CN117275568A - Primary auditory cortex neuron cell release rate curve simulation method and device - Google Patents
Primary auditory cortex neuron cell release rate curve simulation method and device Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a device for simulating a primary auditory cortex neuron cell release rate curve, wherein the method comprises the following steps: the method comprises the steps of obtaining the stimulation data of the neuron cells of the primary auditory cortex, inputting the stimulation data of the neuron cells into a release rate curve simulation model, simulating by adopting the release rate curve simulation model, and outputting the release rate curve of each neuron cell. The invention builds the primary auditory cortex neuron group model based on the cortex micro-loop structure, is beneficial to researching the auditory response characteristics of different types of neurons, and analyzes the important role of the intermediate inhibitory neurons in auditory information processing. The invention simulates the auditory evoked discharge activity of the primary auditory cortex neurons by researching the relation between the physiological parameters and the neuron responses in the model, and provides a certain theoretical support for further understanding the auditory information coding exploration of the brain auditory nervous system.
Description
Technical Field
The invention relates to the technical field of neurons, in particular to a primary auditory cortex neuron cell firing rate curve simulation method, a primary auditory cortex neuron cell firing rate curve simulation device, an electronic device and a computer readable medium.
Background
Sound is an essential information carrier for human society, which plays an important role in daily communication, decision and learning from person to person. It has been determined that the human brain has approximately 10-12 neurons and 10-15 synapses. The encoding and decoding of sound information is performed by the auditory system in the brain. In the auditory system, the primary auditory cortex (primary auditory cortex) is an important nucleus in the auditory up-and down-channels and is a high-level center of the brain for processing external auditory information.
The development of neurobiology has enabled scientists to study primary auditory cortex at global average activity level or single neuron firing activity and synaptic level by means of electroencephalography, local field potential recording, patch clamp technique, etc. However, since the calculation scale of the brain is extremely large, with the deep research, it is difficult to analyze the complex functions and activities of the brain and accurately explain the complex working modes of the brain by only recording the obtained information by the prior biological means. The rapid development of computer technology provides a breakthrough for the bottleneck, and brain-like artificial intelligence is a popular field of the current neuroscience, and analysis of a large number of biological experiments based on a modeling method is helpful for understanding the corresponding neural activity rules and information codes, and further reveals the mystery of the brain.
The brain communicates information without the links between neurons, and there are a wide variety of interactions of neuronal activity. The method is characterized by researching the response characteristics of the neurons in the primary auditory cortex area in the brain, establishing a corresponding neuron model, further researching the relation between physiological parameters and the corresponding real neurons, and having important significance for understanding the auditory information coding of the brain nervous system.
The primary auditory cortex (primary auditory cortex, hereinafter abbreviated as A1) mainly contains four kinds of neurons, which are Pyramidal cells (Pyramidal, hereinafter abbreviated as Pyr), somatostatin positive cells (somatatin, hereinafter abbreviated as SST), parvalbumin positive cells (pvv), and enterovasoactive polypeptide positive cells (Vasoactice Intestinal Peptide, hereinafter abbreviated as VIP), respectively. They are connected to each other and receive input from the thalamus-cortex ascending loop as shown in figure 1.
The effect of a neuron on another neuron may produce an excitatory or inhibitory effect, whereby neurons may be classified as excitatory neurons and inhibitory neurons. In the field of computational neuroscience (computational neuroscience), the Wilson-cooen Model (Wilson-Cowan Model) describes a very simple interaction dynamics between excitation and suppression Model neurons. Its extension has been widely used for neuronal population modeling. The original formula is as follows:
e (t) and I (t) represent the firing rate of the excitatory and inhibitory populations, respectively, which refers to the probability of a neuron population discharging at a certain time. S is S e () And S is i () Is a sigmoid function distributed according to a trigger thresholdD (θ) is a unimodal form, a concept similar to the activation function of neural networks in deep learning. And α (t) is the decay function of the stimulus. c 1 And c 2 Is the connective weight of the excitatory population, c 3 And C 4 Is the connection weight of the inhibitory population, neurons are connected by synapses, different synapse connection strengths are different, and the phenomenon can be simulated by adjusting the value of the weight. While P (t), Q (t) are external inputs to the excitatory and inhibitory populations.
For ease of calculation, it can be reduced to a coarse-grained (coarse-grained) model, i.e. the expression (1) is expressed in differential form:
solving the differential equation sets can obtain firing rate curves of excitatory neurons and inhibitory neurons, respectively.
The original wilson coanda model merely divided nerve cells into two categories, excitatory and inhibitory. In fact, there is a difference between different excitatory neurons and different inhibitory neurons. Taking A1 as an example, PV, SST, VIP cells exhibiting inhibition and Pyr cells exhibiting excitability each receive different intensities of thalamus input and different intensities of interactions between them. Further refinement of the model is required in order to study firing rate characteristics of different types of neurons. The formulation (2) was extended in the article A circuit model of auditory cortex by Youngmin Park et al to simulate the release rate-time response curves of the Pyramidal, somatostatin (SST) and Parvalbumin Positive (PV) cell populations in the primary auditory cortex (Primary Audiotry Cotex, A1):
where u (t), p (t), s (t) are normalized firing rates of excitatory, inhibitory, and SST neuron populations, respectively. w (w) ij For the strength of the connection between the neuron groups, g (t) is the thalamus inhibiting variable, and i (t) is the external input to the neuron group. Note that all parameters and values of equation (3) are dimensionless except for time. Solving the model can obtain the release rate-time of three types of neurons to sound responseThe curves are shown in fig. 2.
However, these two models suffer from the following disadvantages:
first, the current mathematical description of A1 neuron activity trades off for physiologically significant factors, although reducing the parameters that need to be considered, for lack of stringency.
As mentioned previously, neurons in A1 are of four classes, PV, SST, VIP and Pyr, of which there are PV, SST and VIP for inhibitory neurons and Pyr for excitatory neurons. Clearly, the activity characteristics of each type of neuron and their association cannot be represented using the original wilson coon model. While the existing auditory cortex model mentioned in the second point refines the wilson coon model and expands the model into a firing rate curve model describing three types of PV, SST, pyr neurons, the role of VIP in the model is ignored. Furthermore, as shown in fig. 1, there are connections of the thalamus to all four types of neurons of A1, whereas formula (3) ignores the input of SST from the thalamus (external input) because the connection strength of the thalamus to SST is not great.
In practice, however, the thalamus has an input effect not only on each type of neuron in A1, but also with a different intensity, whereas the part qg (t) i (t) describing the thalamus input of formula (3) uses the same weight q.
Second, the action that neurons produce discharges upon acoustic stimulation can be studied by the firing rate versus time curve of a group of neurons of a certain type after acoustic stimulation. Electrophysiological experiments have found that not only are different populations of neurons of different types issued, but also neurons of the same type have different patterns of issuance, which are respectively a sound-giving start type (Onset), a sound-giving end type (Offset), a sound-giving start-end type (On-off) and a sound-giving duration type (Sustained) response, as shown in fig. 3. However, no specific method for simulating these response modes has been proposed.
Disclosure of Invention
In view of the foregoing, embodiments of the present invention have been developed to provide a primary auditory cortex neuronal cell firing rate curve simulation method and corresponding primary auditory cortex neuronal cell firing rate curve simulation device, an electronic device, and a computer readable medium that overcome or at least partially solve the foregoing problems.
The embodiment of the invention discloses a primary auditory cortex neuron cell release rate curve simulation method, which comprises the following steps:
obtaining stimulation data of a neuron cell of a primary auditory cortex; the neuron cells comprise a centrum cell Pyr, a somatostatin positive cell SST, a parvalbumin positive cell PV and an intestinal vascular activity polypeptide positive cell VIP;
inputting the stimulation data of the neuron cells into a release rate curve simulation model; the release rate curve simulation model comprises a Pyr release rate curve simulation sub-model, an SST release rate curve simulation sub-model, a PV release rate curve simulation sub-model and a VIP release rate curve simulation sub-model;
and simulating and outputting the release rate curve of each neuron cell by adopting the release rate curve simulation model.
Optionally, the stimulus data is a current generated by simulating noise stimulus, and is a square wave signal.
Optionally, the firing rate curves of the neuron cells include a sound-giving start-type firing rate curve, a sound-giving end-type firing rate curve, a sound-giving start-end-type firing rate curve, and a sound-giving continuous-type firing rate curve.
Optionally, the method further comprises:
the expression of the release rate curve simulation model of the neuron cells is as follows:
wherein u (t), p (t), s (t) and v (t) respectively represent the release rate curves of Pyr, PV, SST and VIP neuron cells, and t is time;
where f (x) is a neuron spike response function, and the formula is:
wherein x is all excitatory and inhibitory inputs received by all neuronal cells of the primary auditory cortex; r is 3;
wherein I is Thal (t) is an external input from the thalamus, calculated using stimulus data:
I Thal (t)＝I Facil (t)D Fast (t)D Slow (t)
wherein the I is Facil (t) represents the excitatory current produced by postsynaptic neurons activated by acoustic signals, defined as:
gain of sexual synapses; τ I Is an excitatory time constant;
D Fast (t) and D Slow (t) represents the regulation of excitatory currents by fast and slow inhibitory neurons, respectively, defined as:
wherein A is D，fast And A D，slow Gain of inhibitory regulation of fast inhibitory neurons and slow inhibitory neurons, respectively; τ D In order to rapidly suppress the time constant of the factor,and->Is the time constant of the slow inhibitor.
Wherein q is u 、q p 、q s 、q v Weight of Pyr, PV, SST and VIP neuronal cells receiving thalamus input, respectively, q according to the difference in intensity of Pyr, PV, SST and VIP neuronal cells receiving thalamus input u 、q p 、q s 、q v Assigning a value;
wherein w is ij Is the connection strength between different neuronal cell synapses and the connection strength between the same neuronal cell synapses;
wherein g (t) is used to simulate inhibitory modulation of input to the thalamus by the primary auditory cortex, expressed as follows:
wherein g is 0 Is the initial value of g (t),and->A time constant of g (t);
wherein τ u 、τ p 、τ s 、τ v The time required for four classes of neuronal cells to discharge upon receiving an excitation, respectively.
Optionally, the method further comprises:
when τ is D ≤20、τ I Less than or equal to 20, and tau D ＞τ I At the same time, with tau D And τ I The curve of the difference increase simulation approaches to the sound giving start type release rate curve;
when τ is D ≤20、τ I Less than or equal to 20, and tau D ＜τ I At the same time, with tau D And τ I The curve of the difference increase simulation approaches to the sound giving end type release rate curve;
when τ is D ≤20、τ I Less than or equal to 20, and tau D And τ I When approaching to being equal, two peaks appear on the simulated curve, and the simulated curve obtains a sound giving start-end type release rate curve;
when τ is D ≥50、τ I =0.5, simulation results in a sustained release rate curve for sound delivery, τ D Typically taking a value close to 50.
The embodiment of the invention also discloses a device for simulating the primary auditory cortex neuron cell release rate curve, which comprises:
the stimulation data acquisition module is used for acquiring the stimulation data of the neuron cells of the primary auditory cortex; the neuron cells comprise a centrum cell Pyr, a somatostatin positive cell SST, a parvalbumin positive cell PV and an intestinal vascular activity polypeptide positive cell VIP;
the input module is used for inputting the stimulation data of the neuron cells into a release rate curve simulation model; the release rate curve simulation model comprises a Pyr release rate curve simulation sub-model, an SST release rate curve simulation sub-model, a PV release rate curve simulation sub-model and a VIP release rate curve simulation sub-model;
and the simulation module is used for simulating and outputting the release rate curve of each neuron cell by adopting the release rate curve simulation model.
The embodiment of the invention also discloses electronic equipment, which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory is used for storing a computer program;
the processor is used for realizing the primary auditory cortex neuron cell release rate curve simulation method according to the embodiment of the invention when executing the program stored in the memory.
One or more computer-readable media having instructions stored thereon, which when executed by one or more processors, cause the processors to perform the primary cortical neuron cell firing rate curve simulation method according to embodiments of the present invention are also disclosed.
The embodiment of the invention has the following advantages:
according to the primary auditory cortex neuron cell release rate curve simulation method, stimulation data of the primary auditory cortex neuron cells are obtained, the neuron cells comprise vertebral cells Pyr, somatostatin positive cells SST, parvalbumin positive cells PV and intestinal vascular activity polypeptide positive cells VIP, the stimulation data of the neuron cells are input into a release rate curve simulation model, the release rate curve simulation model comprises Pyr release rate curve simulation submodel, SST release rate curve simulation submodel, PV release rate curve simulation submodel and VIP release rate curve simulation submodel, and release rate curves of each neuron cell are simulated and output by adopting the release rate curve simulation model. The invention focuses on the role of VIP neurons in primary auditory cortex, builds a release rate curve simulation model comprising the VIP neurons, and reflects the mutual inhibition/activation relationship between VIP and other types of neuron groups in the primary auditory cortex; aiming at the difference of input intensity of thalamus to each neuron, the invention gives different weights to thalamus current variables input by different neurons according to experimental data, so that the model is more close to physiological significance; in addition, the invention can simulate various release modes of the neuron population by adjusting the time constant of each factor in the release rate curve simulation model equation.
Drawings
FIG. 1 is a schematic illustration of neuronal cells interconnected and receiving thalamus input;
FIG. 2 is a plot of firing rate versus time for three classes of neurons in response to sound;
FIG. 3 is a graph of different response patterns for the presence of neurons of the same class;
FIG. 4 is a flow chart of steps of a method for simulating a primary auditory cortex neuron cell firing rate curve according to an embodiment of the present invention;
FIG. 5 is a graph of input current versus time from the thalamus for Oset response and Offset response provided in an embodiment of the present invention;
FIG. 6 is a graph of input current versus time from the thalamus for an On-Off response provided in an embodiment of the present invention;
FIG. 7 is a plot of input current versus time from the thalamus for a Sustained response hypothalamus provided in an embodiment of the present invention;
FIG. 8 is a comparative schematic of the second peak of the thalamus output current curve provided in the examples of the present invention being strongly suppressed and the two peak heights tending to be equal;
FIG. 9 is a plot of firing rate versus time for four classes of neuronal cells in the primary auditory cortex provided in an embodiment of the present invention;
FIG. 10 is a block diagram of a device for generating user behavior labels based on small programs according to an embodiment of the present invention;
FIG. 11 is a block diagram of an electronic device provided in an embodiment of the invention;
fig. 12 is a schematic diagram of a computer readable medium provided in an embodiment of the invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Referring to fig. 4, a flowchart illustrating steps of a method for simulating a primary auditory cortex neuron cell firing rate curve according to an embodiment of the present invention may specifically include the following steps:
step 401, obtaining stimulation data of a neuron cell of a primary auditory cortex; the neuron cells comprise a centrum cell Pyr, a somatostatin positive cell SST, a parvalbumin positive cell PV and an intestinal vascular activity polypeptide positive cell VIP;
in order to study the auditory response characteristics of different types of nerve cells, the invention constructs a firing rate curve simulation model of the primary auditory cortex nerve cells, simulates the auditory evoked discharge activity of the primary auditory cortex nerve cells by studying the relation between physiological parameters and nerve cell responses in the model, and provides a certain theoretical support for further understanding auditory information coding exploration of the brain auditory nervous system.
Since neurons are stimulated by sound to generate discharging actions, the invention can study the response characteristics of different types of neurons through the distribution rate-time curve of a certain type of neuron group after sound stimulation. Specifically, the stimulation data of the neuron cells of the primary auditory cortex can be obtained first, and the stimulation data can be current generated by simulating noise stimulation and is a square wave signal.
The auditory system processes sound by converting the mechanical wave signal of sound waves from the inner ear into an electrical signal, which is a nerve pulse sequence. However, when the frequency of the sound is high, such pulses can be regarded as a continuous discharge process. It is believed that the signal characteristics of the electrical synapse passing from an auditory neuron to the next post-protrusion neuron are primarily determined by the temporal envelope of the sound signal. Thus, the present invention approximates the white noise signal for sound stimulation to a square wave input signal in the simulation.
In addition, in the embodiment of the invention, the neuron cells can comprise four types of vertebral cells Pyr, somatostatin positive cells SST, parvalbumin positive cells PV and intestinal vasoactive polypeptide positive cells VIP, wherein the PV, the SST and the VIP are inhibitory neurons, and the excitatory neurons are Pyr. The invention covers the neuron cells of the primary auditory cortex, comprehensively researches the mutual inhibition/activation relation of all types of neuron groups in the primary auditory cortex, and solves the problem that the modeling of the neurons of the primary auditory cortex ignores the action of VIP neurons.
Step 402, inputting the stimulation data of the neuron cells into a firing rate curve simulation model; the release rate curve simulation model comprises a Pyr release rate curve simulation sub-model, an SST release rate curve simulation sub-model, a PV release rate curve simulation sub-model and a VIP release rate curve simulation sub-model;
after the stimulation data of the neuronal cells is acquired, the stimulation data of the neuronal cells may be input into a firing rate curve simulation model to simulate the firing rate curve of the neuronal cells.
Because the neuron cells in the embodiment of the invention comprise four types of vertebral cells Pyr, somatostatin positive cells SST, parvalbumin positive cells PV and intestinal vasoactive polypeptide positive cells VIP, in order to simulate the simulation curves of each type of neuron cells, correspondingly, the release rate curve simulation model established by the invention can comprise Pyr release rate curve simulation sub-model, SST release rate curve simulation sub-model, PV release rate curve simulation sub-model and VIP release rate curve simulation sub-model, so that the Pyr release rate curve simulation sub-model can simulate Pyr release rate curve, the SST release rate curve simulation sub-model can simulate SST release rate curve, the PV release rate curve simulation sub-model can simulate PV release rate curve, and the VIP release rate curve simulation sub-model can simulate VIP release rate curve.
In one embodiment of the present invention,
the expression of the release rate curve simulation model of the neuron cells is as follows:
wherein u (t), p (t), s (t) and v (t) respectively represent the release rate curves of Pyr, PV, SST and VIP neuron cells, and t is time;
where f (x) is a neuron spike response function, and the formula is:
wherein x is all excitatory and inhibitory inputs received by all neuronal cells of the primary auditory cortex; r is 3;
wherein I is Thal (t) isExternal input from the thalamus, calculated using stimulation data:
I Thal (t)＝I Facil (t)D Fast (t)D Slow (t)
neurons in the main brain region MGB of the thalamus exist with two different non-protein amino acid energies of the gamma aminobutyric acid A type (gamma aminobutyric acid, GABA) A ) Synaptic responses. Wherein near Pyr somatic cells is rapid GABA A，fast The salient response is inhibited by rapid activation elevation and attenuation by somatic synaptic mediation; while near dendrites is slow GABA A，slow Synaptic responses are inhibited by slow activation rise and decay by dendritic synaptic mediation. Thus the sound information conduction modeling of the MGB can be approximated as a nonlinear time-varying tuning process, sound stimulation other than the generation of excitatory electrical activity I Facil (t) also by rapid inhibition of neurons D Fast (t) and slow inhibitory neuron D Slov (t) the resulting inhibitory electrical activity.
Wherein the I is Facil (t) represents the excitability produced by postsynaptic neurons activated by acoustic signalsThe gain of the sexual synapse may take a value of 1.τ I Is an excitatory time constant;
D Fast (t) and D Slow (t) represents the regulation of excitatory currents by fast and slow inhibitory neurons, respectively, defined as:
wherein A is D，fast And A D，slow The gains for inhibitory regulation of the fast inhibitory neurons and the slow inhibitory neurons, respectively, may take a value of 1.τ D In order to rapidly suppress the time constant of the factor,and->Is the time constant of the slow inhibitor.
Wherein q is u 、q p 、q s 、q v Weight of Pyr, PV, SST and VIP neuronal cells receiving thalamus input, respectively, q according to the difference in intensity of Pyr, PV, SST and VIP neuronal cells receiving thalamus input u 、q p 、q s 、q v Assigning a value;
the thalamus also contributes to the various neurons of A1 to a different degree, q of the model expression u 、q p 、q s 、q v The invention simulates different degrees of contribution of thalamus output to A1 neurons by setting different weights for different neuron populations to accept the weight of thalamus input.
By experimentation, it was determined that the ratio of intensity differences between the different neuronal populations receiving thalamus input was approximately Pyr: PV: SST: VIP=2:1:0.1:0.1. Thus q can be u 、q p 、q s 、q v Assigned to 2, 1, 0.1, respectively.
Wherein w is ij Is the connection strength between different neuronal cell synapses and the connection strength between the same neuronal cell synapses;
as shown in fig. 1, although there is a link between each nerve cell population, studies have shown that there is a difference in the link strength between different nerve cell populations. The invention sets the connection weight w in the model expression ij To simulate this difference.
The strength of synaptic connections between different neural cell populations in the primary auditory cortex can be determined experimentally.
TABLE 1 synaptic connection strength between primary cortical neuronal cells
As an example of the present invention, different synaptic connection weights in a model expression may be assigned as corresponding synaptic connection strengths according to table 1.
In the ascending pathway of the thalamus and primary cortex, there is an inhibitory modulation of inputs to the thalamus by A1, which is simulated using the expression g (t) to simulate the inhibitory modulation of inputs to the thalamus by the primary cortex as follows.
Wherein g is 0 The initial value of g (t) can be 1,and->The time constant of g (t) can take values of 1500ms and 20ms.
Wherein τ u 、τ p 、τ s 、τ v The time required for four classes of neuronal cells to discharge upon receiving an excitation, respectively.
Time constant τ in model expression I 、τ D 、And->Is an important parameter for simulating different types of response modes. These parameters affect the output current profile of the thalamus and thus the firing rate profile of each neuron population.
τ D And τ I In the case of not more than 20, when τ D ＞τ I The simulated curve approaches the Oset response as the difference between the two parameters increases, when τ D ＜τ I As the difference between the two parameters increases, the simulation curve approaches the Offset response mode.
As an exampleSetting τ I ＝0.5，τ D =10 simulated Onset response; setting τ I ＝5，τ D =0.5 simulated Offset response. Referring to fig. 5, there is shown an input current versus time plot from the thalamus for an Onset response and an Offset response.
When τ is I And τ D Approaching equality with no more than 20, the thalamus output curve will appear with two peaks, and the heights of the two peaks will then tend to equalize.
As one example, set τ I ＝5，τ D =10 to simulate On-Off response. Referring to fig. 6, an On-Off response versus input current from the thalamus is shown.
When τ is D ≥50、τ I When=0.5, the response mode approaches to the surained response mode.
As one example, set τ D ＝50、τ I =0.5 to simulate a susained response. Referring to fig. 7, a plot of input current versus time from the thalamus in response to the subtended by the upstained is shown.
It can be considered that the exciting current I Facil (t) and Rapid inhibitory factor D Fast The synergy of (t) is an important factor in determining the simulated response pattern of the neuron population to the acoustic stimulus.
Slow inhibitory factor D Slow (t) simulated the spike attenuation of the firing rate curve at off. As an example, at parameter τ D And τ I In the case of 5, D Slow Time constant of (t)And->The difference in height between the two peaks of the firing rate curve is affected. When->Time->The second peak of the simulated thalamus output current curve is strongly compressedMake->Time->The second peak is slightly inhibited, and the heights of the two peaks tend to be equal. Referring to fig. 8, a comparative schematic showing the second peak of the thalamus output current curve strongly suppressed and the two peak heights tending to be equal is shown.
And step 403, simulating and outputting the release rate curve of each neuron cell by adopting the release rate curve simulation model.
And solving the release rate curve simulation model expression by adopting a numerical method through MATLAB, circularly solving and storing the release rate corresponding to each time point along with the time change, and finally drawing and outputting the release rate curve of the Pyr, SST, PV, VIP four types of neuron cells along with the time change.
In one embodiment of the invention, the firing rate curves for the neuronal cells include a sound-on firing rate curve, a sound-off firing rate curve, a sound-on-off firing rate curve, and a sound-on-fire firing rate curve.
In an embodiment of the present invention, by adjusting the time constant of each factor in the thalamus output current equation, various response firing patterns of each neuron cell can be simulated, so that the firing rate curve of each neuron cell can include a sound-on-fire firing rate curve, a sound-off firing rate curve, a sound-on-off firing rate curve, and a sound-on-fire firing rate curve.
In one embodiment of the present invention,
when τ is D ≤20、τ I Less than or equal to 20, and tau D ＞τ I At the same time, with tau D And τ I The curve of the difference increase simulation approaches to the sound giving start type release rate curve;
when τ is D ≤20、τ I Less than or equal to 20, and tau D ＜τ I At the same time, with tau D And τ I Curve of difference increase simulation approaches to a sound-giving junctionA beam release rate curve;
when τ is D ≤20、τ I Less than or equal to 20, and tau D And τ I When approaching to being equal, two peaks appear on the simulated curve, and the simulated curve obtains a sound giving start-end type release rate curve;
when τ is D ≥50、τ I =0.5, simulation results in a sustained release rate curve for sound delivery, τ D Typically taking a value close to 50.
In an embodiment of the present invention, various response firing patterns for each neuronal cell are simulated by adjusting the time constant of each factor in the thalamus output current equation. When τ is D ≤20、τ I Less than or equal to 20, and tau D ＞τ I At the same time, with tau D And τ I The curve of the difference increase simulation approaches to the sound giving start type release rate curve; when τ is D ≤20、τ I Less than or equal to 20, and tau D ＜τ I At the same time, with tau D And τ I The curve of the difference increase simulation approaches to the sound giving end type release rate curve; when τ is D ≤20、τ I Less than or equal to 20, and tau D And τ I When approaching to being equal, two peaks appear on the simulated curve, and the simulated curve obtains a sound giving start-end type release rate curve; when τ is D ≥50、τ I =0.5, and the simulation yields a sustained release rate curve for sound.
As an example, referring to fig. 9, a firing rate versus time curve of four classes of neuronal cells of the primary auditory cortex is shown.
The method comprises the steps of obtaining stimulation data of neuron cells of a primary auditory cortex, inputting the stimulation data of the neuron cells including a vertebral body cell Pyr, a somatostatin positive cell SST, a parvalbumin positive cell PV and an intestinal vascular activity polypeptide positive cell VIP into a release rate curve simulation model, wherein the release rate curve simulation model comprises a Pyr release rate curve simulation sub-model, an SST release rate curve simulation sub-model, a PV release rate curve simulation sub-model and a VIP release rate curve simulation sub-model, simulating by adopting the release rate curve simulation model, and outputting release rate curves of each neuron cell. The invention focuses on the role of VIP neurons in primary auditory cortex, builds a release rate curve simulation model comprising the VIP neurons, and reflects the mutual inhibition/activation relationship between VIP and other types of neuron groups in the primary auditory cortex; aiming at the difference of input intensity of thalamus to each neuron, the invention gives different weights to thalamus current variables input by different neurons according to experimental data, so that the model is more close to physiological significance; in addition, the invention can simulate various release modes of the neuron population by adjusting the time constant of each factor in the release rate curve simulation model equation.
It should be noted that, for simplicity of description, the method embodiments are shown as a series of acts, but it should be understood by those skilled in the art that the embodiments are not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred embodiments, and that the acts are not necessarily required by the embodiments of the invention.
Referring to fig. 10, a block diagram of a primary auditory cortex neuron cell firing rate curve simulation device according to an embodiment of the present invention is shown, which may specifically include the following modules:
a stimulus data acquisition module 1001 for acquiring stimulus data of neuronal cells of a primary auditory cortex; the neuron cells comprise a centrum cell Pyr, a somatostatin positive cell SST, a parvalbumin positive cell PV and an intestinal vascular activity polypeptide positive cell VIP;
an input module 1002 for inputting stimulation data of the neuronal cells into a firing rate curve simulation model; the release rate curve simulation model comprises a Pyr release rate curve simulation sub-model, an SST release rate curve simulation sub-model, a PV release rate curve simulation sub-model and a VIP release rate curve simulation sub-model;
and a simulation module 1003, configured to simulate and output a firing rate curve of each of the neuronal cells by using the firing rate curve simulation model.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
In addition, the embodiment of the invention also provides an electronic device, as shown in fig. 11, which comprises a processor 1101, a communication interface 1102, a memory 1103 and a communication bus 1104, wherein the processor 1101, the communication interface 1102 and the memory 1103 complete communication with each other through the communication bus 1104,
a memory 1103 for storing a computer program;
the processor 1101 is configured to implement the primary auditory cortex neuron cell firing rate curve simulation method as described in the embodiment when executing the program stored in the memory 1103.
The communication bus mentioned by the above terminal may be a peripheral component interconnect standard (Peripheral Component Interconnect, abbreviated as PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated as EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface is used for communication between the terminal and other devices.
The memory may include random access memory (Random Access Memory, RAM) or non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field-programmable gate arrays (Field-Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In yet another embodiment provided by the present invention, as shown in fig. 12, there is further provided a computer-readable storage medium 1201 having instructions stored therein, which when run on a computer, cause the computer to perform the primary auditory cortex neuron cell firing rate curve simulation method described in the above embodiment.
In yet another embodiment of the present invention, there is also provided a computer program product containing instructions that, when run on a computer, cause the computer to perform the primary cortical neuron cell firing rate curve simulation method described in the above embodiment.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In this specification, each embodiment is described in a related manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (8)
1. A method for simulating a primary auditory cortex neuron cell firing rate curve, the method comprising:
obtaining stimulation data of a neuron cell of a primary auditory cortex; the neuron cells comprise a centrum cell Pyr, a somatostatin positive cell SST, a parvalbumin positive cell PV and an intestinal vascular activity polypeptide positive cell VIP;
inputting the stimulation data of the neuron cells into a release rate curve simulation model; the release rate curve simulation model comprises a Pyr release rate curve simulation sub-model, an SST release rate curve simulation sub-model, a PV release rate curve simulation sub-model and a VIP release rate curve simulation sub-model;
and simulating and outputting the release rate curve of each neuron cell by adopting the release rate curve simulation model.
2. The method of claim 1, wherein the stimulus data is a current generated by analog noise stimulus, and is a square wave signal.
3. The method of claim 1, wherein the firing rate profile for the neuronal cells comprises a sound-on-fire firing rate profile, a sound-off-fire firing rate profile, a sound-on-off firing rate profile, and a sound-on-fire firing rate profile.
4. The method according to claim 2, wherein the method further comprises:
the expression of the release rate curve simulation model of the neuron cells is as follows:
wherein u (t), p (t), s (t) and v (t) respectively represent the release rate curves of Pyr, PV, SST and VIP neuron cells, and t is time;
where f (x) is a neuron spike response function, and the formula is:
wherein x is all excitatory and inhibitory inputs received by all neuronal cells of the primary auditory cortex; r is 3;
wherein I is Thal (t) is an external input from the thalamus, calculated using stimulus data:
I Thal (t)＝I Facil (t)D Fast (t)D Slow (t)
wherein the I is Facil (t) represents the excitability produced by postsynaptic neurons activated by acoustic signalsGain of sexual synapses; τ I Is an excitatory time constant;
D Fast (t) and D Slow (t) represents the regulation of excitatory currents by fast and slow inhibitory neurons, respectively, defined as:
wherein A is D，fast And A D，slow Gain of inhibitory regulation of fast inhibitory neurons and slow inhibitory neurons, respectively; τ D In order to rapidly suppress the time constant of the factor,and->Is the time constant of the slow inhibitor.
Wherein q is u 、q p 、q s 、q v Weight of Pyr, PV, SST and VIP neuronal cells receiving thalamus input, respectively, q according to the difference in intensity of Pyr, PV, SST and VIP neuronal cells receiving thalamus input u 、q p 、q s 、q v Assigning a value;
wherein w is ij Is the connection strength between different neuronal cell synapses and the connection strength between the same neuronal cell synapses;
wherein g (t) is used to simulate inhibitory modulation of input to the thalamus by the primary auditory cortex, expressed as follows:
wherein g is 0 Is the initial value of g (t),and->A time constant of g (t);
wherein τ u 、τ p 、τ s 、τ v The time required for four classes of neuronal cells to discharge upon receiving an excitation, respectively.
5. The method according to claim 4, wherein the method further comprises:
when τ is D ≤20、τ I Less than or equal to 20, and tau D >τ I At the same time, with tau D And τ I The curve of the difference increase simulation approaches to the sound giving start type release rate curve;
when τ is D ≤20、τ I Less than or equal to 20, and tau D ＜τ I At the same time, with tau D And τ I The curve of the difference increase simulation approaches to the sound giving end type release rate curve;
when τ is D ≤20、τ I Less than or equal to 20, and tau D And τ I When approaching to being equal, two peaks appear on the simulated curve, and the simulated curve obtains a sound giving start-end type release rate curve;
when τ is D ≥50、τ I =0.5, simulation results in a sustained release rate curve for sound delivery, τ D Typically taking a value close to 50.
6. A primary auditory cortex neuron cell firing rate curve simulation device, the device comprising:
the stimulation data acquisition module is used for acquiring the stimulation data of the neuron cells of the primary auditory cortex; the neuron cells comprise a centrum cell Pyr, a somatostatin positive cell SST, a parvalbumin positive cell PV and an intestinal vascular activity polypeptide positive cell VIP;
the input module is used for inputting the stimulation data of the neuron cells into a release rate curve simulation model; the release rate curve simulation model comprises a Pyr release rate curve simulation sub-model, an SST release rate curve simulation sub-model, a PV release rate curve simulation sub-model and a VIP release rate curve simulation sub-model;
and the simulation module is used for simulating and outputting the release rate curve of each neuron cell by adopting the release rate curve simulation model.
7. An electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus:
the memory is used for storing a computer program;
the processor, when executing the program stored in the memory, is configured to implement the primary auditory cortex neuron cell firing rate curve simulation method according to any one of claims 1 to 5.
8. One or more computer-readable media having instructions stored thereon that, when executed by one or more processors, cause the processors to perform the primary cortical neuron cell firing rate curve simulation method of any one of claims 1-5.
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