Optimization of stimulation-induced cortical plasticity using an integrate-and-fire spiking neural network model

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Project Summary / Abstract Cortical stimulation has become a prevalent therapy for various ailments including stroke and traumatic brain injury. Although targeted plasticity using closed-loop stimulation paradigms has been extensively characterized in vitro, mechanisms in vivo remain to be further explored. Previous results have also been difficult to compare due to differences in stimulation methods, brain regions, implants, and animal models. Thus, we will incorporate a integrate-and-fire (IF) spiking neural network model in conjunction with experimental validation to methodically compare and uncover novel conditioning paradigms as well as better understand how stimulation affects neural circuitry. The simplicity and computational efficiency of the IF neural network model allows us to quickly simulate hundreds of interconnected neurons. The model will be initialized with a spike-timing dependent plasticity (STDP) rule that reflects previous findings reported from the laboratory, as experimental results strongly suggest classic STDP mediates stimulus-induced plasticity in vivo. The proposed experiments will seek to optimize spike- triggered stimulation by discerning whether various stimulus parameters, including number of pulses and stimulation frequency, affect the induced plasticity. They will also explore novel conditioning protocols such as gamma-triggered stimulation and brain-state dependent stimulation to determine if population-based paradigms could be more effective methods of inducing plasticity. I will conduct these studies within the primary motor cortex of intact macaques for greatest clinical relevance. Experiments will be performed in conjunction with the IF neural network model simulations to coevolve the stimulation parameters and model architecture. The results of this study will provide a framework for comparing stimulation methods and inform clinical applications of cortical stimulation. Through these projects the PI will be trained in a wide array of disciplines including experimental techniques with behaving non-human primates, analytical techniques involving circuit level analyses, and computational modeling skills. Beyond science, he will also learn how to communicate effectively as a research scientist and continue participating in the neuroscience community through collaborations with different laboratories and involvement with various research centers. The research training will take place in the Fetz laboratory at the University of Washington with the facilities, equipment, and resources made available through the Department of Bioengineering, Department of Physiology & Biophysics, and the Washington National Primate Research Center.