2014 — 2016 |
Herzfeld, David James |
F31Activity Code Description: To provide predoctoral individuals with supervised research training in specified health and health-related areas leading toward the research degree (e.g., Ph.D.). |
A Memory of Errors in Motor Adaptation @ Johns Hopkins University
DESCRIPTION (provided by applicant): When we practice a motor task, we can do it better the next time we revisit it. How is this accomplished? The basic assumption in neuroscience has been that this phenomenon, called 'savings', occurs because when we revisit the task, the brain recalls the motor commands that it had previously learned. In this view, motor memory is a memory of motor commands. In contrast to these views, here we propose that motor memory includes a memory of errors, i.e., when we are better at a task, it is often not because we remember the motor commands that we learned before, but because we remember the errors that we have seen before. We propose that this counter-intuitive and previously unknown form of memory, a memory of errors, can account for a very large body of puzzling and unexplained experimental data in the motor learning literature. In this proposal, we begin with a puzzle: in a motor learning task, humans are able to modulate how much they learn from a given error. In some conditions, they learn a large amount, but in other conditions they learn only a small amount. That is, the brain selects how much it is willing to learn from error. To understand the rules that govern control of error-sensitivity, we manipulate the history of errors that subjects experience, and find that positive autocorrelations of errors up-regulate sensitivity, whereas negative autocorrelations down-regulate sensitivity, but only at the specific errors that were actually experienced. That is, experience of an error produces a change in motor commands via errors-sensitivity, which in turn depends on the history of past errors, allowing the brain to respond differently to an error that was experienced before. We formulate this idea with a set of mathematical equations that extend the current framework of learning and show that this new set of equations, representing memory of errors, seamlessly connects a large body of puzzling data. Building on these preliminary results, we propose two groups of experiments to test the foundations of this new idea. The first set of experiments test the prediction that correlations of past errors will modulate error-sensitivity, and that this modulation will be local to the specific errors that were experience. The second set of experiments test the prediction that modulation of error-sensitivity via memory of errors is the basis for the phenomenon of saving. From a clinical standpoint, understanding error-sensitivity is important as it directly affects motor rehabilitation for neuro-trauma or disease. Our theory provides a recipe to modulate error-sensitivity, which should produce faster adaptation, potentially affecting the duration of rehabilitation. In addition, understanding the relationship between error sensitivity and savings may provide useful clues regarding how to effectively apply rehabilitation techniques to promote faster re-learning outside the clinic.
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0.939 |
2020 |
Herzfeld, David James |
K99Activity Code Description: To support the initial phase of a Career/Research Transition award program that provides 1-2 years of mentored support for highly motivated, advanced postdoctoral research scientists. |
Principles of Operation of a Neural Learning Circuit
PROJECT SUMMARY We can learn and successfully recall faces, events, language, concepts, places, facts, things that were frightening or rewarding, and the movement commands required to skillfully move our motor effectors. Decades of scientific research have pointed to the role of synaptic plasticity as the basic currency of the brain?s ability to learn and remember. While we have a fairly detailed understanding of the rules that govern changes in synaptic strength and modifications of a neuron?s intrinsic membrane excitability, our understanding of how these plastic changes lead to behavioral learning and memory is still in its infancy. Behavioral learning is an emergent property of a complete neural learning circuit in which the sites and mechanisms of plasticity are embedded. Without an understanding of the effects of plasticity at the circuit-level, we cannot truly understand learning and memory. Arguably, motor adaptation is the domain where we have best chance to understand the circuit-level rules that govern learning, due to the exquisite relationship between sensory stimuli and adaptive behavior. The cerebellum has been shown to be the brain structure crucial for motor learning, and provides a neural locus to begin to outline the circuit rules that govern learning. Our goal is to leverage the highly conserved cytoarchitecture of the cerebellar circuit to identify the principles of operation that underpin neural learning circuits more generally. During the mentored phase of this award, we will focus on a well-described cerebellar-dependent behavior: pursuit direction learning. Even after the occurrence of a single movement error in this task, the brain learns from the mistake, attempting to minimize the error in the next trial. In the first aim, we will characterize the signals that drive the error-dependent acquisition of this motor memory at a specific synapse in the cerebellar circuit, which is thought to contribute to the bulk of behavioral motor learning. During the second aim, we will record from the complete cerebellar circuit. Our goal is to describe how individual elements and synapses in the cerebellar circuit contribute to behavioral adaptation, including constraints on the site(s) of plasticity that cause behavioral learning and allowing conclusions about the extent to which learning occurs before, inside, or downstream of the cerebellar cortex. During the independent phase, we will again record from the complete cerebellar circuit during a different cerebellar learning task: saccadic adaptation. Using an adaptive behavior that relies on a different cerebellar region, we can begin to dissect the circuit-level principles that generalize broadly across cerebellar learning. Together, our results will provide the first circuit-level rules that underlie behavioral learning. These results should have broad implications across other learning and memory systems, all of which exist as complex circuits that drive behavior.
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