Evolving Schema Representations During Learning

 

Nature volume 590, pages606–611

Evolving schema representations in orbitofrontal ensembles during learning

Jingfeng Zhou, et.al.

Intramural Research Program of the National Institute on Drug Abuse, Baltimore, MD, USA

Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA

 [paraphrase]

How do we learn about what to learn about? Specifically, how do the neural elements in our brain generalize what has been learned in one situation to recognize the common structure of—and speed learning in—other, similar situations? We know this happens because we become better at solving new problems—learning and deploying schemas—through experience. However, we have little insight into this process. Here we show that using prior knowledge to facilitate learning is accompanied by the evolution of a neural schema in the orbitofrontal cortex. Single units were recorded from rats deploying a schema to learn a succession of odour-sequence problems. With learning, orbitofrontal cortex ensembles converged onto a low-dimensional neural code across both problems and subjects; this neural code    represented the common structure of the problems and its evolution accelerated across their learning. These results demonstrate the formation and use of a schema in a prefrontal brain region to support a complex cognitive operation. Our results not only reveal a role for the orbitofrontal cortex in learning but also have implications for using ensemble analyses to tap into complex cognitive functions.

The observed neural representation on each problem is a projection of the true neural representation onto a neural activity space defined by the currently recorded neurons, which can be studied within a lower-dimensional subspace or neural manifold. If the activity in the OFC is converging onto a schema with learning across problems, the true neural representation should become increasingly similar with learning across each problem. However, changes in the recorded neurons across days and problems can cause the observed neural representations to appear misaligned, even if they reflect the same true neural representation. To overcome this problem, we used a canonical correlation analysis (CCA) to align the dimensionality-reduced neural datasets acquired each day on different problems. The CCA finds linear transformations for pairs of neural datasets, such that the transformed datasets are maximally correlated. This results in canonical components (CCs) describing the neural activities from each dataset within a common neural manifold. A higher correlation between a given pair of CCs means better alignment of the two datasets on this particular dimension, consistent with a more generalized, as opposed to idiosyncratic, neural representation.

More broadly, this study identifies a neural signature of schema formation in a prefrontal brain region and for information that is not easily characterized as simply reflecting optimization of motor or sensory processing. The pattern of neural activity in the OFC, identified using dimensionality reduction and manifold alignment,    converged onto a common solution for a complex cognitive operation. Further, the resultant cognitive schema was neurally generalizable not only across problems within the same brain, but also across brains. That the OFC networks in different subjects converged on a common neural representation is key, since it implies that individuals organize information in a similar format, even for advanced prefrontal functions. This is important for neuroscience, since it suggests that we can identify even these types of functions at a granular level by studying groups of subjects. It also has implications for using brain–machine interfaces to enhance functions beyond sensory perception or simple motor activity, since if neural schemas for complex cognitive operations are not specific to individuals, they may be defined from a general understanding or knowledge of their shape, rather than needing to be tailored to each individual for each problem, which would be much less practical.

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