CN110097171B - Activity track positioning method and system based on scorpion micro-vibration positioning mechanism - Google Patents
Activity track positioning method and system based on scorpion micro-vibration positioning mechanism Download PDFInfo
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Abstract
The invention discloses an activity track positioning method and system based on a scorpion micro-vibration positioning mechanism, wherein the method comprises the following steps: establishing a primary neuron model simulating a scorpion sensory neuron, and converting a vibration signal received by a sensor into a pulse signal; establishing a plastic synapse model simulating a scorpion synapse, and obtaining synaptic conductance according to a pulse signal; and establishing a secondary neuron model, and estimating the direction of the vibration source according to the synaptic conductance emission secondary neuron pulse. The invention simulates scorpion to accurately position prey, which is a positioning technology of biological function. The pulse neural network is used for carrying out combined coding on the vibration signals reaching different receivers, and information transmission between the neurons is realized by establishing synaptic connection between the neurons, so that the azimuth information of the vibration source signal is obtained.
Description
Technical Field
The invention relates to the technical field of bionic perception and information processing, in particular to a method and a system for positioning an activity track based on a scorpion micro-vibration positioning mechanism.
Background
Positioning is one of the core technologies for location services, everything interconnection, artificial intelligence, and future super-intelligent (robot + human) applications. Currently, the most widely used position service system is the Global Navigation Satellite System (GNSS), and the dynamic positioning accuracy can reach sub-meter level in an outdoor open environment. However, the satellite signals are affected by adverse factors such as blockage and multipath propagation, so that the global satellite navigation system cannot achieve a good positioning effect in the "urban canyon" erected in a high-rise building or in the indoor of a building, thereby limiting the use and development of the system in an indoor environment. People's daily activities are done indoors for more than 80% of the time, and thus, many different indoor positioning schemes have been developed. In the prior art, the traditional indoor positioning method has the disadvantages of multiple data processing steps, long positioning time and low positioning speed.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
The invention provides a method and a system for positioning an activity track based on a scorpion micro-vibration positioning mechanism, aiming at solving the problem of slow positioning speed of the traditional indoor positioning method in the prior art.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a method for positioning an activity track based on a scorpion micro-vibration positioning mechanism comprises the following steps:
establishing a primary neuron model simulating a scorpion sensory neuron, and converting a vibration signal received by a sensor into a pulse signal;
establishing a plastic synapse model simulating a scorpion synapse, and obtaining synaptic conductance according to a pulse signal;
and establishing a secondary neuron model, and estimating the direction of the vibration source according to the synaptic conductance emission secondary neuron pulse.
The activity track positioning method based on the scorpion micro-vibration positioning mechanism is characterized in that a primary neuron model imitating a scorpion sensory neuron is established, and a vibration signal received by a sensor is converted into a pulse signal, and comprises the following steps:
determining the preference direction of the neuron according to the distribution of biological receptors on the body surface of the scorpion;
calculating phase information of the vibration signal received by the sensor;
obtaining a pulse emission intensity function according to the phase information;
establishing a plurality of Poisson neurons for the vibration signals, and carrying out pulse emission according to a pulse emission intensity function;
and obtaining a Poisson neuron pulse queue obeying Poisson distribution according to the pulse emission probability.
The activity track positioning method based on the scorpion micro-vibration positioning mechanism is characterized in that the phase information is
Wherein, yk(t) is the vibration signal received by the kth sensor at time t, k is 1,2, …, 8;
the pulse emission intensity function is
λk(t)＝2πλ0p(θk(t))
Wherein λ is0Representing the mean pulse emission, p (-) representing the von mises distribution, α being a distribution concentration parameter, B0(α) a zero order modified Bessel function of α;
the pulse transmission probability is
Pk(t)＝λk(t)Δt exp(-λk(t)Δt)
Where Δ t represents the time period for which 1 pulse is transmitted;
the pulse queue of the Poisson neuron is
Wherein m represents the number of Poisson neurons,
denotes yk(t) the time of transmission of the jth pulse of the ith Poisson neuron,
denotes yk(t) the number of pulses of the pulse train emitted by the l-th Poisson neuron.
The activity track positioning method based on the scorpion microvibration positioning mechanism is characterized in that a plastic synapse model imitating a scorpion synapse is established, and synaptic conductance is obtained according to a pulse signal, and comprises the following steps:
queue the first pulse
As excitatory input and post-synaptic pulses, a second pulse train
Synaptic plasticity modification function M as an input to suppress and pre-synaptic pulses and to establish a first pulse traink(t) a synaptic plasticity modification function Q (t) for decreasing the synaptic strength, establishing a second pulse train for increasing the synaptic strength, exponentially decaying to
Wherein the content of the first and second substances,
number in reverse direction, τ, representing k-Time constant, τ, representing a decrease in synaptic plasticity+Time constant, M, representing enhanced synaptic plasticityk(t) denotes a first pulseA synaptic plasticity correction function of the queue, Q (t) representing a synaptic plasticity correction function of the second pulse queue, Q (t) being in particular
When a presynaptic pulse is received at synapse at time t, the synaptic plasticity correction function M of the first pulse queuek(t) decreasing A-Value of change in synaptic conductance gaIncrease Mk(t)gmaxIf g isaIf < 0, g isaSet to 0; when the synapse receives a post-synaptic pulse at time t, the synaptic plasticity correction function Q (t) of the second pulse train is increased by A+Value of change in synaptic conductance gaIncreasing Q (t) gmaxIf g isa＞gmaxThen g isaIs set as gmaxWherein g ismaxIs gaThe maximum amount of change of;
when an excitatory synapse receives a presynaptic pulse, the excitatory synapse conducts gex(t) increase in gmax(ii) a Inhibiting synaptic conductance g when the inhibiting synapse receives a pre-or post-synaptic pulsein(t) increase in ga(ii) a Excitatory synapse conductance g when excitatory synapse and inhibitory synapse do not receive a signalex(t) and inhibition of synaptic conductance gin(t) decay exponentially.
The method for positioning the activity track based on the scorpion microvibration positioning mechanism, wherein a secondary neuron model is established, and the vibration source direction is estimated by transmitting a secondary neuron pulse according to the synaptic conductance, comprises the following steps:
calculating the membrane potential of the LIF neuron according to the synaptic conductance, and when the membrane potential reaches a preset threshold value, the LIF neuron transmits a pulse to obtain a pulse queue of the LIF neuron;
the membrane potential is lowered to a resting potential and accumulation of the membrane potential is resumed;
and positioning by adopting group vector coding to obtain the orientation of the vibration source.
The activity track positioning method based on the scorpion micro-vibration positioning mechanism is characterized in that the membrane potential satisfies the following formula
Wherein V represents a membrane potential, VrestDenotes the resting potential,. tau.mDenotes the time constant, Eex、EinReference potentials, g, for excitatory and inhibitory inputs, respectivelyex(t)、gin(t) excitatory synaptic conductance and inhibitory synaptic conductance, respectively;
the pulse train of the LIF neuron is
Wherein the content of the first and second substances,
denotes yk(t) the firing time of the jth pulse of the LIF neuron,
denotes yk(t) the number of pulses of the pulse train emitted by the LIF neuron.
The activity track positioning method based on the scorpion micro-vibration positioning mechanism is characterized in that the vibration source is oriented to
Wherein arg (. cndot.) represents the argument of a complex number, γkRepresenting the angle of the sensor and i representing the imaginary unit.
The method for positioning the activity track based on the scorpion micro-vibration positioning mechanism is characterized in that the sensors adopt acceleration sensors, 8 acceleration sensors are distributed on the same circle to form a sensor array, and the angles of the 8 acceleration sensors are +/-18 degrees, +/-54 degrees, +/-90 degrees and +/-140 degrees respectively.
The activity track positioning method based on the scorpion micro-vibration positioning mechanism is characterized in that α is 1.5157,m＝8，λ0＝1000，Vrest＝-70mV，τm＝20ms，Eex＝0mV，Ein-70mV, the predetermined threshold value being Vthre＝-45mV，A+＝0.2，A-＝1.05A+，τ+＝τ-＝20ms，gmax＝0.1nS。
An activity track positioning system based on a scorpion micro-vibration positioning mechanism comprises: a processor, and a memory coupled to the processor,
the memory stores an activity track positioning program based on a scorpion micro-vibration positioning mechanism, and when the activity track positioning program based on the scorpion micro-vibration positioning mechanism is executed by the processor, the following steps are realized:
establishing a primary neuron model simulating a scorpion sensory neuron, and converting a vibration signal received by a sensor into a pulse signal;
establishing a plastic synapse model simulating a scorpion synapse, and obtaining synaptic conductance according to a pulse signal;
and establishing a secondary neuron model, and estimating the direction of the vibration source according to the synaptic conductance emission secondary neuron pulse.
Has the advantages that: the invention simulates scorpion to accurately position prey, which is a positioning technology of biological function. The pulse neural network is used for carrying out combined coding on the vibration signals reaching different receivers, and information transmission between the neurons is realized by establishing synaptic connection between the neurons, so that the azimuth information of the vibration source signal is obtained.
Drawings
FIG. 1 is a schematic structural diagram of a vibration signal acquisition device for locating a prey based on a bionic scorpion.
Fig. 2 is a schematic diagram of vibration signal data acquisition information.
Fig. 3A is a graph of vibration signals collected in the present invention.
Fig. 3B is a pulse train diagram of the vibration signal in the present invention.
Fig. 3C is a diagram of the pulse signal in the present invention.
FIG. 4 is a graph showing the results of the localization experiment in the present invention.
FIG. 5 is a flow chart of the method for positioning the trajectory of a scorpion based on the micro-vibration positioning mechanism.
FIG. 6 is a functional block diagram of the scorpion micro-vibration positioning mechanism-based activity track positioning system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1-6, the present invention provides embodiments of a method for positioning an activity track based on a scorpion micro-vibration positioning mechanism.
The positioning method is realized based on a vibration signal acquisition device for positioning a prey by a bionic scorpion, and as shown in figure 1, the acquisition device comprises: the sensor comprises a sensor array 1, a constant current adapter 2 connected with the sensor array 1, an AD acquisition module 3 connected with the constant current adapter 2, a development board 4 connected with the AD acquisition module 3, a signal transmission module 5 connected with the development board 4 and a computer 6 connected with the signal transmission module 5. The sensor array is composed of eight acceleration sensors, the sensitivity of the sensors is 2V/g, the resolution is 0.06mg, and the detection range is-2.5 g to +2.5g (the output voltage is-5V to + 5V). The sensor collects Rayleigh waves within the frequency range of 0.1Hz to 700 Hz. The sensor size is 32 mm phi and 30mm phi, and the weight is 180 g. The sensor is supplied with 24V power supply voltage by the constant current adapter, and transmits signals of 8 channels to the AD acquisition module through the constant current adapter. The constant current adapter is powered by 220V alternating current. An AD conversion chip in the AD acquisition module is AD7606, and can convert 8-channel analog signals and 16-bit bipolar high-precision resolution. The development board adopts the STM32F103 chip (STMicroelectronics) to gather vibration signal, passes through signal transmission module (W5500) with vibration signal and transmits the host computer, finally with signal storage in the host computer.
As shown in fig. 5, the method for positioning the activity track based on the scorpion micro-vibration positioning mechanism of the present invention comprises the following steps:
and S100, establishing a primary neuron model imitating a scorpion sensory neuron, and converting a vibration signal received by a sensor into a pulse signal.
Specifically, the step S100 specifically includes:
and step S110, determining the preference direction of the neurons according to the distribution of the biological receptors on the body surfaces of the scorpions.
The vibration signal is collected by the vibration signal collecting device shown in figure 1, the distribution mode of biological receptors on the body surfaces of scorpions is simulated, 8 acceleration sensors are arranged in a circular mode to form a sensor array, the serial number k is clockwise 1,2, 8, and the vibration signal y is collectedk(t), the angle of the sensor is the angle gamma of the biological sensors on the body surface of the scorpionk± 18 °, ± 54 °, ± 90 °, ± 140 °. Vibration data acquisition is shown in fig. 2, with an inner circle of a circular array of 8 sensors, measured as gammakThe angle is arranged (k is 1,2, …,8), and the vibration signal y received by the sensork(k ═ 1, 2.., 8). The excircle represents the position of 8 acquisition points, vibration signal acquisition is respectively carried out on the acquisition points, the vibration signals are signals generated by stepping for 1min in situ by one person, stepping signal acquisition is respectively carried out in 8 positions of-180 degrees, -135 degrees, -90 degrees, -45 degrees, 0 degrees, 45 degrees and 90 degrees, and 8 groups of data are totally included, one group of data comprises a group of yk(k ═ 1,2, …,8) signals. Defining m first-level Poisson neurons and 1 second-level LIF (accumulated discharge with leakage current) neuron for each vibration sensor, wherein the preference direction of the neurons is gammak。
And step S120, calculating phase information of the vibration signal received by the sensor.
The phase information is
Wherein, ykAnd (t) is a vibration signal received by the kth sensor at the time t, wherein k is 1,2, … and 8.
And step S130, obtaining a pulse emission intensity function according to the phase information.
The pulse emission intensity function is
λk(t)＝2πλ0p(θk(t))
Wherein λ is0Representing the mean pulse emission, p (-) representing the von mises distribution, α being a distribution concentration parameter, B0(α) zero order modified Bessel function α.
And S140, establishing a plurality of Poisson neurons for the vibration signals, and carrying out pulse emission according to the pulse emission intensity function.
Specifically, for the k-th vibration signal yk(t) establishing m Poisson neurons, and carrying out pulse emission.
And S150, obtaining a Poisson neuron pulse queue obeying Poisson distribution according to the pulse emission probability.
The pulse transmission probability is
Pk(t)＝λk(t)Δt exp(-λk(t)Δt)
Where Δ t represents the time period for which 1 pulse is transmitted.
The pulse queue of the Poisson neuron is
Wherein m represents the number of Poisson neurons,
denotes yk(t) the time of transmission of the jth pulse of the ith Poisson neuron,
to representyk(t) the number of pulses of the pulse train emitted by the l-th Poisson neuron. The pulse encoding result of the poisson neuron of the vibration signal collected by one sensor is shown in fig. 3 (including fig. 3A, 3B and 3C), so that the position information of each step of a group of step signals is converted into a pulse signal, and the position information is transmitted in a pulse form.
And S200, establishing a plastic synapse model simulating a scorpion synapse, and obtaining synaptic conductance according to the pulse signal.
Specifically, the step S200 specifically includes:
step S210, queue the first pulse
As excitatory input and post-synaptic pulses, a second pulse train
Synaptic plasticity modification function M as an input to suppress and pre-synaptic pulses and to establish a first pulse traink(t) a synaptic plasticity modification function Q (t) for decreasing the synaptic strength, establishing a second pulse train for increasing the synaptic strength, exponentially decaying to
Wherein the content of the first and second substances,
number in reverse direction, τ, representing k-Time constant, τ, representing a decrease in synaptic plasticity+Time constant, M, representing enhanced synaptic plasticityk(t) denotes a synaptic plasticity modification function of the first pulse train, Q (t) denotes a synaptic plasticity modification function of the second pulse train, and Q (t) is specifically
Step S220, when the synapse receives a presynaptic pulse at time t, the synaptic plasticity correction function M of the first pulse queuek(t) decreasing A-I.e. the synaptic plasticity correction function M of the first pulse traink(t) synaptic plasticity correction function M 'of the first pulse queue at a previous time instant'k(t) reducing A-It can be expressed as the following formula: mk(t)＝M'k(t)-A-Value of change in synaptic conductance gaIncrease Mk(t)gmaxI.e. the value of change in synaptic conductance gaValue g 'of change in synaptic conductance at the previous moment'aOn the basis of (1) reducing Mk(t)gmaxIt can be expressed as the following formula: ga＝g'a+Mk(t)gmaxIf g isaIf < 0, then gaIs set to 0, i.e. g a0; when the synapse receives a post-synaptic pulse at time t, the synaptic plasticity correction function Q (t) of the second pulse train is increased by A+That is, the synaptic plasticity correction function Q (t) of the second pulse train is increased by A on the basis of the synaptic plasticity correction function Q' (t) of the second pulse train at the previous time+It can be expressed as the following formula: q (t) ═ Q' (t) + a+Value of change in synaptic conductance gaIncreasing Q (t) gmaxI.e. the value of change in synaptic conductance gaValue g 'of change in synaptic conductance at the previous moment'aAdding Q (t) gmaxIt can be expressed as the following formula: ga＝g'a+Q(t)gmaxIf g isa＞gmaxThen g isaIs set as gmaxI.e. ga＝gmaxWherein g ismaxIs gaThe maximum amount of change. A _ and A+The parameters of the synaptic plasticity correction function of the first pulse queue and the parameters of the synaptic plasticity correction function of the second pulse queue respectively represent the maximum variation of the function.
Step S230, when the excitatory synapse receives a pre-synaptic pulse, the excitatory synapse conductance gex(t) increase in gmaxInstant spurtingConductance g of contactex(t) excitatory synaptic conductance at the previous moment g'ex(t) addition of gmaxIt can be expressed as the following formula: gex(t)＝g'ex(t)+gmax(ii) a Inhibiting synaptic conductance g when the inhibiting synapse receives a pre-or post-synaptic pulsein(t) increase in gaI.e. inhibition of synaptic conductance gin(t) inhibition of synaptic conductance at the previous moment g'in(t) addition of gaIt can be expressed as the following formula: gin(t)＝g'in(t)+ga(ii) a Excitatory synapse conductance g when excitatory synapse and inhibitory synapse do not receive a signalex(t) and inhibition of synaptic conductance gin(t) decay exponentially, the decay index being:
wherein, tauexDenotes the excitatory synaptic time constant, τinIndicating the inhibitory synaptic time constant.
And S300, establishing a secondary neuron model, and estimating the direction of the vibration source according to the synaptic conductance emission secondary neuron pulse.
Specifically, the step S300 specifically includes:
step S310, calculating membrane potential of the LIF neuron according to synaptic conductance, and when the membrane potential reaches a preset threshold value, the LIF neuron emits pulses to obtain a pulse queue of the LIF neuron.
The membrane potential satisfies the following formula
Wherein V represents a membrane potential, VrestDenotes the resting potential,. tau.mDenotes the time constant, Eex、EinReference potentials, g, for excitatory and inhibitory inputs, respectivelyex(t)、gin(t) excitatory synaptic conductance and inhibitory synaptic conductance, respectively.
The pulse train of the LIF neuron is
Wherein the content of the first and second substances,
denotes yk(t) the firing time of the jth pulse of the LIF neuron,
denotes yk(t) the number of pulses of the pulse train emitted by the LIF neuron.
Step S320, the membrane potential is reduced to the rest potential, and the accumulation of the membrane potential is restarted.
After the pulse train of the LIF neurons is obtained, the membrane potential is dropped to the resting potential, and after the refractory period is over, the membrane potential starts to accumulate again.
And S330, positioning by adopting group vector coding to obtain the orientation of the vibration source.
The vibration source has an orientation of
Wherein arg (. cndot.) represents the argument of a complex number, γkRepresenting the angle of the sensor and i representing the imaginary unit.
In particular, vibration events are located using group vector coding, definition
Where n is the mode length, phi is the direction, exp (i phi) ═ cos phi + i sin phi, exp (i gamma)k)＝cosγk+isinγkThe phase angle phi represents the positioning angle obtained after the group vector processing, i.e.
Therefore, the position information of the step is decoded by the group vector algorithm, and the position information is read.
It is worth to be noted that the present invention simulates the scorpion to accurately locate prey, which is a biological function location technology. The pulse neural network is used for carrying out combined coding on the vibration signals reaching different receivers, and information transmission between the neurons is realized by establishing synaptic connection between the neurons, so that the azimuth information of the vibration source signal is obtained.
Detailed description of the preferred embodiment
Setting parameters α -1.5157, m-8, λ0＝1000、Vrest＝-70mV、τm＝τex＝τin＝20ms、Eex＝0mV、Ein70mV, with a predetermined threshold value of Vthre＝-45mV、A+＝0.2、A-＝1.05A+、τ+＝τ-＝20ms、gmax0.1 nS. The results of reading the position information of the 8 sets of stepping data acquired by the experiment, that is, the estimation results of the positioning angles of the vibration source signals are shown in fig. 4.
Based on the method, the invention also provides a preferable embodiment of the activity track positioning system based on the scorpion micro-vibration positioning mechanism, which comprises the following steps:
as shown in fig. 6, the activity track positioning system based on the scorpion micro-vibration positioning mechanism in the embodiment of the present invention includes: a processor 10, and a memory 20 connected to said processor 10,
the memory 20 stores an activity track positioning program based on a scorpion micro-vibration positioning mechanism, and when the activity track positioning program based on the scorpion micro-vibration positioning mechanism is executed by the processor 10, the following steps are realized:
establishing a primary neuron model simulating a scorpion sensory neuron, and converting a vibration signal received by a sensor into a pulse signal;
establishing a plastic synapse model simulating a scorpion synapse, and obtaining synaptic conductance according to a pulse signal;
and establishing a secondary neuron model, and estimating the direction of the vibration source according to the synaptic conductance emission secondary neuron pulse, which is specifically described above.
When the activity track positioning program based on the scorpion micro-vibration positioning mechanism is executed by the processor 10, the following steps are also realized:
determining the preference direction of the neuron according to the distribution of biological receptors on the body surface of the scorpion;
calculating phase information of the vibration signal received by the sensor;
obtaining a pulse emission intensity function according to the phase information;
establishing a plurality of Poisson neurons for the vibration signals, and carrying out pulse emission according to a pulse emission intensity function;
and obtaining a pulse queue of the Poisson neurons obeying the Poisson distribution according to the pulse emission probability, which is specifically described above.
In the activity track positioning system based on the scorpion micro-vibration positioning mechanism, the phase information is
Wherein, yk(t) is the vibration signal received by the kth sensor at time t, k is 1,2, …, 8;
the pulse emission intensity function is
λk(t)＝2πλ0p(θk(t))
Wherein λ is0Representing the mean pulse emission, p (-) representing the von mises distribution, α being a distribution concentration parameter, B0(α) a zero order modified Bessel function of α;
the pulse transmission probability is
Pk(t)＝λk(t)Δt exp(-λk(t)Δt)
Where Δ t represents the time period for which 1 pulse is transmitted;
the pulse queue of the Poisson neuron is
Wherein m represents the number of Poisson neurons,
denotes yk(t) the time of transmission of the jth pulse of the ith Poisson neuron,
denotes yk(t) the number of pulses of the pulse train emitted by the ith poisson neuron, as described above.
When the activity track positioning program based on the scorpion micro-vibration positioning mechanism is executed by the processor 10, the following steps are also realized:
queue the first pulse
As excitatory input and post-synaptic pulses, a second pulse train
Synaptic plasticity modification function M as an input to suppress and pre-synaptic pulses and to establish a first pulse traink(t) a synaptic plasticity modification function Q (t) for decreasing the synaptic strength, establishing a second pulse train for increasing the synaptic strength, exponentially decaying to
Wherein the content of the first and second substances,
number in reverse direction, τ, representing k-Time-constant indicative of reduced synaptic plasticityNumber, tau+Time constant, M, representing enhanced synaptic plasticityk(t) denotes a synaptic plasticity modification function of the first pulse train, Q (t) denotes a synaptic plasticity modification function of the second pulse train, and Q (t) is specifically
When a presynaptic pulse is received at synapse at time t, the synaptic plasticity correction function M of the first pulse queuek(t) decreasing A-Value of change in synaptic conductance gaIncrease Mk(t)gmaxIf g isaIf < 0, g isaSet to 0; when the synapse receives a post-synaptic pulse at time t, the synaptic plasticity correction function Q (t) of the second pulse train is increased by A+Value of change in synaptic conductance gaIncreasing Q (t) gmaxIf g isa＞gmaxThen g isaIs set as gmaxWherein g ismaxIs gaThe maximum amount of change of;
when an excitatory synapse receives a presynaptic pulse, the excitatory synapse conducts gex(t) increase in gmax(ii) a Inhibiting synaptic conductance g when the inhibiting synapse receives a pre-or post-synaptic pulsein(t) increase in ga(ii) a Excitatory synapse conductance g when excitatory synapse and inhibitory synapse do not receive a signalex(t) and inhibition of synaptic conductance gin(t) all decay exponentially, as described above.
When the activity track positioning program based on the scorpion micro-vibration positioning mechanism is executed by the processor 10, the following steps are also realized:
calculating the membrane potential of the LIF neuron according to the synaptic conductance, and when the membrane potential reaches a preset threshold value, the LIF neuron transmits a pulse to obtain a pulse queue of the LIF neuron;
the membrane potential is lowered to a resting potential and accumulation of the membrane potential is resumed;
and positioning by adopting group vector coding to obtain the orientation of the vibration source, which is specifically described above.
In the activity track positioning system based on the scorpion micro-vibration positioning mechanism, the membrane potential satisfies the following formula
Wherein V represents a membrane potential, VrestDenotes the resting potential,. tau.mDenotes the time constant, Eex、EinReference potentials, g, for excitatory and inhibitory inputs, respectivelyex(t)、gin(t) excitatory synaptic conductance and inhibitory synaptic conductance, respectively;
the pulse train of the LIF neuron is
Wherein the content of the first and second substances,
denotes yk(t) the firing time of the jth pulse of the LIF neuron,
denotes yk(t) the number of pulses in the pulse train emitted by the LIF neuron, as described above.
In the activity track positioning system based on the scorpion micro-vibration positioning mechanism, the vibration source is oriented in
Wherein arg (. cndot.) represents the argument of a complex number, γkRepresenting the angle of the sensor and i representing the imaginary unit, as described above.
In the activity track positioning system based on the scorpion micro-vibration positioning mechanism, the sensors adopt acceleration sensors, 8 acceleration sensors are distributed on the same circle to form a sensor array, and the angles of the 8 acceleration sensors are respectively +/-18 degrees, +/-54 degrees, +/-90 degrees and +/-140 degrees.
Positioning based on micro-vibration of scorpionsIn the mechanism motion track positioning system, α is 1.5157, m is 8, lambda0＝1000，Vrest＝-70mV，τm＝τex＝τin＝20ms，Eex＝0mV，Ein-70mV, the predetermined threshold value being Vthre＝-45mV，A+＝0.2，A-＝1.05A+，τ+＝τ-＝20ms，gmax＝0.1nS。
In summary, the method and system for positioning the activity track based on the scorpion micro-vibration positioning mechanism provided by the invention comprises the following steps: establishing a primary neuron model simulating a scorpion sensory neuron, and converting a vibration signal received by a sensor into a pulse signal; establishing a plastic synapse model simulating a scorpion synapse, and obtaining synaptic conductance according to a pulse signal; and establishing a secondary neuron model, and estimating the direction of the vibration source according to the synaptic conductance emission secondary neuron pulse. The invention simulates scorpion to accurately position prey, which is a positioning technology of biological function. The pulse neural network is used for carrying out combined coding on the vibration signals reaching different receivers, and information transmission between the neurons is realized by establishing synaptic connection between the neurons, so that the azimuth information of the vibration source signal is obtained.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (7)
1. A method for positioning an activity track based on a scorpion micro-vibration positioning mechanism is characterized by comprising the following steps:
establishing a primary neuron model simulating a scorpion sensory neuron, and converting a vibration signal received by a sensor into a pulse signal;
establishing a plastic synapse model simulating a scorpion synapse, and obtaining synaptic conductance according to a pulse signal;
establishing a secondary neuron model, and estimating a vibration source direction according to a synaptic conductance emission secondary neuron pulse;
the establishing of the first-level neuron model imitating the scorpion sensory neurons converts the vibration signals received by the sensors into pulse signals, and the establishing method comprises the following steps:
determining the preference direction of the neuron according to the distribution of biological receptors on the body surface of the scorpion;
calculating phase information of the vibration signal received by the sensor;
obtaining a pulse emission intensity function according to the phase information;
establishing a plurality of Poisson neurons for the vibration signals, and carrying out pulse emission according to a pulse emission intensity function;
obtaining a Poisson neuron pulse queue obeying Poisson distribution according to the pulse emission probability;
the phase information is
Wherein, yk(t) is the vibration signal received by the kth sensor at time t, k is 1,2, …, 8;
the pulse emission intensity function is
λk(t)＝2πλ0p(θk(t))
Wherein λ is0Representing the mean pulse emission, p (-) representing the von mises distribution, α being a distribution concentration parameter, B0(α) a zero order modified Bessel function of α;
the pulse transmission probability is
Pk(t)＝λk(t)Δtexp(-λk(t)Δt)
Where Δ t represents the time period for which 1 pulse is transmitted;
the pulse queue of the Poisson neuron is
Wherein m represents the number of Poisson neurons,
denotes yk(t) the time of transmission of the jth pulse of the ith Poisson neuron,
denotes yk(t) the number of pulses in the pulse train emitted by the first poisson neuron;
the establishing of the plastic synapse model imitating the scorpion synapse and the obtaining of the synapse conductance according to the pulse signal comprise the following steps:
queue the first pulse
As excitatory input and post-synaptic pulses, a second pulse train
Synaptic plasticity modification function M as an input to suppress and pre-synaptic pulses and to establish a first pulse traink(t) a synaptic plasticity modification function Q (t) for decreasing the synaptic strength, establishing a second pulse train for increasing the synaptic strength, exponentially decaying to
Wherein the content of the first and second substances,
number in reverse direction, τ, representing k-Time constant, τ, representing a decrease in synaptic plasticity+Time constant, M, representing enhanced synaptic plasticityk(t) denotes a synaptic plasticity modification function of the first pulse train, Q (t) denotes a synaptic plasticity modification function of the second pulse train, and Q (t) is specifically
When a presynaptic pulse is received at synapse at time t, the synaptic plasticity correction function M of the first pulse queuek(t) decreasing A-Increase in synaptic conductance change value ga by Mk(t)gmaxIf g isa<0, then gaSet to 0; when the synapse receives a post-synaptic pulse at time t, the synaptic plasticity correction function Q (t) of the second pulse train is increased by A+Value of change in synaptic conductance gaIncreasing Q (t) gmaxIf g isa>gmaxThen g isaIs set as gmaxWherein g ismaxIs gaThe maximum amount of change of;
when an excitatory synapse receives a presynaptic pulse, the excitatory synapse conducts gex(t) increase in gmax(ii) a Inhibiting synaptic conductance g when the inhibiting synapse receives a pre-or post-synaptic pulsein(t) increase in ga(ii) a Excitatory synapse conductance g when excitatory synapse and inhibitory synapse do not receive a signalex(t) and inhibition of synaptic conductance gin(t) decay exponentially.
2. The method of claim 1, wherein the establishing of the secondary neuron model and the estimation of the vibration source orientation according to the synaptic conductance emitted secondary neuron pulse comprises:
calculating the membrane potential of the LIF neuron according to the synaptic conductance, and when the membrane potential reaches a preset threshold value, the LIF neuron transmits a pulse to obtain a pulse queue of the LIF neuron;
the membrane potential is lowered to a resting potential and accumulation of the membrane potential is resumed;
and positioning by adopting group vector coding to obtain the orientation of the vibration source.
3. The method of claim 2, wherein the membrane potential satisfies the following formula
Wherein V represents a membrane potential, VrestDenotes the resting potential,. tau.mDenotes the time constant, Eex、EinReference potentials, g, for excitatory and inhibitory inputs, respectivelyex(t)、gin(t) excitatory synaptic conductance and inhibitory synaptic conductance, respectively;
the pulse train of the LIF neuron is
Wherein the content of the first and second substances,
denotes yk(t) the firing time of the jth pulse of the LIF neuron,
denotes yk(t) the number of pulses of the pulse train emitted by the LIF neuron.
4. The method of claim 3, wherein the direction of the vibration source is
Wherein arg (. cndot.) represents the argument of a complex number，γkRepresenting the angle of the sensor and i representing the imaginary unit.
5. The method according to claim 4, wherein the sensors are acceleration sensors, 8 acceleration sensors are distributed on the same circle to form a sensor array, and the angles of the 8 acceleration sensors are ± 18 °, ± 54 °, ± 90 °, ± 140 °, respectively.
6. The method as claimed in claim 5, wherein α is 1.5157, m is 8, λ is0＝1000，Vrest＝-70mV，τm＝20ms，Eex＝0mV，Ein-70mV, the predetermined threshold value being Vthre＝-45mV，A+＝0.2，A-＝1.05A+，τ+＝τ-＝20ms，gmax＝0.1nS。
7. A moving track positioning system based on a scorpion micro-vibration positioning mechanism is characterized by comprising: a processor, and a memory coupled to the processor,
the memory stores an activity track positioning program based on a scorpion micro-vibration positioning mechanism, and when the activity track positioning program based on the scorpion micro-vibration positioning mechanism is executed by the processor, the following steps are realized:
establishing a primary neuron model simulating a scorpion sensory neuron, and converting a vibration signal received by a sensor into a pulse signal;
establishing a plastic synapse model simulating a scorpion synapse, and obtaining synaptic conductance according to a pulse signal;
establishing a secondary neuron model, and estimating a vibration source direction according to a synaptic conductance emission secondary neuron pulse;
when the activity track positioning program based on the scorpion micro-vibration positioning mechanism is executed by the processor, the following steps are also realized:
determining the preference direction of the neuron according to the distribution of biological receptors on the body surface of the scorpion;
calculating phase information of the vibration signal received by the sensor;
obtaining a pulse emission intensity function according to the phase information;
establishing a plurality of Poisson neurons for the vibration signals, and carrying out pulse emission according to a pulse emission intensity function;
obtaining a Poisson neuron pulse queue obeying Poisson distribution according to the pulse emission probability;
the phase information is
Wherein, yk(t) is the vibration signal received by the kth sensor at time t, k is 1,2, …, 8;
the pulse emission intensity function is
λk(t)＝2πλ0p(θk(t))
Wherein λ is0Representing the mean pulse emission, p (-) representing the von mises distribution, α being a distribution concentration parameter, B0(α) a zero order modified Bessel function of α;
the pulse transmission probability is
Pk(t)＝λk(t)Δtexp(-λk(t)Δt)
Where Δ t represents the time period for which 1 pulse is transmitted;
the pulse queue of the Poisson neuron is
Wherein m represents the number of Poisson neurons,
denotes yk(t) the time of transmission of the jth pulse of the ith Poisson neuron,
denotes yk(t) the number of pulses in the pulse train emitted by the first poisson neuron;
queue the first pulse
As excitatory input and post-synaptic pulses, a second pulse train
Synaptic plasticity modification function M as an input to suppress and pre-synaptic pulses and to establish a first pulse traink(t) a synaptic plasticity modification function Q (t) for decreasing the synaptic strength, establishing a second pulse train for increasing the synaptic strength, exponentially decaying to
Wherein the content of the first and second substances,
number in reverse direction, τ, representing k-Time constant, τ, representing a decrease in synaptic plasticity+Time constant, M, representing enhanced synaptic plasticityk(t) denotes a synaptic plasticity modification function of the first pulse train, Q (t) denotes a synaptic plasticity modification function of the second pulse train, and Q (t) is specifically
When a presynaptic pulse is received at synapse at time t, the synaptic plasticity correction function M of the first pulse queuek(t) decreasing A-Value of change in synaptic conductance gaIncrease Mk(t)gmaxIf g isa<0, then gaSet to 0; when the synapse receives a post-synaptic pulse at time t, the synaptic plasticity correction function Q (t) of the second pulse train is increased by A+Value of change in synaptic conductance gaIncreasing Q (t) gmaxIf g isa>gmaxThen g isaIs set as gmaxWherein g ismaxIs gaThe maximum amount of change of;
when an excitatory synapse receives a presynaptic pulse, the excitatory synapse conducts gex(t) increase in gmax(ii) a Inhibiting synaptic conductance g when the inhibiting synapse receives a pre-or post-synaptic pulsein(t) increase in ga(ii) a Excitatory synapse conductance g when excitatory synapse and inhibitory synapse do not receive a signalex(t) and inhibition of synaptic conductance gin(t) decay exponentially.
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