CN111291879B - Signal generating device with habit and sensitization - Google Patents

Signal generating device with habit and sensitization Download PDF

Info

Publication number
CN111291879B
CN111291879B CN202010233459.1A CN202010233459A CN111291879B CN 111291879 B CN111291879 B CN 111291879B CN 202010233459 A CN202010233459 A CN 202010233459A CN 111291879 B CN111291879 B CN 111291879B
Authority
CN
China
Prior art keywords
input
voltage
memristor
feedback
module
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010233459.1A
Other languages
Chinese (zh)
Other versions
CN111291879A (en
Inventor
洪庆辉
颜任遨
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hunan University
Original Assignee
Hunan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hunan University filed Critical Hunan University
Priority to CN202010233459.1A priority Critical patent/CN111291879B/en
Publication of CN111291879A publication Critical patent/CN111291879A/en
Application granted granted Critical
Publication of CN111291879B publication Critical patent/CN111291879B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Neurology (AREA)
  • Electronic Switches (AREA)
  • Electrotherapy Devices (AREA)

Abstract

The invention relates to a signal generating device with habit and sensitization and an adjusting method, belonging to the technical field of circuit signal generation and adjustment. The method comprises the steps that habituation and sensitization reactions are carried out on signal behaviors which change along with time through an input module, an output module and a feedback module; habituation refers to the process of gradually weakening the response to an input signal of an input module when repeated; sensitization refers to the process of producing a weak output response after receiving a weak signal, with a significant increase in response to the signal after receiving a strong signal. The invention has the beneficial effects that: the habit and sensitization technology related to the generation and adjustment of the circuit signals is realized.

Description

Signal generating device with habit and sensitization
Technical Field
The invention relates to a signal generating device with habit and sensitization and an adjusting method, belonging to the technical field of circuit signal generation and adjustment.
Background
The invention relates to a habituation and sensitization technology related to circuit signal generation and adjustment, which is not related to the traditional circuit structure device at present, and partially adopts a software algorithm to realize habituation and sensitization control, and the inventor starts with a hardware structure to realize a habituation and sensitization signal generation device and an adjustment method.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, thereby realizing the habit and sensitization technology related to the generation and adjustment of circuit signals.
The principle of the invention is that in biology a synapse is a contact structure that delivers a pulse from one neuron to another or from one cell to another. Synaptic weights represent the degree of correlation between adjacent neurons or cells; the input module is used for changing the synaptic weight, and the input module corresponds to synapses. In the nervous system, when the input of a neuron reaches a certain threshold, the neuron is activated and outputs a peak signal to the next neuron. When the input is below the threshold, the neuron does not output, and the output module corresponds to the neuron output. In a normal organism, the conduction potential caused by weak stimulation carries out neuron feedback within a certain range, and a feedback module corresponds to the neuron feedback.
The technical scheme of the invention is that the habit and sensitization reaction is carried out on the signal behavior which changes along with time through an input module, an output module and a feedback module; habituation refers to the process of gradually weakening the response to an input signal of an input module when repeated; sensitization refers to the process of producing a weak output response after receiving a weak signal, with a significant increase in response to the signal after receiving a strong signal.
The input module comprises an input memristor Mk and input voltages u respectively connected with the input memristor Mk k And feedback voltage u f Feedback voltage u f An insulated gate bipolar transistor is also connected; k denotes the input branch where the input memristor Mk is located, k=1, 2, … n. In the present invention, the synaptic weight is reset to the memristive value Mk.
Insulated Gate Bipolar Transistor (IGBT) guarantees feedback voltage u f Only in the absence of an input voltage, the total current input i will sum up all the current flows on each branch and flow into the next module;
the memristance of the input memristor Mk changes along with the change of voltage, namely the habit and sensitization are realized by the input voltage u k And feedback voltage u f A process of changing the weight of the input memristor Mk;
when the forward voltage acts on the input memristor Mk and the voltage is higher than the positive threshold value thereof, the memristance of the input memristor Mk is reduced; if the forward voltage is lower than the positive threshold value, the memristance of the input memristor Mk is kept unchanged;
when a negative voltage acts on the input memristor Mk and the voltage is lower than a negative threshold value of the input memristor Mk, the memristor value of the input memristor Mk is increased;
normally, there is a time interval between two adjacent signals. In the gap between two adjacent signals, the input memristor Mk can pass through the feedback voltage u f The negative threshold of the input memristor Mk is set close to zero, so that with a smaller feedback voltage u f The memristance of the input memristor Mk is gradually increased such that the current on the respective branch k (k=1, 2, … n) decreases.
The output module is used for outputting a pulse voltage corresponding to the input current when the input current is larger than a preset threshold value; the output module is an integrating circuit, and the waveform of the output voltage is closer to the pulse waveform in living beings by integrating the total current i in the input module.
The CMOS of the output module can set the threshold value u according to the actual situation s Only the voltage obtained after integration is higher than this threshold u s Can be output to the next neuron.
The feedback module is used for transmitting corresponding feedback signals to the input module, and then adjusting the structure of the whole circuit, in the circuit, the voltage caused by weak stimulation is set to be 1mV to 30mV, and the stimulation with the voltage higher than 30mV is regarded as harmful strong stimulation; by setting the threshold of the double pole double throw switches S1, S2, the nociceptive strong stimulus and the weak stimulus are distinguished: if the stimulus is weak, the switch S1 is opened, the switch S2 is opened, and the positive voltage V2 is input into the next circuit; conversely, if a strong stimulus, switches S1 and S2 are closed, the negative voltage V1 is in series with the positive voltage V2; meanwhile, the absolute value of the set negative voltage V1 is larger than that of the positive voltage V2, so that the polarity of the total voltage generated by series connection is opposite to that of the voltage V2, and the part of circuits in the feedback module distinguish strong stimulation from weak stimulation through the voltages with different polarities; in normal organisms, the conduction potential caused by weak stimulation is within a certain range.
Since the habituation process is cumulative and memory, the feedback memristor Mf is a good choice for remembering the state of the circuit. If the stimulus is weak, a positive voltage V2 flows into the negative end of the feedback memristor Mf, and the memristance of the feedback memristor Mf is gradually increased; if the stimulus is strong, negative voltage V1+V2 flows into the negative end of the feedback memristor Mf, and the memristor Mf memristor gradually drops; by controlling the voltages V1 and V2 and the parameters of the feedback memristor Mf, the feedback memristor Mf can slowly rise under weak stimulation and rapidly fall under strong stimulation.
Setting parameters of the feedback memristor Mf, so that the memristance of the feedback memristor Mf is adjusted to the maximum by three continuous weak stimulation, and the memristance of the feedback memristor Mf is adjusted to the minimum by one strong stimulation; when input is available, the feedback voltage is invalid in the input module; in the input gap, the feedback voltage u f Work at the input module.
The feedback voltage of the input end can be regulated through the double-pole double-throw switches S3 and S4, if the memristor Mf memristor value is maximum, the double-pole double-throw switches S3 and S4 are closed, the voltage V3 is connected with the voltage V4 in series, and the voltage V is input in the form of feedback voltage, so that the input memristor Mk of each branch of the input module is regulated; in the input gap, by increasing the memristance of the input memristor Mk, the output response of the weak stimulus decreases with decreasing weight; the increase in synaptic weight due to sensitization will decrease slowly over time and the voltage V4 will gradually adjust the weight of the input in the input gap, whether or not the circuit is in habitual mode.
The habituation adjusting method comprises the steps that when weak stimulus is received, the value of the feedback memristor Mf is reduced, and in an input gap, the value of the feedback memristor Mf is slowly increased; by adjusting the related parameters of the memory feedback resistor Mf, the feedback memristor Mf is set to have a resistance value close to the minimum value R after being continuously subjected to three weak stimuli ON The method comprises the steps of carrying out a first treatment on the surface of the In addition, the magnitude of memristance increase in the input gap is the same as the magnitude of memristance decrease caused by the next weak stimulus, i.e., by oscillating the memristance around a minimum, the system stabilizes in the habitual mode.
The habit removing adjusting method comprises the steps that when strong stimulus is input, the voltage is higher than the threshold value of the input memristor Mk, the memristor value of the input memristor Mk is reduced, the input current is increased, and the output voltage is increased; in addition, the feedback memristor Mf also rises rapidly and jumps out of habit under the action of the strong stimulus signal.
The invention has the beneficial effects that: the habit and sensitization technology related to the generation and adjustment of the circuit signals is realized. When the strong stimulus is input, the voltage of the strong stimulus is higher than the threshold value of the modulus, and the modulus is reduced, so that the input current and the output of the strong stimulus are enhanced; in addition, the disturbance in the feedback module also rises rapidly, jumping out of habit, due to the strong stimulus signal. It is achieved that when a plurality of strong stimuli is input, not only the output amplitude of the subsequent weak stimulus increases, but also the output amplitude produced by the strong stimulus itself increases. The sensitivity will gradually decrease over time.
Drawings
Fig. 1 is a schematic diagram of the present invention.
Fig. 2 shows the structure of an embodiment of the present invention.
FIG. 3 is a diagram of an input memristor branch.
Fig. 4 is a block diagram of an output module.
Fig. 5 is a block diagram of a feedback module.
FIG. 6 is a graph of simulation results for achieving habituation upon receiving continuous stimulation.
FIG. 7 is a graph showing simulation results of sensitization by continuous stimulation.
Detailed Description
The preferred embodiment of the present invention will be further described with reference to fig. 1 to 7, in which the input module, the output module and the feedback module are used to perform habituation and sensitization reactions on the time-varying signal behaviors; habituation refers to the process of gradually weakening the response to an input signal of an input module when repeated; sensitization refers to the process of producing a weak output response after receiving a weak signal, with a significant increase in response to the signal after receiving a strong signal.
The input module comprises an input memristor Mk and input voltages u respectively connected with the input memristor Mk k And feedback voltage u f Feedback voltage u f An insulated gate bipolar transistor is also connected; k represents the input branch where the input memristor Mk is located, k=1, 2, … n; the input branch circuit is shown in fig. 3. In biology, synapses are a contact structure that delivers impulses from one neuron to another or from one cell to another. Synaptic weights represent the degree of correlation between adjacent neurons or cells; the input module is used for changing the synaptic weight, and the input module corresponds to the synapse.
Insulated Gate Bipolar Transistor (IGBT) guarantees feedback voltage u f Only in the absence of an input voltage, the total current input i will sum up all the current flows on each branch and flow into the next module;
memristance of input memristor Mk as a function of voltageChanging the sense, i.e. habituation and sensitization, by the input voltage u k And feedback voltage u f A process of changing the weight of the input memristor Mk;
when the forward voltage acts on the input memristor Mk and the voltage is higher than the positive threshold value thereof, the memristance of the input memristor Mk is reduced; if the forward voltage is lower than the positive threshold value, the memristance of the input memristor Mk is kept unchanged;
when a negative voltage acts on the input memristor Mk and the voltage is lower than a negative threshold value of the input memristor Mk, the memristor value of the input memristor Mk is increased;
normally, there is a time interval between two adjacent signals. In the gap between two adjacent signals, the input memristor Mk can pass through the feedback voltage u f The negative threshold of the input memristor Mk is set close to zero, so that with a smaller feedback voltage u f The memristance of the input memristor Mk is gradually increased such that the current on the respective branch k (k=1, 2, … n) decreases.
The output module is used for outputting a pulse voltage corresponding to the input current when the input current is larger than a preset threshold value; the output module is an integrating circuit, as shown in fig. 4, and the waveform of the output voltage is closer to the waveform of the pulse in the living being by integrating the total current i in the input module.
In the nervous system, when the input of a neuron reaches a certain threshold, the neuron is activated and outputs a peak signal to the next neuron. When the input is below the threshold, the neuron does not output, and the output module corresponds to the neuron output.
The CMOS of the output module can set the threshold value u according to the actual situation s Only the voltage obtained after integration is higher than this threshold u s Can be output to the next neuron.
The feedback module is used for transmitting corresponding feedback signals to the input module, and then adjusting the structure of the whole circuit, as shown in fig. 5. In normal organisms, the conduction potential caused by weak stimulation is fed back to neurons in a certain range, and the feedback module corresponds to the neuron feedback. In the present circuit, the voltage caused by weak stimulation is set to 1mV to 30mV, and stimulation with a voltage greater than 30mV is considered as damaging strong stimulation; by setting the threshold of the double pole double throw switches S1, S2, the nociceptive strong stimulus and the weak stimulus are distinguished: if the stimulus is weak, the switch S1 is opened, the switch S2 is opened, and the positive voltage V2 is input into the next circuit; conversely, if a strong stimulus, switches S1 and S2 are closed, the negative voltage V1 is in series with the positive voltage V2; meanwhile, the absolute value of the set negative voltage V1 is larger than that of the positive voltage V2, so that the polarity of the total voltage generated by series connection is opposite to that of the voltage V2, and the part of circuits in the feedback module distinguish strong stimulation from weak stimulation through the voltages with different polarities; in normal organisms, the conduction potential caused by weak stimulation is within a certain range.
Since the habituation process is cumulative and memory, the feedback memristor Mf is a good choice for remembering the state of the circuit. If the stimulus is weak, a positive voltage V2 flows into the negative end of the feedback memristor Mf, and the memristance of the feedback memristor Mf is gradually increased; if the stimulus is strong, negative voltage V1+V2 flows into the negative end of the feedback memristor Mf, and the memristor Mf memristor gradually drops; by controlling the voltages V1 and V2 and the parameters of the feedback memristor Mf, the feedback memristor Mf can slowly rise under weak stimulation and rapidly fall under strong stimulation.
Setting parameters of the feedback memristor Mf, so that the memristance of the feedback memristor Mf is adjusted to the maximum by three continuous weak stimulation, and the memristance of the feedback memristor Mf is adjusted to the minimum by one strong stimulation; when input is available, the feedback voltage is invalid in the input module; in the input gap, the feedback voltage u f Work at the input module.
The feedback voltage of the input end can be regulated through the double-pole double-throw switches S3 and S4, if the memristor Mf memristor value is maximum, the double-pole double-throw switches S3 and S4 are closed, the voltage V3 is connected with the voltage V4 in series, and the voltage V is input in the form of feedback voltage, so that the input memristor Mk of each branch of the input module is regulated; in the input gap, by increasing the memristance of the input memristor Mk, the output response of the weak stimulus decreases with decreasing weight; the increase in synaptic weight due to sensitization will decrease slowly over time and the voltage V4 will gradually adjust the weight of the input in the input gap, whether or not the circuit is in habitual mode.
The habituation adjusting method comprises the steps that when weak stimulus is received, the value of the feedback memristor Mf is reduced, and in an input gap, the value of the feedback memristor Mf is slowly increased; by adjusting the related parameters of the memory feedback resistor Mf, the feedback memristor Mf is set to have a resistance value close to the minimum value R after being continuously subjected to three weak stimuli ON The method comprises the steps of carrying out a first treatment on the surface of the In addition, the magnitude of memristance increase in the input gap is the same as the magnitude of memristance decrease caused by the next weak stimulus, i.e., by oscillating the memristance around a minimum, the system stabilizes in the habitual mode.
Given a current source, the memristance value can be determined by measuring the negative terminal voltage of the feedback memristor Mf, since the three weak stimuli must be continuous, i.e. the interval time between the weak stimuli should not be too long, the value of the current source in the module is set to 80 μa, which causes the voltage across the feedback memristor Mf to exceed its threshold; the feedback memristor Mf is slowly reduced in the input gap; given a continuous weak stimulus input, memristive oscillations to some extent are formed; if the input gap is short enough, the memristance Mf will oscillate below the set standard resistance, which means that the system enters an inertial state, as shown in FIG. 6.
The habit removing adjusting method comprises the steps that when strong stimulus is input, the voltage is higher than the threshold value of the input memristor Mk, the memristor value of the input memristor Mk is reduced, the input current is increased, and the output voltage is increased; in addition, the feedback memristor Mf also rises rapidly and jumps out of habit under the action of the strong stimulus signal, as shown in fig. 7.

Claims (3)

1. A signal generating device with habit and sensitization is provided, which carries out habit and sensitization reaction on signal behavior changing along with time through an input module, an output module and a feedback module; habituation refers to the process of gradually weakening the response to an input signal of an input module when repeated; sensitization refers to the process of generating a weak output response after receiving a weak signal, the response to the signal significantly increasing after receiving a strong signal;
the input module comprises an input memristor Mk and input voltages u respectively connected with the input memristor Mk k And feedback voltage u f Feedback voltage u f An insulated gate bipolar transistor is also connected; k represents the input branch where the input memristor Mk is located, k=1, 2, … n;
insulated Gate Bipolar Transistor (IGBT) guarantees feedback voltage u f Only in the absence of an input voltage, the total current input i will sum up all the current flows on each branch and flow into the next module;
the memristance of the input memristor Mk changes along with the change of voltage, namely the habit and sensitization are realized by the input voltage u k And feedback voltage u f A process of changing the weight of the input memristor Mk;
when the forward voltage acts on the input memristor Mk and the voltage is higher than the positive threshold value thereof, the memristance of the input memristor Mk is reduced; if the forward voltage is lower than the positive threshold value, the memristance of the input memristor Mk is kept unchanged;
when a negative voltage acts on the input memristor Mk and the voltage is lower than a negative threshold value of the input memristor Mk, the memristor value of the input memristor Mk is increased;
in the gap between two adjacent signals, the input memristor Mk can pass through the feedback voltage u f Adjusting, the negative threshold of the input memristor Mk is set close to zero, with a small feedback voltage u f Gradually increasing the memristance of the input memristor Mk to reduce the current on the corresponding branch k;
the output module is used for outputting a pulse voltage corresponding to the input current when the input current is larger than a preset threshold value; the output module is an integrating circuit, and the waveform of the output voltage is closer to the pulse waveform in the living beings by integrating the total current i in the input module;
the CMOS of the output module can set the threshold value u according to the actual situation s Only the voltage obtained after integration is higher than this threshold u s Can output to the next neuron;
the method is characterized in that:
the feedback module is used for transmitting corresponding feedback signals to the input module, and then adjusting the structure of the whole circuit;
in the present circuit, the voltage caused by weak stimulation is set to 1mV to 30mV, and stimulation with a voltage greater than 30mV is considered as damaging strong stimulation;
by setting the threshold of the double pole double throw switches S1, S2, the nociceptive strong stimulus and the weak stimulus are distinguished: if the stimulus is weak, the switch S1 is opened, the switch S2 is opened, and the positive voltage V2 is input into the next circuit; conversely, if a strong stimulus, switches S1 and S2 are closed, the negative voltage V1 is in series with the positive voltage V2;
meanwhile, the absolute value of the set negative voltage V1 is larger than that of the positive voltage V2, so that the polarity of the total voltage generated by series connection is opposite to that of the voltage V2, and the part of circuits in the feedback module distinguish strong stimulation from weak stimulation through the voltages with different polarities;
if the stimulus is weak, a positive voltage V2 flows into the negative end of the feedback memristor Mf, and the memristance of the feedback memristor Mf is gradually increased; if the stimulus is strong, negative voltage V1+V2 flows into the negative end of the feedback memristor Mf, and the memristor Mf memristor gradually drops;
when input is available, the feedback voltage is invalid in the input module; in the input gap, the feedback voltage u f Working at an input module;
the feedback voltage of the input end can be regulated through the double-pole double-throw switches S3 and S4, if the memristor Mf memristor value is maximum, the double-pole double-throw switches S3 and S4 are closed, the voltage V3 is connected with the voltage V4 in series, and the voltage V is input in the form of feedback voltage, so that the input memristor Mk of each branch of the input module is regulated; in the input gap, by increasing the memristance of the input memristor Mk, the output response of the weak stimulus decreases with decreasing weight;
the increase in synaptic weight due to sensitization will decrease slowly over time and the voltage V4 will gradually adjust the weight of the input in the input gap, whether or not the circuit is in habitual mode.
2. A habituation and sensitization signal generating apparatus according to claim 1, comprising the following adjusting method:
the habituation adjusting method comprises the steps that when weak stimulus is received, the value of the feedback memristor Mf is reduced, and in an input gap, the value of the feedback memristor Mf is slowly increased; by adjusting the related parameters of the memory feedback resistor Mf, the feedback memristor Mf is set to have a resistance value close to the minimum value R after being continuously subjected to three weak stimuli ON The method comprises the steps of carrying out a first treatment on the surface of the In addition, the magnitude of memristance increase in the input gap is the same as the magnitude of memristance decrease caused by the next weak stimulus, i.e., by oscillating the memristance around a minimum, the system stabilizes in the habitual mode.
3. A habituation and sensitization signal generating apparatus according to claim 1, comprising the following adjusting method:
the habit removing adjusting method comprises the steps that when strong stimulus is input, the voltage is higher than the threshold value of the input memristor Mk, the memristor value of the input memristor Mk is reduced, the input current is increased, and the output voltage is increased; in addition, the feedback memristor Mf also rises rapidly and jumps out of habit under the action of the strong stimulus signal.
CN202010233459.1A 2020-03-29 2020-03-29 Signal generating device with habit and sensitization Active CN111291879B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010233459.1A CN111291879B (en) 2020-03-29 2020-03-29 Signal generating device with habit and sensitization

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010233459.1A CN111291879B (en) 2020-03-29 2020-03-29 Signal generating device with habit and sensitization

Publications (2)

Publication Number Publication Date
CN111291879A CN111291879A (en) 2020-06-16
CN111291879B true CN111291879B (en) 2023-08-22

Family

ID=71027261

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010233459.1A Active CN111291879B (en) 2020-03-29 2020-03-29 Signal generating device with habit and sensitization

Country Status (1)

Country Link
CN (1) CN111291879B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112998720B (en) * 2021-01-29 2023-07-25 广东技术师范大学 Intelligent early-warning wearable heart rate monitoring circuit and control method thereof

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108804786A (en) * 2018-05-26 2018-11-13 江西理工大学 A kind of memristor precircuit design method that associative neural network synaptic weight is plastic
JP2019003546A (en) * 2017-06-19 2019-01-10 株式会社デンソー Method for adjusting output level of neuron of multilayer neural network
CN110059816A (en) * 2019-04-09 2019-07-26 南京邮电大学 A kind of neural network element circuit based on memristor
CN110600610A (en) * 2019-09-26 2019-12-20 中国科学院微电子研究所 Artificial neural synapse device and preparation method thereof
CN110647982A (en) * 2019-09-26 2020-01-03 中国科学院微电子研究所 Artificial sensory nerve circuit and preparation method thereof

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9489618B2 (en) * 2014-05-27 2016-11-08 Purdue Research Foudation Electronic comparison systems
CN110413551B (en) * 2018-04-28 2021-12-10 上海寒武纪信息科技有限公司 Information processing apparatus, method and device

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019003546A (en) * 2017-06-19 2019-01-10 株式会社デンソー Method for adjusting output level of neuron of multilayer neural network
CN108804786A (en) * 2018-05-26 2018-11-13 江西理工大学 A kind of memristor precircuit design method that associative neural network synaptic weight is plastic
CN110059816A (en) * 2019-04-09 2019-07-26 南京邮电大学 A kind of neural network element circuit based on memristor
CN110600610A (en) * 2019-09-26 2019-12-20 中国科学院微电子研究所 Artificial neural synapse device and preparation method thereof
CN110647982A (en) * 2019-09-26 2020-01-03 中国科学院微电子研究所 Artificial sensory nerve circuit and preparation method thereof

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Yang Zhang et al..Memristive Model for Synaptic Circuits.《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—II: EXPRESS BRIEFS》.2017,第64卷(第7期),第767-771页. *

Also Published As

Publication number Publication date
CN111291879A (en) 2020-06-16

Similar Documents

Publication Publication Date Title
Wang et al. A novel memristor-based circuit implementation of full-function Pavlov associative memory accorded with biological feature
JP6644064B2 (en) Neuromorphic synapses, their arrays, and their systems
US9208434B2 (en) Neuromorphic system exploiting the intrinsic characteristics of memory cells
Lee et al. Oxide based nanoscale analog synapse device for neural signal recognition system
KR20140141778A (en) Synapse circuit for spike-timing dependent plasticity(stdp) operation and neuromorphic system
CN106981567A (en) A kind of artificial synapse device and its modulator approach based on photoelectric coupling memristor
CN109002647A (en) A kind of memristor associative memory neural network circuit with delay learning functionality
Huayaney et al. Learning in silicon beyond STDP: a neuromorphic implementation of multi-factor synaptic plasticity with calcium-based dynamics
CN111291879B (en) Signal generating device with habit and sensitization
Rasche et al. An improved silicon neuron
Indiveri et al. A VLSI neuromorphic device for implementing spike-based neural networks
Pershin et al. Experimental demonstration of associative memory with memristive neural networks
US10558910B2 (en) Neuromorphic device and method of adjusting a resistance change ratio thereof
Indiveri et al. Spike-based learning in VLSI networks of integrate-and-fire neurons
KR20210023277A (en) Integrate-and-fire neuron circuit using single-gated feedback field-effect transistor
Liu Analog VLSI circuits for short-term dynamic synapses
Lee et al. An electronic neuron with input-specific spiking
CN108777153B (en) Multi-terminal input synapse device and plasticity modulation method thereof
CN111630528B (en) Neural circuit and operation method
US11443177B2 (en) Artificial neuromorphic circuit and operation method
Kwon et al. Integrate-and-fire neuron circuit and synaptic device with a floating body MOSFET
Joshi et al. A carbon nanotube spiking cortical neuron with tunable refractory period and spiking duration
CN210924659U (en) Neural circuit
Elias et al. Analog VLSI neuromorph with spatially extensive dendritic tree
CN116894470B (en) Neural functional circuit for simulating animal operability conditional reflex

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant