural activity, yet how this organization reconfigures across behaviors varying in cognitive load remains unclear. We investigated how the structure of working-memory representations in the cortex compares to task representations that do not involve working memory. We used a task-switching paradigm in virtual reality, where mice alternated between a navigation-based working-memory task and a simpler task with matched sensorimotor demands. During behavior, we simultaneously imaged three cortical areas: higher visual area AM, and two association areas--premotor (M2) and retrosplenial cortex. At the single-neuron level, trial-averaged activity appeared similar across tasks. However, pairwise correlations decreased during the working-memory task, particularly in association areas. In addition, the corresponding linear task subspace explained the variance of both tasks equally well, whereas the simpler task subspace failed to do so, suggesting an asymmetric relationship between them. Nonlinear dimensionality reduction revealed a shared low-dimensional structure across tasks. Yet, the organization of neuronal firing fields along this shared structure accounted for the difference in pairwise correlations: in the working-memory task, firing fields were more disjoint, especially among neurons in association areas that formed sequences along the memory dimension. Moreover, the degree of overlap between these firing fields predicted the mices behavioral reliance on working memory. We conclude that behaviors varying in cognitive demands are supported by a single low-dimensional neural structure, which can expand or contract depending on cognitive load. We thus provide a framework for how task representations across the cortex reconfigure to support cognitive processes.

neuroscience↗

Canonical Representational Mapping for Cognitive Neuroscience

Understanding neural representations is central to cognitive neuroscience, yet isolating meaningful patterns from noisy or correlated data remains challenging. Canonical Representational Mapping (CRM) is a novel multivariate analysis method to identify neural patterns aligned with specific cognitive hypotheses. CRM maximizes correlations between multivariate datasets - similar to Canonical Correlation Analysis - while controlling for confounding sources of variance, such as shared noise or irrelevant task conditions. We validate CRM with simulations and apply it across diverse neurophysiological datasets: In one application, we use CRM to map large language model activations onto intracranial electroencephalography recordings during story listening, controlling for contextual autocorrelations. In another example, we factorize overlapping representations in functional Magnetic Resonance Imaging into distinct context and episode components. Finally, we uncover frequency-coupled representations shared between hippocampus and medial prefrontal cortex in navigating rodents. Our results introduce CRM as a powerful tool to isolate representations across modalities and species.

neuroscience↗

Striatal pathways oppositely shift cortical activity along the decision axis

The cortex and basal ganglia are organized into multiple parallel loops that serve motor, limbic, and cognitive functions. The classic model of cortico-basal ganglia interactions posits that within each loop, the direct pathway of the basal ganglia activates the cortex and the indirect pathway inhibits it1-3. While this model has found support in the motor domain4,5, whether opponent control by the two pathways extends to the cognitive domain remains unknown. Here, we record from anterior cingulate cortex (ACC) and dorsomedial striatum (DMS) while inhibiting direct or indirect pathway neurons in DMS, as mice perform an accumulation-of-evidence task6-10. Inconsistent with the classic model, the manipulations do not produce opponent changes in overall ACC activity. Instead, the pathways exert opponent influence over a subpopulation of ACC neurons that encode accumulated sensory evidence, the task-relevant decision variable. The direction of the modulation depends on a neurons tuning to ipsilateral versus contralateral evidence, such that the two pathways generate opponent shifts in coding specifically along the decision axis. Thus, our results uncover unexpected specificity in the effects of basal ganglia pathways on the cortex, with the two pathways of the DMS exerting opponent control not on overall activity but on coding of the relevant task variable. This functional specificity may extend to other basal ganglia loops to support different aspects of adaptive behavior, with the pathways serving a general role in selecting and shifting cortical representations to subserve circuit-specific functions.

neuroscience↗

TWINKLE: An open-source two-photon microscope for teaching and research

Many laboratories use two-photon microscopy through commercial suppliers, or homemade designs of considerable complexity. The integrated nature of these systems complicates customization, troubleshooting, and training on the principles of two-photon microscopy. Here, we present "Twinkle": a microscope for Two-photon Imaging in Neuroscience, and Kit for Learning and Education. It is a fully open, high performing and easy-to-set-up microscope that can effectively be used for both education and research. The instrument features a > 1 mm field of view, using a modern objective with 3 mm working distance and 2 inch diameter optics combined with GaAsP photomultiplier tubes to maximize the fluorescence signal. We document our experiences using this system as a teaching tool in several two week long workshops, exemplify scientific use cases, and conclude with a broader note on the place of our work in the growing space of open scientific instrumentation.

neuroscience↗

Data science and its future in large neuroscience collaborations.

The rise of large scientific collaborations in neuroscience requires systematic, scalable, and reliable data management. How this is best done in practice remains an open question. To address this, we conducted a data science survey among currently active U19 grants, funded through the NIHs BRAIN Initiative. The survey was answered by both data science liaisons and Principal Investigators, speaking for [~]500 researchers across 21 nation-wide collaborations. We describe the tools, technologies, and methods currently in use, and identify several shortcomings of current data science practice. Building on this survey, we develop plans and propose policies to improve data collection, use, publication, re-use and training in the neuroscience community.

neuroscience↗