Real-time implementation of the Purkinje network on digital neuromorphic system

The Purkinje cells are vital nucleus responsible for the neural information processing in cerebellar network, which is important for human motor control. Digital neuromorphic implementation is a promising field, which uses spiking neural network to realize brain-inspired computation. In this study, a Purkinje network is implemented on a digital neuromorphic system LaCSNN. A set of efficient implementation methods are provided, and the firing dynamics can be regenerated in real time. The application of biologically inspired control is also discussed. The presented work is an important work towards the implementation of large-scale human brain network, and can be applied in various fields of real-time control area.

[1]  Michael Häusser,et al.  Membrane potential bistability is controlled by the hyperpolarization‐activated current IH in rat cerebellar Purkinje neurons in vitro , 2002, The Journal of physiology.

[2]  Jiang Wang,et al.  Digital implementations of thalamocortical neuron models and its application in thalamocortical control using FPGA for Parkinson's disease , 2016, Neurocomputing.

[3]  J Midtgaard,et al.  Synaptic control of excitability in turtle cerebellar Purkinje cells. , 1989, The Journal of physiology.

[4]  Bin Deng,et al.  Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties , 2017, Scientific Reports.

[5]  Frank C. Hoppensteadt,et al.  Bursts as a unit of neural information: selective communication via resonance , 2003, Trends in Neurosciences.

[6]  Bin Deng,et al.  Cost-efficient FPGA implementation of basal ganglia and their Parkinsonian analysis , 2015, Neural Networks.

[7]  Bin Deng,et al.  Cost-efficient FPGA implementation of a biologically plausible dopamine neural network and its application , 2018, Neurocomputing.

[8]  Ray W Turner,et al.  Firing dynamics of cerebellar purkinje cells. , 2007, Journal of neurophysiology.

[9]  J. Hounsgaard,et al.  Intrinsic determinants of firing pattern in Purkinje cells of the turtle cerebellum in vitro. , 1988, The Journal of physiology.

[10]  Bin Deng,et al.  FPGA implementation of hippocampal spiking network and its real-time simulation on dynamical neuromodulation of oscillations , 2017, Neurocomputing.

[11]  Bin Deng,et al.  Efficient hardware implementation of the subthalamic nucleus-external globus pallidus oscillation system and its dynamics investigation , 2017, Neural Networks.

[12]  Bin Deng,et al.  Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks , 2019, IEEE Transactions on Cybernetics.

[13]  R. Llinás,et al.  Electrophysiological properties of in vitro Purkinje cell dendrites in mammalian cerebellar slices. , 1980, The Journal of physiology.

[14]  H. Sompolinsky,et al.  Bistability of cerebellar Purkinje cells modulated by sensory stimulation , 2005, Nature Neuroscience.

[15]  G. Ermentrout,et al.  Analysis of neural excitability and oscillations , 1989 .

[16]  Bin Deng,et al.  Design of Hidden-Property-Based Variable Universe Fuzzy Control for Movement Disorders and Its Efficient Reconfigurable Implementation , 2019, IEEE Transactions on Fuzzy Systems.

[17]  Eugene M. Izhikevich,et al.  Neural excitability, Spiking and bursting , 2000, Int. J. Bifurc. Chaos.

[18]  Eugene M. Izhikevich,et al.  Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting , 2006 .