Synapses between neurons are plastic - able to become stronger or weaker. When we learn, new memories are encoded in modified synapses across our brains.
The aim of this project is to understand the rules that determine which synapses will change during learning, and how that change results in an adapted behavior. In particular, we will analyze how an error in a movement acts as a trigger for synaptic change that, in turn, improves the accuracy of subsequent movements. When an incorrect movement is made, the brain gets feedback about the error, and uses this information to guide the induction of plasticity at appropriate synapses. The part of the brain responsible for motor learning, the cerebellum, gets this feedback about errors through a synaptic input known as a climbing fiber. When an error in movement occurs, activity in the climbing fiber is an error signal that sends the message to the cerebellum that the circuit controlling the movement needs to be adjusted by adjusting the strength of some of the synapses. It is usually the synapses that were recently active that are modified. We will analyze which patterns of activity in the climbing fibers or othe cerebellar neurons are necessary and sufficient to cause plasticity to be induced. The rules governing the induction of plasticity at the synapses in a circuit define the algorithm that circui uses to learn. A better understanding those rules can guide strategies to more effectively tap the learning potential of neural circuits in both healthy individuals, those with neurological disorder, and in patients relearning how to control their movements after stroke This proposal addresses not just unanswered questions in the field of motor learning, but is relevant for a more general understanding of how the synaptic plasticity mechanisms in a neural circuit may be finely tuned for the specific computational demands of the behavior it controls.
The proposed experiments will elucidate neural learning rules governing the local 'decisions' cerebellar synapses make on a moment-by-moment basis about whether to alter their strength, based on their pattern of input. Ultimately, our goal is to understand how these local decisions are coordinated throughout a neural circuit to yield an algorithm for the adaptive regulation of the circuit's function over multiple time scales. An understanding of the algorithms that neural circuits use to tune their own performance will have broad impact on everything from artificial intelligence to designing interventions to optimize learning in health and in neural circuits damaged by injury, stroke or disease.
| Suvrathan, Aparna; Payne, Hannah L; Raymond, Jennifer L (2018) Timing Rules for Synaptic Plasticity Matched to Behavioral Function. Neuron 97:248-250 |
| Nguyen-Vu, Td Barbara; Zhao, Grace Q; Lahiri, Subhaneil et al. (2017) A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity. Elife 6: |
| Suvrathan, Aparna; Payne, Hannah L; Raymond, Jennifer L (2016) Timing Rules for Synaptic Plasticity Matched to Behavioral Function. Neuron 92:959-967 |
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| Guo, Christine C; Ke, Michael C; Raymond, Jennifer L (2014) Cerebellar encoding of multiple candidate error cues in the service of motor learning. J Neurosci 34:9880-90 |
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| Nguyen-Vu, T D Barbara; Kimpo, Rhea R; Rinaldi, Jacob M et al. (2013) Cerebellar Purkinje cell activity drives motor learning. Nat Neurosci 16:1734-6 |
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