Biology subjects

Baram, A. B.

Publications and source records attributed to Baram, A. B..

4 recordsLinked to original sources

bioRxiv2024

A hierarchical coordinate system for sequence memory in human entorhinal cortex.

The entorhinal cortex (EC) supports a coordinate system for spatial memories, organised in a hierarchy along the EC dorso-ventral axis. Recent theories suggest that a similar coordinate system could scaffold non-spatial memories. Here we show that an abstract hierarchical coordinate system supports arbitrary sequence memories in the human medial temporal lobe (MTL). In single-unit recordings from MTL, we find abstract, coordinate-like coding in a simple sequential memory task. In fMRI we find that abstract coordinate representations are arranged hierarchically along the entorhinal cortex, mirroring the anatomical gradient of grid cells in the rodent EC but now for non-spatial sequences. We replicate this finding in an independent cohort of participants. These data suggest that memories are scaffolded on a hierarchical coordinate system aligned to preserved anatomy across domains and species.

neuroscience↗

bioRxiv2019

Entorhinal and ventromedial prefrontal cortices abstract and generalise the structure of reinforcement learning problems

Knowledge of the structure of a problem, such as relationships between stimuli, enables rapid learning and flexible inference. Humans and other animals can abstract this structural knowledge and generalise it to solve new problems. For example, in spatial reasoning, shortest-path inferences are immediate in new environments. Spatial structural transfer is mediated by grid cells in entorhinal and (in humans) medial prefrontal cortices, which maintain their structure across different environments. Here, using fMRI, we show that entorhinal and ventromedial prefrontal cortex (vmPFC) representations perform a much broader role in generalising the structure of problems. We introduce a task-remapping paradigm, where subjects solve multiple reinforcement learning (RL) problems differing in structural or sensory properties. We show that, as with space, entorhinal representations are preserved across different RL problems only if task structure is preserved. In vmPFC, representations of standard RL signals such as prediction error also vary as a function of task structure.

neuroscience↗

bioRxiv2018

Intuitive planning: global navigation through cognitive maps based on grid-like codes

It is proposed that a cognitive map encoding the relationships between objects supports the ability to flexibly navigate the world. Place cells and grid cells provide evidence for such a map in a spatial context. Emerging evidence suggests analogous cells code for non-spatial information. Further, it has been shown that grid cells resemble the eigenvectors of the relationship between place cells and can be learnt from local inputs. Here we show that these locally-learnt eigenvectors contain not only local information but also global knowledge that can provide both distributions over future states as well as a global distance measure encoding approximate distances between every object in the world. By simply changing the weights in the grid cell population, it is possible to switch between computing these different measures. We demonstrate a simple algorithm can use these measures to globally navigate arbitrary topologies without searching more than one step ahead. We refer to this as intuitive planning.

neuroscience↗

bioRxiv2018

What is a cognitive map? Organising knowledge for flexible behaviour

It is proposed that a cognitive map encoding the relationships between entities in the world supports flexible behaviour, but the majority of the neural evidence for such a system comes from studies of spatial navigation. Recent work describing neuronal parallels between spatial and non-spatial behaviours has rekindled the notion of a systematic organisation of knowledge across multiple domains. We review experimental evidence and theoretical frameworks that point to principles unifying these apparently disparate functions. These principles describe how to learn and use abstract, generalisable knowledge and suggest map-like representations observed in a spatial context may be an instance of general coding mechanisms capable of organising knowledge of all kinds. We highlight how artificial agents endowed with such principles exhibit flexible behaviour and learn map-like representations observed in the brain. Finally, we speculate on how these principles may offer insight into the extreme generalisations, abstractions and inferences that characterise human cognition.

neuroscience↗