title: Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties.
abstract: Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, 300072, Tianjin, China.
authors.forename: Shuangming
authors.initials: S
authors.lastname: Yang
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, 300072, Tianjin, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Deng
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, 300072, Tianjin, China.
authors.forename: Jiang
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Automation and Electrical Engineering, Tianjin University of Technology and Educations, 300222, Tianjin, China.
authors.forename: Huiyan
authors.initials: H
authors.lastname: Li
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, 300072, Tianjin, China.|Department of Electrical Engineering and Computer Science, Case Western Reserve University, 44106, Cleveland, Ohio, USA.
authors.forename: Chen
authors.initials: C
authors.lastname: Liu
authors.affiliation: Department of Electrical Engineering and Computer Science, Case Western Reserve University, 44106, Cleveland, Ohio, USA.
authors.forename: Chris
authors.initials: C
authors.lastname: Fietkiewicz
authors.affiliation: Department of Electrical Engineering and Computer Science, Case Western Reserve University, 44106, Cleveland, Ohio, USA.
authors.forename: Kenneth A
authors.initials: KA
authors.lastname: Loparo
mesh_terms: D002540:Cerebral Cortex / Q000503:physiopathology*; D003199:Computer Systems; D006801:Humans; D008564:Membrane Potentials; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000503:physiopathology; D009474:Neurons / Q000502:physiology*; D020734:Parkinsonian Disorders / Q000503:physiopathology*; D013788:Thalamus / Q000503:physiopathology*