Biology subjects

Nili, H.

Publications and source records attributed to Nili, H..

3 recordsLinked to original sources

bioRxiv2018

The hippocampus and neocortical inhibitory engrams protect against memory interference

Our experiences often overlap with each other, sharing features, stimuli or higher-order information. But despite this overlap, we are able to selectively recall individual memories to guide our decisions and future actions. The neural mechanisms that support such precise memory recall, however, remain unclear. Here, using ultra-high field 7T MRI we reveal two distinct mechanisms that protect memories from interference. The first mechanism involves the hippocampus, where the BOLD signal predicts behavioural measures of memory interference, and contextual representations that aid separation of overlapping memories are organised using a relational code. The second mechanism involves neocortical inhibition: when we reduce the concentration of neocortical GABA using trans-cranial direct current stimulation (tDCS) neocortical memory interference increases in proportion to the reduction in GABA, which in turn predicts behavioural performance. Together these findings suggest that memory interference is mediated by both the hippocampus and neocortex, where the hippocampus aids separation of memories by coding context-dependent relational information, while neocortical inhibition prevents unwanted co-activation between overlapping memories.

neuroscience↗

bioRxiv2018

Neural structure mapping in human probabilistic reward learning

Humans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented by a single dimension (i.e. on a mental line), for example according to their cardinality, size or value. Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their pay-out probability in a way that suggested they were encoded onto the same mental number line. Our findings suggest that in humans, learning about values is accompanied by structural alignment of value representations with neural codes for the concept of magnitude.

neuroscience↗

bioRxiv2018

Focused learning promotes continual task performance in humans

Humans can learn to perform multiple tasks in succession over the lifespan (\"continual\" learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans. Unlike neural networks, humans that were trained on temporally autocorrelated task objectives (focussed training) learned to perform new tasks more effectively, and performed better on a later test involving randomly interleaved tasks. Analysis of error patterns suggested that focussed learning permitted the formation of factorised task representations that were protected from mutual interference. Furthermore, individuals with a strong prior tendency to represent the task space in a factorised manner enjoyed greater benefit of focussed over interleaved training. Building artificial agents that learn to factorise tasks appropriately may be a promising route to solving continual task performance in machine learning.\n\nSignificance StatementHumans learn to perform many different tasks over the lifespan, such as speaking both French and Spanish. The brain has to represent task information without mutual interference. In machine learning, this \"continual learning\" is a major unsolved challenge. Here, we studied the patterns of errors made by humans and state-of-the-art deep networks whilst they learned new tasks from scratch and without instruction. Humans, but not machines, seem to benefit from training regimes that focussed on one task at a time, especially when they had a prior bias to represent stimuli in a way that facilitated task separation. Machines trained to exhibit the same prior bias suffered less interference between tasks, suggesting new avenues for solving continual learning in artificial systems.

neuroscience↗