Dynamic organization of motor cortical neural activities in learning control tasks

Project: Research project

Project Details


Experimental and computational approaches are proposed to address the question of how electrical activities in neuronal circuits give rise to intelligent motor control behaviors. The goal of the proposed study is to elucidate the fundamental mechanism of a rats neural representation in the motor cortical areas as the rat learns by trial and error to derive his control strategy. The basis of the proposed study is from researches to date which indicate that motor cortex may not be a static structure for movement representation, and that a dynamic substrate may be involved in motor skill learning and cognitive motor actions. In lieu of these observations, we propose to examine adaptation in a rats motor cortical neural activities while the rat learns to perform directional control tasks. We further hypothesize that as the rat improves his trial success rate, his neural firing activities become more organized in a way that they result in more clear and accurate predictions of his control decisions and motor control behaviors. The proposed research entails studies in the following two aspects: 1) Multichannel chronic recording from a rats motor cortical areas while the rat performs a directional control task in a behavioral apparatus. The experiment involves a self-paced, freely moving rat learning by trial and error to switch a directional light cue to a center location by pressing a left or right control lever. The goal of the experimental study is to systematically collect cortical and behavioral data. 2) Interpreting the rats behavioral data in relation to neural spike activities. Towards this end, spatiotemporal motor cortical neural spike patterns will be analyzed to quantify an increased trial outcome prediction rate based on neural activities in relation to accurate lever control as the rat gradually masters his task. By doing so we aim to functionally reverse engineer the rats neural circuits in the motor cortical areas to find out how he successfully learns by trial and error to derive an abstract control strategy. It is hypothesized that a dynamic model based on dynamic patterns of motor cortical neurons is developed internally which in turn is used by the rat in performing his tasks to improve his trial success rate. Intellectual merit. The proposed research is a systematic approach to addressing some fundamental issues in relation to the integration of perception and control, cortical plasticity, spike coding/decoding, and the causal relationship between neural cortical representation and intelligent behaviors. The questions raised in this study are frequently debated in many areas such as cognitive neuroscience, neurophysiology, psychology, and neuroengineering. The unique design of the experimental and computational paradigm makes the proposed research potentially transformative in advancing our understanding of a specific area of cortical function and its relationship to behavior. Broad impact. The designed experiments enable us to study the complex pattern of serial, reciprocal, and parallel interconnections among cortical regions implicated in goal directed reach movement. While focusing on the critical perception-to-action step, the experiment takes into account sensory motor integration without being distracted by known facts on the causal relationships in other brain areas involved. As such, the proposed work can focus on addressing the most challenging questions and some controversial issues in perception and control to reveal its neural basis. Conversely, our understanding gained through this study can help us reverse engineer robust controllers or specifically robots and/or brain machine interface devices that are capable of performing sophisticated sensory motor integration tasks. Other potential engineering applications may include nano-circuits designed to replace or provide remedy for deficient neural circuits in patients with perceptual-motor impairment.
Effective start/end date6/1/105/31/14


  • National Science Foundation (NSF): $328,074.00

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