Operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior, engages a high-dimensional neuronal population space spanning multiple brain regions. A complete characterization of an operant memory remains elusive. Some sites of plasticity participating in the engram underlying an example of operant memory in Aplysia have been previously uncovered. Three studies are described here that sought to draw closer to a thorough characterization of this memory. The first study used a computational model to examine the ways in which sites of plasticity (individually and in combination) contribute to memory expression. Each site of plasticity altered multiple features of motor output simultaneously. Plasticity loci exhibited mutual dependence and synergism. The second study identified a low-dimensional signature of operant memory. Using single-neuron resolution voltage imaging and dimensionality reduction, an advancement in the recruitment of one of two motor modules was identified as the primary signature of operant learning in the population activity. The third study expanded the functional neurocartography framework developed by Frady et al. (2016), a semi-supervised machine learning algorithm for identification of the same neuron across subjects. A cyclic matching method was developed, allowing for unsupervised extraction of groups of neurons and automated selection of high-quality matches. Taken together, the results of these studies provide several insights and tools useful toward the characterization of an operant memory.
","abstract_html":"<p>Operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior, engages a high-dimensional neuronal population space spanning multiple brain regions. A complete characterization of an operant memory remains elusive. Some sites of plasticity participating in the engram underlying an example of operant memory in <em>Aplysia </em>have been previously uncovered. Three studies are described here that sought to draw closer to a thorough characterization of this memory. The first study used a computational model to examine the ways in which sites of plasticity (individually and in combination) contribute to memory expression. Each site of plasticity altered multiple features of motor output simultaneously. Plasticity loci exhibited mutual dependence and synergism. The second study identified a low-dimensional signature of operant memory. Using single-neuron resolution voltage imaging and dimensionality reduction, an advancement in the recruitment of one of two motor modules was identified as the primary signature of operant learning in the population activity. The third study expanded the functional neurocartography framework developed by Frady et al. (2016), a semi-supervised machine learning algorithm for identification of the same neuron across subjects. A cyclic matching method was developed, allowing for unsupervised extraction of groups of neurons and automated selection of high-quality matches. Taken together, the results of these studies provide several insights and tools useful toward the characterization of an operant memory.</p>","abstract_has_math":false,"creators":["Costa, Renan Murillo","0000-0001-7195-5259
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