CN111401540A - Neuron model construction method and neuron model - Google Patents
Neuron model construction method and neuron model Download PDFInfo
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Abstract
The application discloses a neuron model construction method and a neuron model constructed by the neuron model, wherein the neuron model is constructed by a white noise generator, a refractory period switch signal generator, a post-spike potential signal generator, an activation threshold potential signal generator, a zero-crossing detector, a trigger and a plurality of post-spike potential filters to simulate white noise signals of neurons, emission of spike signals, a refractory period and a hyperpolarization phenomenon, and provides a complete model of the neurons from signal receiving to signal processing and emission of the spike signals to generation of output signals.
Description
Technical Field
The invention relates to a simulation technology of neurons, in particular to a neuron model construction method and a neuron model constructed by the same.
Background
Neurons are also called nerve cells, and can conduct bioelectric signals through the change of ion concentration inside and outside cell membranes. Electrical signals are transmitted between neurons through synaptic connections and by neurotransmitters. Many neurons are interconnected to form a neural center, which is responsible for the higher neural activities such as sensing of external information, control of body reflection, and thinking memory.
The receiving, processing and outputting of the bioelectric signals by the neurons are a complex process, and the simulation and the modeling of the bioelectric signals are performed, so that the deep understanding of the working mechanism of the nerve cells is facilitated on one hand, and the development of a new artificial neural network algorithm and a hardware circuit is facilitated on the other hand.
Disclosure of Invention
In view of the above problems, the present application aims to propose a neuron model capable of simulating the conduction of bioelectrical signals of neurons.
The application provides a neuron model construction method, wherein input signals of the model are summation of multiple paths of input signals to obtain a total input signal; the output signals of the model are membrane potential signals and post-synaptic potential signals;
emitting a white noise signal with a white noise generator to simulate white noise in neurons;
sending out a refractory period switching signal by using a refractory period switching signal generator, and simulating the refractory period of neurons;
sending out a post-spike potential signal by using a post-spike potential signal generator, and simulating the process of restoring the membrane potential of the neuron from the hyperpolarized potential to the resting potential;
an activation threshold potential signal generator is used for sending out an activation threshold potential signal which is used for simulating and judging whether the neuron can be activated;
realizing zero-crossing detection by using a zero-crossing detector to judge whether the current membrane potential of the model exceeds an activation threshold potential; if the current membrane potential exceeds the activation threshold potential signal, the zero-crossing detector sends out a spike signal to simulate the depolarization and repolarization processes of the neuron membrane potential; if the current membrane potential does not exceed the activation threshold potential signal, not sending a spike signal;
simulating responses of synapses of neurons to transfer spike signals of the neurons to other neurons by using a plurality of post-synaptic potential filters;
controlling a trigger by using a peak signal sent by a zero-crossing detection unit; controlling the triggering of the refractory period switch signal generator, the post-spike potential signal generator and the activation threshold potential signal generator by using a trigger;
summing the total input signal with a white noise signal, weighting with a refractory period switch signal and summing with a post-spike potential signal in sequence to obtain a basic potential signal; the basic potential signal and the activation threshold potential signal are subtracted, then a zero-crossing detector judges whether zero crossing occurs or not, and a peak signal is output when zero crossing occurs; the peak signal triggers a refractory period switch signal generator, a post-peak potential signal generator and an activation threshold potential signal generator through a trigger; summing the basic potential signal and the peak signal to obtain a membrane potential signal; the spike signal also generates a post-synaptic potential signal through a post-synaptic potential filter.
Correspondingly, the present application also proposes a neuron model, which includes: a white noise generator, a refractory period switching signal generator, a post-spike potential signal generator, an activation threshold potential signal generator, a zero-crossing detector, a trigger, and a plurality of post-spike potential filters; wherein the content of the first and second substances,
a white noise generator for emitting a white noise signal, thereby simulating white noise in neurons;
the refractory period switch signal generator is used for sending out a refractory period switch signal, so as to simulate the refractory period of neurons;
the post-spike potential signal generator is used for sending out a post-spike potential signal so as to simulate the process that the membrane potential of the neuron is recovered from the hyperpolarization potential to the resting potential;
the activation threshold potential signal generator is used for sending out an activation threshold potential signal, so that the adaptive change and the accumulation effect of the activation threshold potential of neurons are simulated;
the zero-crossing detector is used for realizing zero-crossing detection so as to judge whether the current membrane potential of the model exceeds an activation threshold potential; if the current membrane potential exceeds the activation threshold potential signal, the zero-crossing detector sends out a spike signal to simulate the depolarization and repolarization processes of the neuron membrane potential; if the current membrane potential does not exceed the activation threshold potential signal, not sending a spike signal;
the plurality of post-synaptic potential filters are used for simulating responses of synapses of neurons for transmitting spike signals of the neurons to other neurons in a process;
summing a total input signal obtained by summing multiple paths of input signals of the model with a white noise signal, weighting the total input signal with a refractory period switch signal, and summing with a post-peak potential signal to obtain a basic potential signal; the basic potential signal and the activation threshold potential signal are subtracted, then a zero-crossing detector judges whether zero crossing occurs or not, and a peak signal is output when zero crossing occurs; the peak signal triggers a refractory period switch signal generator, a post-peak potential signal generator and an activation threshold potential signal generator through a trigger; summing the basic potential signal and the peak signal to obtain a membrane potential signal; the spike signal also generates a post-synaptic potential signal through a post-synaptic potential filter.
Preferably, the white noise signal is intrinsic characteristic noise of neurons.
Preferably, after the refractory period switching signal generator is triggered, the generated refractory period switching signal changes from 1 to 0 and then changes from 0 to 1 within a change duration.
Preferably, there may be an adaptive dynamic process of the activation threshold. Due to the influence of the switching dynamic process of the ion channel, when the activation threshold potential generator is triggered, the activation threshold value is increased on the basis of a rest threshold value, is activated to a higher hyperpolarization threshold value, and then is gradually restored to the rest threshold value state. Such activation thresholds have an additive effect. That is, when the activation threshold potential signal has not yet returned to the resting threshold, if triggered again, an increment is generated based on the current value, and then the change is made to the resting threshold.
Preferably, the post-synaptic potential signal has a connection strength; the connection strength is divided into two parts, the first part is the resting connection strength, and the second part is the activated connection strength; the activation connection strength is adaptive to changes and has an additive effect. When the post-synaptic potential is triggered by a spike, the activation connection strength is increased and then gradually changes toward zero. If the activation connection strength has not returned to zero, it is again triggered, due to the additive effect, to produce an increment, which then changes back to zero. The sum of the resting bond strength and the activating bond strength constitutes the bond strength of the postsynaptic potential. The increment of the activation connection strength may be a positive value or a negative value. When the increment is positive, the neurons are connected in an excitatory manner. When the increment is negative, inhibitory connection is formed between neurons.
Preferably, the plurality of post-synaptic potential filters generate a plurality of post-synaptic potential signals for inputting to other neuron models for forming an artificial neural network.
The complete model of the neuron from receiving a signal to processing the signal, emitting a spike signal and generating an output signal is provided through the established neuron model. The model covers white noise signals of neurons, emission of spike signals, refractory period and hyperpolarization phenomena. The activation threshold of the model may be adaptive, and if a neuron spikes for a short period of time, the activation threshold will be higher due to additive effects, making it more difficult for the neuron to be activated. Adaptive changes of the activation threshold potential model fatigue behavior when neurons send spikes. The post-synaptic potential has a connection strength, which models the connection strength of signals as they pass through synapses between neurons. The neuron model can be used for developing an algorithm or a hardware circuit of an artificial neuron and is applied to construction of an artificial neural network.
Drawings
FIG. 1 is a diagram showing the process of membrane potential changes when a neuron spikes;
FIG. 2 is a diagram of one embodiment of a neuron model;
FIG. 3 is a waveform diagram of a neuron refractory period switching signal;
FIG. 4 is a waveform diagram of one representation of a spike signal and a post-spike potential signal;
FIG. 5 is a waveform diagram of another representation of a spike signal and a post-spike potential signal;
FIG. 6 is a waveform of a neuron activation threshold potential signal;
fig. 7 is a waveform diagram of the connection strength of the post-synaptic potential.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings.
FIG. 1 shows the change in membrane potential when a neuron spikes. In the absence of a strong external stimulus potential, the neuronal membrane potential remains at resting potential. When the received external stimulation potential exceeds an activation threshold, the ion channel of the neuron membrane is changed, depolarization and repolarization phenomena occur, and a spike signal is generated. Then the membrane potential of the neuron is hyperpolarized and a negative maximum occurs, after which the membrane potential gradually returns to the resting potential. In this process, the neurons become less sensitive to external electrical stimuli, and this time is called the refractory period.
The process of receiving, processing and outputting spikes of external stimulation signals by neurons can be modeled. FIG. 2 depicts an embodiment of a neuron model. Firstly, the multipath stimulation signals received by the neuron are summed to obtain I (t) which is used as the total input signal of the neuron. The input signal is summed with a white noise signal n (t) to simulate the effect of white noise in neurons on the input signal. The white noise signal n (t) is emitted by a white noise generator in the neuron. The signal is then weighted with the refractory period switching signal ear (t) generated by the refractory period switching signal generator. The refractory period switching signal generator is controlled by a trigger. After the switch signal generator of the refractory period is triggered, the switch signal ear (t) of the refractory period changes from 1 to 0 and then gradually recovers to 1, and the change lasts for time tear。
The dashed line in fig. 3 represents a refractory period switching signal ear (t) with a square wave shape, which changes from 1 to 0 after being triggered, and changes for a duration tearIt remains 0 and then becomes 1 again. At varying duration tearIn the interior, neurons remain in an absolute refractory state to external stimuli. The solid line in FIG. 3 indicates anotherThe embodiment of the refractory period switching signal ear (t) is changed from 1 to 0 after being triggered, and the change duration t isearGradually changing from 0 to 1. When the refractory period switching signal ear (t) is in this state, the process of slowly recovering the neuronal membrane potential from absolute refractory can be simulated. Exceeding the duration of change tearThen, when there is no new trigger, the refractory period switching signal ear (t) remains at 1.
After the signal is weighted by the refractory period switching signal ear (t), it is summed with the post-spike potential signal psmp (t) to obtain the base potential signal b (t). The post-spike potential signal psmp (t) is generated by the post-spike potential signal generator triggered by the trigger. The duration of the change of the post-spike potential signal psmp (t) is tpsmpAnd the process is used for outputting the process that the membrane potential of the neuron slowly recovers from the hyperpolarization potential to the resting potential. Duration of change tpsmpAfter the end and when there is no new trigger, the post-spike potential signal psmp (t) remains 0. Duration t of change of refractory period switching signal ear (t)earAnd the duration t of the change of the post-spike potential signal psmp (t)psmpMay or may not be equal. FIG. 4 shows that at tpsmpA waveform diagram of a post-spike potential signal psmp (t) in a time domain, which is a sawtooth wave, first reaches a negative maximum and then returns to a resting potential. T in FIG. 5psmpAnother waveform diagram of the post-spike potential signal psmp (t) is shown in the time range, which changes from a negative large value to a resting potential non-linearly.
The activation threshold potential of the neuron is adaptive to changes. The activation threshold potential signal te (t) is sent by the activation threshold potential signal generator. Fig. 6 is a schematic diagram of the change process of the neuron activation threshold potential signal te (t) after being triggered. te (te)restThe rest threshold is a constant, and if the current active threshold potential signal te (t) is the rest threshold and is not triggered, the current active threshold potential signal is always kept at the rest threshold. When the activation threshold potential generator is triggered, the activation threshold te (t) is at the rest threshold terestOn the basis of (2) to produce an increment teaddActivation to a higher hyperpolarization threshold te0Then gradually recovering to a resting threshold state when recoveringIs m between tte. This activation threshold has an additive effect, given that T is the last time the spike was discharged, then there is te0＝te(T)+teadd. That is, when the activation threshold potential signal te (t) has not yet returned to the rest threshold, if triggered again, it will increment based on the current value, and then change to the rest threshold.
The basic potential signal b (t) needs to be subtracted from the activation threshold potential signal te (t), and then zero-crossing detection is performed to determine whether the current membrane potential exceeds the activation threshold potential. When the membrane potential exceeds the activation threshold, the neuron emits a spike signal sp (t) that simulates the process of depolarization and repolarization of the neuron's membrane potential. And when the zero-crossing condition is not met, no spike signal is emitted. In the embodiment of fig. 4, the spike signal sp (t) is a triangular wave. In the embodiment of fig. 5, the spike signal sp (t) is an impulse function.
The white noise generator, the refractory period switch signal generator, the post-spike potential signal generator, and the activation threshold potential signal generator may constitute a neuron intrinsic signal generator, all of which are controlled by the trigger. The trigger may be integrated in the neuron intrinsic signal generator. The trigger is controlled by a spike sp (t), and is triggered when receiving the spike.
The spike signal sp (t) generated by the neuron is transmitted to the post-synaptic potential filter. The post-synaptic potential filter is used for simulating the response of the signal transmission process to other neurons through synapses and outputting a post-synaptic potential signal psp (t). There may be many post-synaptic potential filters that can be used to simulate the connection of a neuron to other neurons to form a neural network. The filtered post-synaptic potential signal psp (t) may also be multiplexed. Fig. 5 shows a post-synaptic potential signal psp (t) that is delayed with respect to a spike signal sp (t).
The post-synaptic potential signal psp (t) has a junction strength w (t) derived from the resting junction strength wrestAnd activation of the joining strength wact(t) two parts. Strength of rest connection wrestIs constant, activates the joint strength wact(t) is adaptable and hasIt is additive. The connection strength of the postsynaptic potential signal is the sum of the resting connection strength and the activated connection strength, i.e. w (t) ═ wrest+wact(t) of (d). Fig. 7 shows a postsynaptic potential connection strength signal w (t). Dotted line is the resting joint strength wrestAnd is always kept unchanged. Activating the connection strength w when receiving the trigger of the spike signal sp (t)act(t) generating an increment waddThen gradually changing to zero with the required time tw. If the activation connection strength has not returned to zero, it is again triggered, due to the additive effect, to produce an increment, which then changes back to zero. Strength of rest connection wrestAnd activated bond strength wact(t) the superposition of the waveforms is the connection strength signal w (t) for the postsynaptic potential. Activating an increment w of joint strengthaddAnd may be positive or negative. When the increment of the activation connection strength is positive, the connection between neurons is excitatory. When the increment of the activation connection strength is negative, the connection between neurons is inhibitory.
The base potential signal b (t) is summed with the spike signal sp (t) to obtain a membrane potential signal mp (t) for outputting the membrane potential change state of the neuron. The membrane potential signal mp (t) and the series of post-synaptic potential signals psp (t) together constitute the output signal of the neuron model. To this end, the neuron model in this embodiment completes the conversion from the input signal i (t) to the output membrane potential signal mp (t) and the set of post-synaptic potential signals psp (t).
The white noise generator, the refractory period switching signal generator, the post-spike potential signal generator, the activation threshold potential signal generator, the zero-crossing detector, the flip-flop, and the post-spike potential filters, and the operations of summing, weighting, etc., referred to in the present application, may be implemented by corresponding hardware circuits, or may be implemented by programs on devices such as computers, etc.
Unless defined otherwise, all technical and/or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The materials, methods, and examples set forth in this application are illustrative only and not intended to be limiting.
Although the present invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the teachings of this application and yet remain within the scope of this application.
Claims (12)
1. A neuron model building method is disclosed, wherein, the input signal of the model is a total input signal obtained by summing a plurality of paths of input signals; the output signals of the model are membrane potential signals and post-synaptic potential signals;
emitting a white noise signal with a white noise generator to simulate white noise in neurons;
sending out a refractory period switching signal by using a refractory period switching signal generator, and simulating the refractory period of neurons;
sending out a post-spike potential signal by using a post-spike potential signal generator, and simulating the process of restoring the membrane potential of the neuron from the hyperpolarized potential to the resting potential;
an activation threshold potential signal generator is used for sending out an activation threshold potential signal which is used for simulating and judging whether the neuron can be activated;
realizing zero-crossing detection by using a zero-crossing detector to judge whether the current membrane potential of the model exceeds an activation threshold potential; if the current membrane potential exceeds the activation threshold potential signal, the zero-crossing detector sends out a spike signal to simulate the depolarization and repolarization processes of the neuron membrane potential; if the current membrane potential does not exceed the activation threshold potential signal, not sending a spike signal;
simulating responses of synapses of neurons to transfer spike signals of the neurons to other neurons by using a plurality of post-synaptic potential filters;
controlling a trigger by using a peak signal sent by a zero-crossing detection unit; controlling the triggering of the refractory period switch signal generator, the post-spike potential signal generator and the activation threshold potential signal generator by using a trigger;
summing the total input signal with a white noise signal, weighting with a refractory period switch signal and summing with a post-spike potential signal in sequence to obtain a basic potential signal; the basic potential signal and the activation threshold potential signal are subtracted, then a zero-crossing detector judges whether zero crossing occurs or not, and a peak signal is output when zero crossing occurs; the peak signal triggers a refractory period switch signal generator, a post-peak potential signal generator and an activation threshold potential signal generator through a trigger; summing the basic potential signal and the peak signal to obtain a membrane potential signal; the spike signal also generates a post-synaptic potential signal through a post-synaptic potential filter.
2. The neuron model building method according to claim 1, wherein the white noise signal is intrinsic characteristic noise of neurons.
3. A neuron model building method according to claim 1, wherein after the refractory period switching signal generator is triggered, the generated refractory period switching signal changes from 1 to 0 and then from 0 to 1 within a change duration.
4. A neuron model building method according to claim 1, wherein the activation threshold potential signal is adaptive and has an additive effect, and when it is a resting threshold and is not triggered, it remains at the resting threshold; when triggered, an increment is generated based on the current threshold, the increment is activated to a higher hyperpolarized threshold, and then the increment is gradually changed to a resting threshold.
5. The neuron model building method according to claim 1, wherein the post-synaptic potential signal has a connection strength; the connection strength is divided into two parts, the first part is the resting connection strength, and the second part is the activated connection strength; the activation connection strength is adaptive to changes and has an additive effect.
6. A neuron model building method as defined in claim 1, wherein the plurality of post-synaptic potential filters generate a plurality of post-synaptic potential signals for input to other neuron models to form an artificial neural network.
7. A neuron model, comprising: a white noise generator, a refractory period switching signal generator, a post-spike potential signal generator, an activation threshold potential signal generator, a zero-crossing detector, a trigger, and a plurality of post-spike potential filters; wherein the content of the first and second substances,
a white noise generator for emitting a white noise signal, thereby simulating white noise in neurons;
the refractory period switch signal generator is used for sending out a refractory period switch signal, so as to simulate the refractory period of neurons;
the post-spike potential signal generator is used for sending out a post-spike potential signal so as to simulate the process that the membrane potential of the neuron is recovered from the hyperpolarization potential to the resting potential;
the activation threshold potential signal generator is used for sending out an activation threshold potential signal, so that the threshold potential for simulating and judging whether the neuron can be activated is judged;
the zero-crossing detector is used for realizing zero-crossing detection so as to judge whether the current membrane potential of the model exceeds an activation threshold potential; if the current membrane potential exceeds the activation threshold potential signal, the zero-crossing detector sends out a spike signal to simulate the depolarization and repolarization processes of the neuron membrane potential; if the current membrane potential does not exceed the activation threshold potential signal, not sending a spike signal;
the plurality of post-synaptic potential filters are used for simulating responses of synapses of neurons for transmitting spike signals of the neurons to other neurons in a process;
the trigger is used for controlling the triggering of the refractory period switch signal generator, the post-spike potential signal generator and the activation threshold potential signal generator; the trigger is controlled by a peak signal sent by the zero-crossing detection unit;
summing a total input signal obtained by summing multiple paths of input signals of the model with a white noise signal, weighting the total input signal with a refractory period switch signal, and summing with a post-peak potential signal to obtain a basic potential signal; the basic potential signal and the activation threshold potential signal are subtracted, then a zero-crossing detector judges whether zero crossing occurs or not, and a peak signal is output when zero crossing occurs; the peak signal triggers a refractory period switch signal generator, a post-peak potential signal generator and an activation threshold potential signal generator through a trigger; summing the basic potential signal and the peak signal to obtain a membrane potential signal; the spike signal also generates a post-synaptic potential signal through a post-synaptic potential filter.
8. The neuron model of claim 7, wherein the white noise signal is intrinsic characteristic noise of neurons.
9. A neuron model as defined in claim 7, wherein the refractory period switching signal generator is triggered to generate a refractory period switching signal that changes from 1 to 0 and then from 0 to 1 for a duration of change.
10. A neuron model as defined in claim 7, wherein the activation threshold potential signal is adaptive and has an additive effect that, when it is a resting threshold and not triggered, remains at the resting threshold; when triggered, an increment is generated based on the current threshold, the increment is activated to a higher hyperpolarized threshold, and then the increment is gradually changed to a resting threshold.
11. A neuron model as defined in claim 7, wherein the post-synaptic potential signals have a connection strength; the connection strength is divided into two parts, the first part is the resting connection strength, and the second part is the activated connection strength; the activation connection strength is adaptive to changes and has an additive effect.
12. A neuron model as defined in claim 7, wherein the plurality of post-synaptic potential filters produce a plurality of post-synaptic potential signals for input to other neuron models for forming an artificial neural network.
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