Biology subjects

Wojcik, M. J.

Publications and source records attributed to Wojcik, M. J..

3 recordsLinked to original sources

bioRxiv2025

Working memory shapes neural geometry in human EEG over learning

Working memory has been traditionally studied as a passive storage for information. However, recent advances have suggested that working memory is prospective rather than retrospective, meaning that its content undergoes transformations that will support future behaviour. One perspective that underscores this notion conceptualises memory processes as a computational resource that can be used to reduce the complexity of computation at decision time. Here, we explore this perspective by examining whether the process of maintenance shapes neural geometry and leads to low-dimensional representations during storage and later decision time. We recorded EEG in 25 human participants who learnt to solve a XOR task. We hypothesised that separating task features by a working memory delay would result in participants temporally decomposing the XOR computation, by prospectively processing one of the task features early in trial time. In line with our predictions, participants transformed the first feature from a sensory to an abstract format and maintained this pre-processed information throughout the delay. This process was related to the low-dimensional representation required at decision time early in learning, a representation that has recently been shown to support later cross-generalisation. These results demonstrate that low-dimensional representations, elsewhere associated with slow learning, might also provide a mechanism for maintenance processes in working memory.

neuroscience↗

bioRxiv2023

Ignorance is bliss: effects of noise and metaboliccost on cortical task representations

Cognitive flexibility requires both the encoding of task-relevant and the ignoring of task-irrelevant stimuli. While the neural coding of task-relevant stimuli is increasingly well understood, the mechanisms for ignoring task-irrelevant stimuli remain poorly understood. Here, we study how task performance and biological constraints jointly determine the coding of relevant and irrelevant stimuli in neural circuits. Using mathematical analyses and task-optimized recurrent neural networks, we show that neural circuits can exhibit a range of representational geometries depending on the strength of neural noise and metabolic cost. By comparing these results with recordings from primate prefrontal cortex (PFC) over the course of learning, we show that neural activity in PFC changes in line with a minimal representational strategy. Specifically, our analyses reveal that the suppression of dynamically irrelevant stimuli is achieved by activity-silent, sub-threshold dynamics. Our results provide a normative explanation as to why PFC implements an adaptive, minimal representational strategy.

neuroscience↗

bioRxiv2023

Learning shapes neural geometry in the prefrontal cortex

The relationship between the geometry of neural representations and the task being performed is a central question in neuroscience1-6. The primate prefrontal cortex (PFC) is a primary focus of inquiry, as it can encode information with geometries that either rely on past experience7-13 or are experience agnostic3,14-16. One hypothesis is that PFC representations should evolve with learning4,17,18, from a format that supports exploration of all possible task rules to a format that minimises the encoding of task-irrelevant features4,17,18 and supports generalisation7,8. Here we test this idea by recording neural activity from PFC when learning a new rule ( XOR rule) from scratch. We show that PFC representations progress from being high dimensional, nonlinear and randomly mixed to low dimensional and rule selective. Upon generalising the rule to novel stimuli, these representations further evolve into an abstract, stimulus-invariant geometry. These findings reconcile previously conflicting accounts of PFC function by demonstrating how neural representations adapt across distinct stages of learning.

neuroscience↗