Biology subjects

Bruijns, S. A.

Publications and source records attributed to Bruijns, S. A..

3 recordsLinked to original sources

bioRxiv2025

Low dimensional latent structure underlying the choices of mice

An impressive wealth of cognitive neuroscience tasks involves combining perceptual information with an estimate of the latent state of the environment to make a decision. Such tasks have driven the development of theoretically-motivated cognitive models which offer compactly parameterised, and thereby insightful, accounts of the internal processes by which this might happen. However, the very large amounts of data that can now be collected present a challenge and an opportunity for this frame-work. The challenge is that these models can be shown to underfit the data systematically (Nassar & Frank, 2016; Palminteri, Wyart, & Koechlin, 2017; Wilson & Collins, 2019) - particularly in how they characterise the effect of the latent state estimate. But this opens up the opportunity to extend the framework and employ richer, more highly parameterised and flexible models, in a data-driven manner (Dezfouli, Griffiths, Ramos, Dayan, & Balleine, 2019). Unfortunately, it is tremendously difficult to interpret these models, precisely because of their flexibility. Here, we follow a recent approach (Eckstein, Summerfield, Daw, & Miller, 2024), in which components of compact models are progressively replaced with more flexible homologues, but, where possible, the resulting insights are mapped back into theoretically-transparent forms. We prove the effectiveness of our scheme by applying it to one of the largest available decision-making data sets for mice - more than 300,000 choices from 139 subjects studied by the International Brain Lab (The International Brain Laboratory et al., 2021). We found widely generalisable phenomena such as notable effects of continual fluctuations in task engagement and systematic differences between learning and forgetting of chosen and unchosen options in determining the subjective state estimate. Our results show that combining theory-driven and data-driven methods can reveal cognitive processes that would have been difficult to discover using either method on its own.

animal behavior and cognition↗

bioRxiv2023

Dissecting the Complexities of Learning With Infinite Hidden Markov Models

Learning to exploit the contingencies of a complex experiment is not an easy task for animals. Individuals learn in an idiosyncratic manner, revising their approaches multiple times as they are shaped, or shape themselves, and potentially end up with different strategies. Their long-run learning curves are therefore a tantalizing target for the sort of individualized quantitative characterizations that sophisticated modelling can provide. However, any such model requires a flexible and extensible structure which can capture radically new behaviours as well as slow changes in existing ones. To this end, we suggest a dynamic input-output infinite hidden semi-Markov model, whose latent states are associated with specific components of behaviour. This model includes an infinite number of potential states and so has the capacity to describe substantially new behaviours by unearthing extra states; while dynamics in the model allow it to capture more modest adaptations to existing behaviours. We individually fit the model to data collected from more than 100 mice as they learned a contrast detection task over tens of sessions and around fifteen thousand trials each. Despite large individual differences, we found that most animals progressed through three major stages of learning, the transitions between which were marked by distinct additions to task understanding. We furthermore showed that marked changes in behaviour are much more likely to occur at the very beginning of sessions, i.e. after a period of rest, and that response biases in earlier stages are not predictive of biases later on in this task.

animal behavior and cognition↗

bioRxiv2022

Reproducibility of in-vivo electrophysiological measurements in mice

Understanding brain function relies on the collective work of many labs generating reproducible results. However, reproducibility has not been systematically assessed within the context of electrophysiological recordings during cognitive behaviors. To address this, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. Experimenters in ten laboratories repeatedly targeted Neuropixels probes to the same location (spanning secondary visual areas, hippocampus, and thalamus) in mice making decisions; this generated a total of 121 experimental replicates, a unique dataset for evaluating reproducibility of electrophysiology experiments. Despite standardizing both behavioral and electrophysiological procedures, some experimental outcomes were highly variable. A closer analysis uncovered that variability in electrode targeting hindered reproducibility, as did the limited statistical power of some routinely used electrophysiological analyses, such as single-neuron tests of modulation by individual task parameters. Reproducibility was enhanced by histological and electrophysiological quality-control criteria. Our observations suggest that data from systems neuroscience is vulnerable to a lack of reproducibility, but that across-lab standardization, including metrics we propose, can serve to mitigate this.

neuroscience↗