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Svahn, E.

Publications and source records attributed to Svahn, E..

3 recordsLinked to original sources

Acetylcholine reflects uncertainty during hidden state inference

To act adaptively, animals must infer features of the environment that cannot be observed directly, such as which option is currently rewarding, or which context they are in. These internal estimates, known as hidden states, guide behaviour but are inherently uncertain. Theoretical models propose that efficient inference requires tracking both the most likely state and the uncertainty of that estimate. While neural representations of state identity have been described in cortical and hippocampal circuits, the origin of uncertainty signals remains unclear. Here we show that acetylcholine (ACh) reflects this uncertainty during hidden state inference. Using fibre photometry in mice performing a probabilistic two-armed bandit task, we found that ACh release in medial prefrontal cortex and ventral hippocampus tracked recent omission history and predicted whether animals would switch choice on the next trial. Blocking muscarinic receptors with scopolamine selectively impaired loss-driven switching without affecting stable performance. A hidden-state inference model in which ACh modulated how uncertainty from past experience shaped future beliefs reproduced both the physiological and behavioural effects, whereas alternative models did not. These results identify ACh as a neuromodulatory signal reflecting uncertainty during hidden-state inference, linking theoretical models of belief updating to their biological implementation in the mammalian brain.

neuroscience↗

Heron: A Knowledge Graph editor for intuitive implementation of python based experimental pipelines.

To realise a research project idea, an experimenter faces a series of conflicting design and implementation considerations, regarding both its hardware and software components. For instance, the ease of implementation, in time and expertise, should be balanced against the ease of future reconfigurability and number of black box components. Other, often conflicting, considerations include the level of documentation and ease of reproducibility, resource availability as well as access to online communities. To alleviate this balancing act between opposing requirements we present Heron, a new Python-based platform to construct and run experimental and data analysis pipelines. Herons main principle is to allow researchers to design and implement the experimental flow as close as possible to their mental schemata of the experiment, in the form of a Knowledge Graph. Heron is designed to increase the implementation speed of experiments (and their subsequent updates), while minimising the number of incorporated black box components. It enhances the readability and reproducibility of the final implementation and allows the use of combinations of hardware and software otherwise impossible or too costly to achieve. Given this, Heron offers itself to sciences whose needs involve experiments with a large number of interconnected hardware and software components like robotics, neuroscience, behavioural sciences, physics, chemistry, environmental science, etc.. It is designed with those experimentalists in mind which: i) Demand full control of their setup. ii) Prefer not to have to choose between hardware and software that run only on a specific chip/operating system combination. iii) Appreciate the ease and speed that high-level languages (e.g. Python) and Graphical User Interfaces (GUIs) offer them. It assumes an intermediate knowledge of the Python language and ecosystem, offering a flexible and powerful way to construct experimental setups. It removes any inaccessible corners, yet keeps implementation costs significantly reduced compared to using lower level languages. Finally, its use results in a much cleaner and easier to understand code base, amicable to documentation and reproducibility efforts.

animal behavior and cognition↗

Telencephalic outputs from the medial entorhinal cortex are copied directly to the hippocampus

Complementary actions of the neocortex and the hippocampus enable encoding and long-term storage of experience dependent memories. Standard models for memory storage assume that sensory signals reach the hippocampus from superficial layers of the entorhinal cortex (EC). Deep layers of the EC on the other hand relay hippocampal outputs to the telencephalic structures including many parts of the neocortex. Here we show that cells in Layer 5a of the medial EC send a copy of their telencephalic outputs back to the CA1 region of the hippocampus. Combining cell-type specific anatomical tracing, high-throughput RNA-sequencing based projection mapping and optogenetics aided circuit mapping, we show that these projections have a unique topography and target hippocampal pyramidal cells and interneurons. Our results suggest that rather than serving as a relay, deep EC may coordinate hippocampal-neocortical interactions in spatial cognition and memory.

neuroscience↗