CN105160205A - LNC model based FPGA coding analysis platform for acupuncture neural electric signal - Google Patents

Abstract

The invention provides an LNC model based FPGA coding analysis platform for an acupuncture neural electric signal. The platform comprises three parts, i.e., an acupuncture experiment, a PC machine offline analysis part and an FPGA single neuron coder. Firstly, sample data of an acupuncture neural pathway under the stimulation effect is acquired through the acupuncture experiment; secondly, the PC machine offline analysis part preprocesses experiment data and estimates optimal parameters of an LNC model; and finally, the FPGA single neuron coder is constructed according to the optimal parameters of the LNC model. Different discharge states can be generated under the stimulation of different conditions by utilizing the FPGA coder, so that real neurons are simulated; and the real neurons and a simulation result of an upper computer are subjected to comparative analysis. The platform effectively combines experiment, software simulation and FPGA hardware simulation for making a series of researches of analysis, processing, modeling, prediction and the like of the acupuncture neural electric signal, and has important values for research of an acupuncture coding mechanism.

Description

Based on the acupuncture electroneurographic signal FPGA Coded Analysis platform of LNC model

Technical field

The present invention relates to biomedical engineering technology, particularly a kind of acupuncture electroneurographic signal FPGA encoding platform based on LNC model.

Background technology

Acupuncture is as the important component part of the traditional Chinese medical science, and clinical efficacy is remarkable.Needling action makes body produce a large amount of telecommunications breaths and chemical information, and both actings in conjunction realizations are to the integration of body function activity and regulation and control.Electroneurographic signal is the key signal that acupuncture plays effect, and the relation between Neural spike train activity and stimulation is the focus of neuro-physiology research.Domestic and international researchist adopts the method for the data analyses such as Nonlinear feature extraction to excavate its inherent law on the one hand, mechanistic exploration is carried out on the other hand from the angle of modeling, such as by setting up the bang path with plastic delay feed-forward network model simulation acupuncture electric signal and complex network mapping method etc., but how on earth nervous system encodes, acupuncture information is still undistinct.

Current neuron models mainly contain two classes, i.e. electrophysiological model and phenomenological model.Electrophysiological model can describe neuronic dynamic perfromance preferably, but complex forms, be unfavorable for setting up effective input-output mappings relation.Phenomenological model form simple and flexible, but physiological significance is indefinite.Existing neuron models all can not be used for specification and analysis acupuncture data all sidedly, and under same given stimulation, because the time change being subject to peripheral nerve metanetwork stimulates, neuronic discharge response has random character in essence.There are some researches show, it mononeuric input-output map relation nature is linear-nonlinear cascade connection, can by linear-non-linear cascade (LinearandNonlinearCascade, LNC) model is portrayed, and estimates that neuronic response exports successively by input stimulus through linear filter and non-linear conversion.Although LNC form is simple, but the substance much about biophysics and functional anatomy is covered, combine the feature of electrophysiological model and phenomenological model and there is random flash-over characteristic, being suitable for constructing the mapping relations between the input and output of acupuncture electric signal.

Field programmable gate array (FieldProgrammableGateArray, FPGA) technology comes into one's own gradually in the application in the computational neuroscience field taking biological nervous system as object.Relative to simulation system very flexible, the shortcomings such as the construction cycle is long, the features such as FPGA has that volume is little, density is high, computing velocity fast (flank speed can reach 150MHz), flexible in programming, amendment parameter convenience, low-power consumption, low cost, reconfigurable, high reliability.Application the FPGA of concurrent operation can realize computing and the specificity analysis of neuron and network, can realize running under yardstick in actual time, operation efficiency is high, be convenient to application, and integrated level is high, have broad application prospects in neuroid characteristic research, bionics, intelligent system and neurotherapeutic etc., therefore the FPGA hardware implementing of encoding for mononeuron is significant.

In Study on Acupuncture electroneurographic signal, still have the following disadvantages: existing technology there is no the simulation hardware single neuron model structure using FPGA to realize; Utilize merely matlab to realize the emulation that LNC etc. gives model, the actual nerve unit under acupuncture path cannot be simulated; Most research only emulates around neuron models, does not have rigorous acupuncture experimental design, cannot set up the neuron models under true experimental data, go forward side by side one-step prediction and analysis neuron encoding mechanism.

Summary of the invention

For the deficiency existed in above-mentioned technology, the object of this invention is to provide a kind of based on LNC can Reality simulation neuron coding FPGA platform, and design acupuncture experiment, experimental data is utilized to estimate the inner parameter of LNC model, and then the LNC mononeuron hardware encoding platform built under experimental data driving, for mononeuric encoding mechanism research provides rigorous solution.

For achieving the above object, the technical solution used in the present invention is to provide a kind of acupuncture electroneurographic signal FPGA Coded Analysis platform based on LNC model, wherein: this platform includes interconnective acupuncture experimental section, PC off-line analysis part, FPGA mononeuron scrambler, described acupuncture experimental section gathers experimental data sample, after the pre-service of PC off-line analysis part, using the parameter estimation foundation of pretreated experiment sample data as PC off-line analysis part, FPGA mononeuron scrambler is estimated to obtain model optimized parameter according to PC off-line analysis part, set up and configure the inner parameter of LNC model.

Described experimental section completes the collection of sample experimental data, and sample experimental data, after pre-service, passes to foundation and analysis that PC off-line analysis part carries out neuron models.

Described PC off-line analysis part includes host computer and matlab simulated environment.

Described epigynous computer section comprises parameter setting module, electric discharge contrast module and data analysis module; Parameter setting module emulates amplitude, the Frequency Index of input stimulus for arranging host computer matlab, and comprises GLM parameter estimation module; Electric discharge contrast module adopts graphic correlation matlab intuitively to emulate and FPGA emulation difference between the two; More data analysis module is used for emulating to matlab emulation and FPGA the Spike train obtained and does further statistical study.

Described matlab simulated environment is realized by matlab kit, neuron is put and a little regards point process as, the optimized parameter of LNC model under experimental data drives is estimated based on maximum likelihood rule, described LNC model is made up of linear filter and static non-linear function two parts, and two parts cascade produces mononeuric discharge rate index.

Described FPGA mononeuron scrambler includes convolution algorithm part and exponent arithmetic part, convolution algorithm part is responsible for the convolution algorithm in the linear filter in hardware implementing LNC model, the FFTIP core that the realization of convolution algorithm part can use Ahera to provide is to realize FFT and IFFT function, exponent arithmetic part is responsible for the static non-linear function computing in hardware implementing LNC model, can adopt the IP kernel of exponent arithmetic under Cordic algorithm.

The invention has the beneficial effects as follows field LNC single neuron model being applied to acupuncture electric signal coding study, and set up the hardware simulation platform based on FPGA.Advantage is: 1. this platform adopts LNC neuron models to describe the input/output relation of acupuncture electric signal, is of value to the essence disclosing acupuncture encoding mechanism.2. this platform adopts PC and FPGA mixing to build system platform, and PC is responsible for Pretreatment Test data under line and the optimized parameter of maximal possibility estimation LNC model, and FPGA platform is responsible for realizing real LNC mononeuron cataloged procedure.3. the mononeuron under the acupuncture nerve pathway under this platform adopts the FPGA of maximum operation frequency 200MHz to realize experimental data driving, concurrent operation ensures that film potential output frequency is within 1ms, fast operation, and has certain biophysics meaning.4.FPGA mononeuron scrambler at fast coding input signal under different stimulated, can produce the neural discharge information based on LNC model, and can pass PC back by USB communication modes, compares with line drag simulation result.

Accompanying drawing explanation

Fig. 1 is the FPGA Coded Analysis platform structure schematic diagram based on LNC of the present invention;

Fig. 2 is the structural representation of LNC model of the present invention;

Fig. 3 is the structured flowchart of FPGA mononeuron scrambler in the present invention;

Fig. 4 is the schematic diagram of host computer interface in the present invention.

Embodiment

Below in conjunction with accompanying drawing to of the present invention based on LNC can Reality simulation neuron coding FPGA platform structure be described further.

Of the present inventionly based on LNC can the design philosophy of FPGA platform of Reality simulation neuron coding be: as shown in Figure 1, first design acupuncture experiment 1, extract experiment sample data.Then, utilize PC off-line analysis part 2 to be processed data by matlab software, by maximum likelihood algorithm, estimate the basic parameter obtaining LNC model.Finally, utilize the IP kernel of FPGA, the data obtained using off-line analysis are as foundation, and allocation models parameter, builds FPGA mononeuron scrambler 3, complete the realization of final FPGA mononeuron encoding platform.

Acupuncture electroneurographic signal FPGA Coded Analysis platform based on LNC model of the present invention, this platform includes interconnective acupuncture experimental section 1, PC off-line analysis part 2, FPGA mononeuron scrambler 3, described acupuncture experimental section 1 gathers experimental data sample, after the pre-service of PC off-line analysis part 2, using the parameter estimation foundation of pretreated experiment sample data as PC off-line analysis part 2, FPGA mononeuron scrambler 3 is estimated to obtain model optimized parameter according to PC off-line analysis part 2, set up and configure the inner parameter of LNC model 4.

Described experimental section 1 completes the collection of sample experimental data, and sample experimental data, after pre-service, passes to foundation and analysis that PC off-line analysis part 2 carries out neuron models.

Described PC off-line analysis part 2 includes host computer and matlab simulated environment.

Described epigynous computer section comprises parameter setting module 12, electric discharge contrast module 14 and data analysis module 17; Parameter setting module 12 emulates amplitude, the Frequency Index of input stimulus for arranging host computer matlab, and comprises GLM parameter estimation module 13; Electric discharge contrast module 14 adopts graphic correlation matlab intuitively to emulate and FPGA emulation difference between the two; Data analysis module 17 does further statistical study for emulating to matlab emulation and FPGA the Spike train obtained more.

Described matlab simulated environment is realized by matlab kit, neuron is put and a little regards point process as, the optimized parameter of LNC model 4 under experimental data drives is estimated based on maximum likelihood rule, described LNC model 4 is made up of linear filter 7 and static non linear 8 function two parts, and two parts cascade produces mononeuric discharge rate index.

Described FPGA mononeuron scrambler 3 includes convolution algorithm part 4 and exponent arithmetic part 5, convolution algorithm part 4 is responsible for the convolution algorithm in the linear filter 7 in hardware implementing LNC model 4, the FFTIP core that the realization of convolution algorithm part 4 can use Ahera to provide is to realize FFT and IFFT function, exponent arithmetic part 5 is responsible for static non linear 8 functional operation in hardware implementing LNC model 4, can adopt the IP kernel of exponent arithmetic under Cordic algorithm.

Described acupuncture experimental section 1: the male rat of adult healthy can be adopted as acupuncture experimental subjects, utilize the point st 36 of needle stimulus rat.While acupuncture stimulation, operation centered by the L1 lumbar vertebrae of rat also fully exposes, be separated L4 Dorsal Root nerve tract and cut off at proximal part, isolate the neural pencil that receptive field is positioned at point st 36 district, pencil is taken and is placed on a pair bipolar platinum wire recording electrode, carry out telecommunications breath record with physiology electric information recorder MP150 (BIOPAC), can be used as the output response under acupuncture stimulation input signal.

Described PC off-line analysis part 2: this part is based on LNC model 4, and according to experiment sample data, by maximum likelihood rule, the Optimization Toolbox by matlab software realizes.Concrete computation process is as follows:

A given outside stimulus x (t), conditional intensity (discharge rate) function is

λ(t)＝exp(g.x+μ),(1)

Wherein g represents linear filter, and μ is Neural spike train rate base value.The conditional probability distribution that setting neuron models θ produces Spike train meets Poisson stochastic process, and definition plausibility function is

L＝∑logλ(t _sp)-∫λ(t)dt,(2)

Wherein t _sprepresent discharging time, consequent integration contains the overall time range applying to stimulate.When plausibility function L value is maximum, parameter θ corresponding under this kind of condition is the optimal estimation of LNC.

Described LNC model 4:LNC is the parametric family θ={ θ of a linear statistics model _i, regard inside neurons as black box, only pay close attention to the mapping relations between external input signal and output signal.Fig. 2 is the composition structure of LNC, and wherein linear filter 7 is for stimulating filtering, and be applied to input signal, higher-dimension sophisticated signal is converted into low dimension version to be transmitted backward, and filtered signal obtains instantaneous discharge rate via static non-linear function 8.The expression formula of LNC is:

r(t)＝F(g*x(t))(3)

Wherein g represents linear filter, and x (t) represents input signal, and F represents static non-linear function, is exponential function herein, and r (t) is discharge rate.

Described FPGA single neuron model part: as shown in Figure 3, mainly comprises two modules: convolution algorithm module 5 and exponent arithmetic module 6.A high performance device StratixII that FPGA can adopt altera corp to produce.This chip comprises 12 PLL able to programme, and has perfect Clock management and frequency synthesis ability, can support the high-speed-differential I/O signal up to lGb/s simultaneously, can meet the demand of high performance system.

The hard-wired Part I of described convolution algorithm module 5:LNC is the convolution algorithm that will complete input signal and stimulate between wave filter:

$g*x\left(t\right)={\∫}_0^{\mathrm{\∞}}g\left(\mathrm{\τ}\right)x(t-\mathrm{\τ})d_{\mathrm{\τ}}---\left(4\right)$

In practical application, for requirement of real time, according to time domain cyclic convolution theorem, after g (t) and x (t) is carried out zero padding discretize respectively, become x (n) and g (n), both linear convolutions can replace with cyclic convolution.As shown in Figure 3, both are carried out FFT computing 9 more respectively, obtain corresponding frequency domain response G (k) and X (k), the result be multiplied with G (k) by X (k) does IFFT computing 10 again, namely can obtain the convolution results y (n) of x (n) and g (n).In order to save the development time, the FFTIP core that Ahera can be used to provide is to realize FFT and IFFT function.

Described exponent arithmetic module 6: this module can adopt look-up table, but this method utilizes FPGA hardware implementing to get up to take up room large and precision is not high, so this patent utilizes Cordic algorithm to carry out exponent arithmetic.The optimized integration of Cordic algorithm simply adds and shifting function, and hardware implementing is very easy to.Its ultimate principle is:

Due to e ^x=sinh (x)+cosh (x), thus only need calculate sinh (x) and cosh (x).Again because:

$\begin{array}{c}\left[\begin{array}{c}\cosh (x\±y)\\ \sinh (x\±y)\end{array}\right]=\left[\begin{array}{cc}\cosh y& \±\sinh y\\ \±\sinh y& \cosh y\end{array}\right]\left[\begin{array}{c}\cosh x\\ \sinh x\end{array}\right]\\ =\cosh y\left[\begin{array}{cc}1& \tanh y\\ \±\tanh y& 1\end{array}\right]\left[\begin{array}{c}\cosh x\\ \sinh x\end{array}\right]\end{array}---\left(5\right)$

Y=artanh (2 is chosen in formula ^-i), then:

$\left[\begin{array}{c}\cosh (x\±y)\\ \sinh (x\±y)\end{array}\right]=\frac{1}{\sqrt{1-2^{-2i}}}\left[\begin{array}{cc}1& \±2^{-i}\\ \±2^{-i}& 1\end{array}\right]\left[\begin{array}{c}\cosh x\\ \sinh x\end{array}\right]---\left(6\right)$

Like this in order to calculate sinh (a) and cosh (a), just can a artanh (2 ^-i) cumulative sum be expressed as:

a＝∑d _iartanh2 ^-i(7)

Wherein d _ibe 1 or-1, get initial value sinh0=0 and cosh0=1, utilize above-mentioned iterative formula can calculate sinh (a) and cosh (a), and then calculate e ^x.According to above-mentioned principle, then can utilize totalizer, comparer sum counter builds FPGA hardware environment.In order to save the development time, the IP kernel 11 of Cordic also can be adopted directly to configure FPGA.

Described host computer: as shown in Figure 4, this part comprises parameter setting module 12, electric discharge contrast module 14, and data analysis module 17.Wherein, parameter setting module 12 emulates the index such as amplitude, frequency of input stimulus for arranging host computer matlab, and comprises GLM parameter estimation module 13; Electric discharge contrast module 14 adopts graphic correlation matlab intuitively to emulate and FPGA emulation difference between the two; Data analysis module 17 does further statistical study for emulating to matlab emulation and FPGA the Spike train obtained more.

The foregoing is only embodiments of the invention, not do any restriction to technical scope of the present invention, any trickle amendment utilizing instructions of the present invention and accompanying drawing to make Gu every, equivalency transform, include in claims of the present invention.

Claims (1)

1. the acupuncture electroneurographic signal FPGA Coded Analysis platform based on LNC model, it is characterized in that: this platform includes interconnective acupuncture experimental section (1), PC off-line analysis part (2), FPGA mononeuron scrambler (3), described acupuncture experimental section (1) gathers experimental data sample, after PC off-line analysis part (2) pre-service, using the parameter estimation foundation of pretreated experiment sample data as PC off-line analysis part (2), FPGA mononeuron scrambler (3) is estimated to obtain model optimized parameter according to PC off-line analysis part (2), set up and configure the inner parameter of LNC model (4),

Described experimental section (1) completes the collection of sample experimental data, and sample experimental data, after pre-service, passes to foundation and analysis that PC off-line analysis part (2) carries out neuron models;

Described PC off-line analysis part (2) includes host computer and matlab simulated environment;

Described epigynous computer section comprises parameter setting module (12), electric discharge contrast module (14) and data analysis module (17); Parameter setting module (12) emulates amplitude, the Frequency Index of input stimulus for arranging host computer matlab, and comprises GLM parameter estimation module (13); Electric discharge contrast module (14) adopts graphic correlation matlab intuitively to emulate and FPGA emulation difference between the two; Data analysis module (17) does further statistical study for emulating to matlab emulation and FPGA the Spike train obtained more;

Described matlab simulated environment is realized by matlab kit, neuron is put and a little regards point process as, the optimized parameter of LNC model (4) under experimental data drives is estimated based on maximum likelihood rule, described LNC model (4) is made up of linear filter (7) and static non linear (8) function two parts, and two parts cascade produces mononeuric discharge rate index;

Described FPGA mononeuron scrambler (3) includes convolution algorithm part (4) and exponent arithmetic part (5), convolution algorithm part (4) is responsible for the convolution algorithm in the linear filter (7) in hardware implementing LNC model (4), the FFTIP core that the realization of convolution algorithm part (4) can use Ahera to provide is to realize FFT and IFFT function, exponent arithmetic part (5) is responsible for static non linear (8) functional operation in hardware implementing LNC model (4), can adopt the IP kernel of exponent arithmetic under Cordic algorithm.