当前位置： SCI文献检索 > Journal of Neural Engineering期刊下所有文献 > Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

Abstract：

OBJECTIVE:Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). APPROACH:The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. MAIN RESULTS:This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. SIGNIFICANCE:This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

journal_name

J Neural Eng

journal_title

Journal of neural engineering

authors

Xu T,Xiao N,Zhai X,Kwan Chan P,Tin C

doi

10.1088/1741-2552/aa98e9

subject

Has Abstract

pub_date

2018-02-01 00:00:00

pages

016021

issue

eissn

1741-2560

issn

1741-2552

journal_volume

pub_type

杂志文章

在线工具

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.