Biology subjects

Juechems, K.

Publications and source records attributed to Juechems, K..

3 recordsLinked to original sources

bioRxiv2021

Generalising state transition structure in the human medial temporal lobe

Effective planning involves knowing where different actions will take us. However natural environments are rich and complex, leading to an exponential increase in memory demand as a plan grows in depth. One potential solution to this problem is to share the neural state transition functions used for planning between similar contexts. Here, we asked human participants to perform a sequential decision making task designed so that knowledge could be shared between some contexts but not others. Computational modelling showed that participants shared a model of state transitions between contexts where appropriate. fMRI data identified the medial temporal lobe as a locus for learning of state transitions, and within the same region, correlated BOLD patterns were observed in contexts where state transition information were shared. Finally, we show that the transition model is updated more strongly following the receipt of positive compared to negative outcomes, a finding that challenges conventional theories of planning which assume knowledge about our environment is updated independently of outcomes received. Together, these findings propose a computational and neural account of how information relevant for planning can be shared between contexts.

neuroscience↗

bioRxiv2021

Rich and lazy learning of task representations in brains and neural networks

How do neural populations code for multiple, potentially conflicting tasks? Here, we used computational simulations involving neural networks to define "lazy" and "rich" coding solutions to this multitasking problem, which trade off learning speed for robustness. During lazy learning the input dimensionality is expanded by random projections to the network hidden layer, whereas in rich learning hidden units acquire structured representations that privilege relevant over irrelevant features. For context-dependent decision-making, one rich solution is to project task representations onto low-dimensional and orthogonal manifolds. Using behavioural testing and neuroimaging in humans, and analysis of neural signals from macaque prefrontal cortex, we report evidence for neural coding patterns in biological brains whose dimensionality and neural geometry are consistent with the rich learning regime.

neuroscience↗

bioRxiv2018

A network for computing value homeostasis in the human medial prefrontal cortex

Humans and other animals make decisions in order to satisfy their goals. However, it remains unknown how neural circuits compute which of multiple possible goals should be pursued (e.g. when balancing hunger and thirst) and combine these signals with estimates of available reward alternatives. Here, humans undergoing functional magnetic resonance imaging (fMRI) accumulated two distinct assets over a sequence of trials. Financial outcomes depended on the minimum cumulate of either asset, creating a need to maintain \"value homeostasis\" by redressing any imbalance among the assets. BOLD signals in the dorsal anterior cingulate cortex (dACC) tracked the level of homeostatic imbalance among goals, whereas the ventromedial prefrontal cortex (vmPFC) signalled the level of homeostatic redress incurred by a choice, rather than the overall amount received. These results suggest that a network of medial frontal brain regions compute a value signal that maintains homeostatic balance among internal goals.

neuroscience↗