Observable signatures of learning in neural circuits

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Learning is a critical function of the nervous system, and it arises from the plasticity of the synaptic connections between neurons. Accordingly, a major effort in neuroscience aims to understand the principles underlying synaptic plasticity: to identify mathematical rules that can predict when, and by how much, synaptic strengths will change. While experiments in slices of brain tissue can measure the strengths of synapses, and their changes during protocols that drive plasticity, there is currently no reliable way to do these same experiments in the brains of living animals. Thus, neuroscientists lack an understanding of how learning via synaptic plasticity works under the conditions that we care most about. To overcome this difficulty, I propose to develop a mathematical framework that will reveal signatures of different synaptic plasticity rules within a neural circuit, that can be observed using the types of data typically collected from the brains of living animals. Next, I will apply this mathematical framework to data collected by my collaborators in the brains of awake behaving animals, to identify the plasticity mechanisms that are most (or least) consistent with the neural data. ******For each possible synaptic plasticity mechanism, we will use analytical calculations, and simulations of neural circuits, to identify the mean-variance relationship, and the relationship between signal and noise correlations, in the neurons' activities. These quantities are often measured in the brains of awake behaving animals, using standard experimental methods (Utah arrays, Ca2+ imaging, etc.), both by my collaborators (e.g., Brain Observatory team at the Allen Institute for Brain Science), and by others who make their data publicly available (e.g., via the CRCNS repository). ******Next, we will develop a data-science method that will "demix" neural data into components with different mean-variance relationships and relationships between signal and noise correlations; this method will estimate the relative contributions of each of these components. Finally, we will apply this method to neural data. By identifying each component with the plasticity mechanism that yields the same mean-variance relationship and relationship between signal and noise correlations, we will estimate the relative contribution of each plasticity mechanism to the neural variability within each dataset.******By identifying the mechanisms underlying learning, this work may lead to better treatments for those with learning disorders. Moreover, this work could lead to subsequent advances in machine learning (ML): by implementing these mechanisms in next-generation ML systems, developers can create more biorealistic ML. Inasmuch as today's ML algorithms are inspired by our current understanding of neural information processing, and are already impressively powerful, we anticipate that these next-generation ML systems could have substantial impacts on the broader community.