Browsing Theses and Dissertations, Electronic (ETDs) by Author "Abbott, Larry"
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ItemACTION SEQUENCING BY RATS(Johns Hopkins University, 2020-03-23) Manakov, Maxim; Krakauer, John W; Karpova, Alla; Druckmann, Shaul; Jayaraman, Vivek; Dudman, Joshua; Abbott, Larry; Knierim, JamesThe capacity to sequence information is central to human intelligence, and it is likely that reasoning – an orderly progression of thoughts to reach a conclusion – arose from the ability to sequence actions by the motor system. As such, an understanding of the neural basis of action sequence generation may eventually shed light on the neural basis of general intelligence. In this thesis, I present an experimental framework for studying the neural underpinnings of cognitive sequencing – the ability to perceive, represent and execute a set of ordered actions that can be modified or operated on independently of the details of motor implementation – in the Long Evans rat. In this self-guided exploration of structured sequence space framework, rats resort to structured exploration to generate sequences that display trajectory and temporal abstraction of individual sequence elements and can be operated on as a unit. Strikingly, animals’ exploratory strategies/potential errors align with the grammatical structure of the animals’ acquired “vocabulary”. I also detail the identification of the rodent homologue of the supplementary motor cortex (SMC) through a combination of anatomical studies and pharmacological inactivations. Using targeted electrophysiological recordings of SMC neural ensemble dynamics and comparing it to the dynamics observed in the anterior cingulate cortex (ACC), I show that whereas ACC has a high-dimensional representation that distinguishes many sequences and contexts, the SMC has a more rigid and low-dimensional representation that captures some aspects of the abstract structure of the task. Combined with the observation that optogenetic perturbation of SMC-to-ACC projection curtails exploratory sequences, these results argue that SMC is a candidate region that may inform network models of sequence generation and abstraction.