Linked Life Data

19879294

Source:http://linkedlifedata.com/resource/pubmed/id/19879294

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-2-1
pubmed:abstractText
A practical brain-machine interface (BMI) requires real-time decoding algorithms to be realised in a portable device rather than a personal computer. In this article, a field-programmable gate array (FPGA) implementation of a probabilistic neural network (PNN) is proposed and developed to decode motor cortical ensemble recordings in rats performing a lever-pressing task for water rewards. A chronic 16-channel microelectrode array was implanted into the primary motor cortex of the rat to record neural activity, and the pressure signal of the lever were recorded simultaneously. To decode the pressure value from neural activity, both Matlab-based and FPGA-based mapping algorithms using a PNN were implemented and evaluated. In the FPGA architecture, training data of the network were stored in random access memory (RAM) blocks and multiply-add operations were realised by on-chip DSP48E slices. In the approximation of the activation function, a Taylor series and a look-up table (LUT) are used to achieve an accurate approximation. The results of FPGA implementation are as accurate as the realisation of Matlab, but the running speed is 37.9 times faster. This novel and feasible method indicates that the performance of current FPGAs is competent for portable BMI applications.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1872-678X
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
185
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
299-306
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Field-programmable gate array implementation of a probabilistic neural network for motor cortical decoding in rats.
pubmed:affiliation
Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, PR China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't