Biology subjects

Saxe, A.

Publications and source records attributed to Saxe, A..

4 recordsLinked to original sources

bioRxiv2023

Striatal dopamine reflects individual long-term learning trajectories

Learning from naive to expert occurs over long periods of time, accompanied by changes in the brains neuronal signals. The principles governing behavioural and neuronal dynamics during long-term learning remain unknown. We developed a psychophysical visual decision task for mice that allowed for studying learning trajectories from naive to expert. Mice adopted sequences of strategies that became more stimulus-dependent over time, showing substantial diversity in the strategies they transitioned through and settled on. Remarkably, these transitions were systematic; the initial strategy of naive mice predicted their strategy several weeks later. Longitudinal imaging of dopamine release in dorsal striatum demonstrated that dopamine signals evolved over learning, reflecting stimulus-choice associations linked to each individuals strategy. A deep neural network model trained on the task with reinforcement learning captured behavioural and dopamine trajectories. The models learning dynamics accounted for the mices diverse and systematic learning trajectories through a hierarchy of saddle points. The model used prediction errors mirroring recorded dopamine signals to update its parameters, offering a concrete account of striatal dopamines role in long-term learning. Our results demonstrate that long-term learning is governed by diverse yet systematic transitions through behavioural strategies, and that dopamine signals exhibit key characteristics to support this learning.

neuroscience↗

bioRxiv2021

Neural knowledge assembly in humans and deep networks

Human understanding of the world can change rapidly when new information comes to light, such as when a plot twist occurs in a work of fiction. This flexible "knowledge assembly" requires few-shot reorganisation of neural codes for relations among objects and events. However, existing computational theories are largely silent about how this could occur. Here, participants learned a transitive ordering among novel objects within two distinct contexts, before exposure to new knowledge that revealed how they were linked. BOLD signals in dorsal frontoparietal cortical areas revealed that objects were rapidly and dramatically rearranged on the neural manifold after minimal exposure to linking information. We then adapt stochastic online gradient descent to permit similar rapid knowledge assembly in a neural network model.

neuroscience↗

bioRxiv2021

Organizing memories for generalization in complementary learning systems

Memorization and generalization are complementary cognitive processes that jointly promote adaptive behavior. For example, animals should memorize a safe route to a water source and generalize to features that allow them to find new water sources, without expecting new paths to exactly resemble previous ones. Memory aids generalization by allowing the brain to extract general patterns from specific instances that were spread across time, such as when humans progressively build semantic knowledge from episodic memories. This cognitive process depends on the neural mechanisms of systems consolidation, whereby hippocampal-neocortical interactions gradually construct neocortical memory traces by consolidating hippocampal precursors. However, recent data suggest that systems consolidation only applies to a subset of hippocampal memories; why certain memories consolidate more than others remains unclear. Here we introduce a novel neural network formalization of systems consolidation that highlights an overlooked tension between neocortical memory transfer and generalization, and we resolve this tension by postulating that memories only consolidate when it aids generalization. We specifically show that unregulated memory transfer can be detrimental to generalization in unpredictable environments, whereas optimizing systems consolidation for generalization generates a high-fidelity, dual-system network supporting both memory and generalization. This theory of generalization-optimized systems consolidation produces a neural network that transfers some memory components to the neocortex and leaves others dependent on the hippocampus. It thus provides a normative principle for reconceptualizing numerous puzzling observations in the field and provides new insight into how adaptive behavior benefits from complementary learning systems specialized for memorization and generalization.

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↗