Huiyan Li

Digital implementations of thalamocortical neuron models and its application in thalamocortical control using FPGA for Parkinson's disease

Due to the relay ability of sensory information and communication between cortical regions, the thalamocortical (TC) relay neuron plays an essential role in the therapy of Parkinsons disease. This paper first explores a series of efficient methods for the hardware implementation of TC relay neuron models, aiming to reproduce relevant biological behaviors and present appropriate feedback control in neural dynamics in thalamic systems. In addition, a modified two-dimensional TC neuron model is presented for convenient realization to decrease the complexity of the original model and promote the feasibility of the digital design, which shows significance for the large-scale network simulation of TC-based networks and the establishment of digital thalamus. A system-on-a-chip model-based control system is implemented on an FPGA using the modified TC neuron model, which is aimed at the real-time feedback control of tremor dominant Parkinsonian state. In this paper, the hardware syntheses and theoretical researches are given to illustrate the outstanding performance of the presented hardware implementation. The presented platform can be applied in both the brain-machine interface and the robotic control projects, and the proposed modular hardware framework can be extended to the real-time closed-loop treatments of other dyskinesia diseases. We first present efficient digital implementation of thalamocortical neuron model.Modified thalamocortical neuron model is proposed for a convenient implementation.This model can decrease the resource cost while retaining the biological dynamics.A digital thalamocortical model-based controller is designed using the modified model.The control platform can be applied in real-time control of Parkinsons disease.

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

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.

Closed-Loop Control of Tremor-Predominant Parkinsonian State Based on Parameter Estimation

A significant feature of Parkinsons disease (PD) is the inability of the thalamus to respond faithfully to sensorimotor information from the cerebral cortex. This may be the result of abnormal oscillations in the basal ganglia (BG). Deep brain stimulation (DBS) is regarded as an effective method to modulate these pathological brain rhythmic activities. However, the selection of DBS parameters is challenging because the mechanism is not well understood. This work proposes the design of a closed-loop control strategy to automatically adjust the parameters of a DBS waveform based on a computational model. By estimating the synaptic input from BG to the thalamic neuron model as feedback variable, we designed and compared various control algorithms to counteract the effects of pathological oscillatory inputs. We then obtained optimal DBS parameters to modulate the tremor-predominant Parkinsonian state. We showed that even a simple proportional controller provides higher fidelity of thalamic relay of sensorimotor information and lower energy expenditure, as compared with classical open-loop DBS. Integral action further enhances DBS performance. Additionally, a positive bias voltage further improves the relay ability of the thalamus with decreased stimulation energy expenditure. These findings were conducive to the development of a more effective DBS to further improve the treatment of the PD.

Modeling and Analysis of Beta Oscillations in the Basal Ganglia

Enhanced beta (12–30 Hz) oscillatory activity in the basal ganglia (BG) is a prominent feature of the Parkinsonian state in animal models and in patients with Parkinson’s disease. Increased beta oscillations are associated with severe dopaminergic striatal depletion. However, the mechanisms underlying these pathological beta oscillations remain elusive. Inspired by the experimental observation that only subsets of neurons within each nucleus in the BG exhibit oscillatory activities, a computational model of the BG-thalamus neuronal network is proposed, which is characterized by subdivided nuclei within the BG. Using different currents externally applied to the neurons within a given nucleus, neurons behave according to one of the two subgroups, named “-N” and “-P,” where “-N” and “-P” denote the normal and the Parkinsonian states, respectively. The ratio of “-P” to “-N” neurons indicates the degree of the Parkinsonian state. Simulation results show that if “-P” neurons have a high degree of connectivity in the subthalamic nucleus (STN), they will have a significant downstream effect on the generation of beta oscillations in the globus pallidus. Interestingly, however, the generation of beta oscillations in the STN is independent of the selection of the “-P” neurons in the external segment of the globus pallidus (GPe), despite the reciprocal structure between STN and GPe. This computational model may pave the way to revealing the mechanism of such pathological behaviors in a realistic way that can replicate experimental observations. The simulation results suggest that the STN is more suitable than GPe as a deep brain stimulation target.

A neural mass model of basal ganglia nuclei simulates pathological beta rhythm in Parkinson's disease.

An increase in beta oscillations within the basal ganglia nuclei has been shown to be associated with movement disorder, such as Parkinsons disease. The motor cortex and an excitatory-inhibitory neuronal network composed of the subthalamic nucleus (STN) and the external globus pallidus (GPe) are thought to play an important role in the generation of these oscillations. In this paper, we propose a neuron mass model of the basal ganglia on the population level that reproduces the Parkinsonian oscillations in a reciprocal excitatory-inhibitory network. Moreover, it is shown that the generation and frequency of these pathological beta oscillations are varied by the coupling strength and the intrinsic characteristics of the basal ganglia. Simulation results reveal that increase of the coupling strength induces the generation of the beta oscillation, as well as enhances the oscillation frequency. However, for the intrinsic properties of each nucleus in the excitatory-inhibitory network, the STN primarily influences the generation of the beta oscillation while the GPe mainly determines its frequency. Interestingly, describing function analysis applied on this model theoretically explains the mechanism of pathological beta oscillations.

Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks

The investigation of the human intelligence, cognitive systems and functional complexity of human brain is significantly facilitated by high-performance computational platforms. In this paper, we present a real-time digital neuromorphic system for the simulation of large-scale conductance-based spiking neural networks (LaCSNN), which has the advantages of both high biological realism and large network scale. Using this system, a detailed large-scale cortico-basal ganglia-thalamocortical loop is simulated using a scalable 3-D network-on-chip (NoC) topology with six Altera Stratix III field-programmable gate arrays simulate 1 million neurons. Novel router architecture is presented to deal with the communication of multiple data flows in the multinuclei neural network, which has not been solved in previous NoC studies. At the single neuron level, cost-efficient conductance-based neuron models are proposed, resulting in the average utilization of 95% less memory resources and 100% less DSP resources for multiplier-less realization, which is the foundation of the large-scale realization. An analysis of the modified models is conducted, including investigation of bifurcation behaviors and ionic dynamics, demonstrating the required range of dynamics with a more reduced resource cost. The proposed LaCSNN system is shown to outperform the alternative state-of-the-art approaches previously used to implement the large-scale spiking neural network, and enables a broad range of potential applications due to its real-time computational power.

Real-Time Prediction of the Unobserved States in Dopamine Neurons on a Reconfigurable FPGA Platform

Real-time prediction of dynamical characteristics of Dopamine (DA) neurons, including properties in ion channels and membrane potentials, is meaningful and critical for the investigation of the dynamical mechanisms of DA cells and the related psychiatric disorders. However, obtaining the unobserved states of DA neurons is significantly challenging. In this paper, we present a real-time prediction system for DA unobserved states on a reconfigurable field-programmable gate array (FPGA). In the presented system, the unscented Kalman filter (UKF) is implemented into a DA neuron model for dynamics prediction. We present a modular structure to implement the prediction algorithm and a digital topology to compute the roots of matrices in the UKF implementation. Implementation results show that the proposed system provides the real-time computational ability to predict the DA unobserved states with high precision. Although the presented system is aimed at the state prediction of DA cells, it can also be applied into the dynamic-clamping technique in the electrophysiological experiments, the brain-machine interfaces and the neural control engineering works.

Design of the feedback controller for deep brain stimulation of the parkinsonian state based on the system identification

A novel closed-loop control strategy of deep brain stimulation is explored in this paper. By establishing an input-output model of the basal ganglia, the causality between the external stimuli and neuronal activities can be revealed. One-step ahead prediction constructs the probable future information of the tracking errors, which is used to guide the amplitude of the current pulse train stimuli. By comparing the traditional and iterative learning proportional control algorithms, the latter control strategy not only automatically can optimize the control signals without requirements of any particular knowledge on the details of model, but also can reduce the energy expenditure of the stimuli by accelerating the control process. This work may point to the potential value of model-based design of closed-loop controllers and pave the way towards the optimization of deep brain stimulation parameters and structures for Parkinsons disease.

Memory and computing function of four-node neuronal network motifs

Four-node neuronal network motifs are widespread in neural networks. Their dynamical and functional roles are studied in this paper. By computational modeling, firing-rate model and integrate-and-fire neuron model with the chemical coupling are used to model two typical four-node neuronal network motifs. Numerical results show that the structures of the motifs and the properties of every node play the significant roles in the dynamics and functions. By analyzing the impacts of the input current and the neuronal excitability, several interesting phenomena, such as acceleration and delay of response and long- and short-term memory, are observed. In addition, it is shown that the large time constants can prolong short-term memory which plays important roles in almost all neural computation and cognition task. Furthermore, these motifs can accomplish simple calculations of subtractors and comparators.