EP3158509A1 - Memristive nanofiber neural networks - Google Patents

Memristive nanofiber neural networks

Info

Publication number
EP3158509A1
EP3158509A1 EP15810294.7A EP15810294A EP3158509A1 EP 3158509 A1 EP3158509 A1 EP 3158509A1 EP 15810294 A EP15810294 A EP 15810294A EP 3158509 A1 EP3158509 A1 EP 3158509A1
Authority
EP
European Patent Office
Prior art keywords
memristive
neural
node
network
fiber
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.)
Ceased
Application number
EP15810294.7A
Other languages
German (de)
French (fr)
Other versions
EP3158509A4 (en
Inventor
Juan Claudio Nino
Jack D. KENDALL
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.)
University of Florida
University of Florida Research Foundation Inc
Original Assignee
University of Florida
University of Florida Research Foundation Inc
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 University of Florida, University of Florida Research Foundation Inc filed Critical University of Florida
Publication of EP3158509A1 publication Critical patent/EP3158509A1/en
Publication of EP3158509A4 publication Critical patent/EP3158509A4/en
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11C—STATIC STORES
    • G11C13/00—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00, or G11C25/00
    • G11C13/0002—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00, or G11C25/00 using resistive RAM [RRAM] elements
    • G11C13/0007—Digital stores characterised by the use of storage elements not covered by groups G11C11/00, G11C23/00, or G11C25/00 using resistive RAM [RRAM] elements comprising metal oxide memory material, e.g. perovskites
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/065—Analogue means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0495—Quantised networks; Sparse networks; Compressed networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections

Definitions

  • a memristor is a two-terminal device that changes its resistance in response to the amount of electrical current that has previously flown through the device.
  • Memristors may be used in crossbar neural network architectures.
  • crossbar neural network multiple memristors are connected in a perpendicular crossbar array with memristor synapses at each crossing.
  • crossbar neural network architectures may require the use of complex designs in order to counteract parasitic leak paths.
  • redundant synapses do not exist in crossbar neural networks.
  • a recurrent connection in a crossbar neural network requires complex circuit layouts, and from a footprint point of view, crossbar designs scale quadratically in size with the number of neurons.
  • FIG. 1 is a drawing of a core-shell memristive nanofiber according to various embodiments of the present disclosure.
  • FIG. 2 is a drawing of an example of a nanofiber-based memristive neural network according to various embodiments of the present disclosure.
  • FIG. 3 is a drawing of an example of a nanofiber-based memristive neural network according to various embodiments of the present disclosure.
  • FIG. 4 is a drawing of an example of a simulation of a circuit layout of a nanofiber-based memristive neural network according to various embodiments of the present disclosure.
  • FIG. 5 is a flowchart illustrating an example of a method of creating a nanofiber-based memristive neural network according to various embodiments of the present disclosure.
  • a neural network may comprise populations of simulated neurons with weighted connections between them.
  • a neural network in accordance with various embodiments of the present disclosure may comprise an array of neural nodes that are interconnected using randomized connections of memristive fibers.
  • Such a neural node may comprise, for example, a Complimentary Metal-Oxide-Semiconductor (CMOS) Leaky Integrate-and-Fire (LIF) neural circuit, or any other suitable type of neural circuit.
  • CMOS Complimentary Metal-Oxide-Semiconductor
  • LIF Leaky Integrate-and-Fire
  • Each neural node may output one or more signals that are responsive to one or more input signals that the neural node has received. For example, upon one or more input current signals reaching a threshold value, a neural node may output a voltage spike to one or more output paths.
  • a neural network may also comprise memristive nanofibers.
  • Memristive nanofibers may be used to form artificial synapses in neural networks.
  • Each memristive nanofiber may couple one or more neural nodes to one or more other neural nodes.
  • one or more output signals may be transmitted from a particular neural node to one or more other neural nodes.
  • the particular neural nodes to which particular memristive nanofibers are connected may be randomized. In this regard, the particular neural nodes to which the memristive nanofibers are connected are not predetermined prior to the memristive fibers being connected to the one or more neural nodes.
  • the neural network may be used, for example, to model a Liquid State Machine (LSM).
  • LSMs Liquid State Machines
  • FIG. 14 A New Framework for Neural Computation Based on Perturbations, Neural Computation (Volume 14, Issue 1 1 ) (Nov. 1 1 , 2002), which is incorporated by reference herein in its entirety.
  • Each memristive nanofiber of the memristive neural network may comprise a conductive core, a memristive shell, and one or more electrodes.
  • Memristive nanofibers having a conductive core, memristive shell, and one or more electrodes may be formed using electrospinning or any other suitable method.
  • An electrode of the memristive nanofiber may serve as a conductive attachment point between the memristive nanofiber and an input or output terminal of a neural node.
  • the conductive core of the memristive nanofiber in some embodiments may comprise T1O2 and/or any other suitable material.
  • the memristive shell may at least partially surround the conductive core and thereby form a synapse between two or more neural nodes.
  • the memristive shell may cause the memristive nanofiber to form a connection that increases or decreases in strength in response to the past signals that have traveled through the memristive nanofiber.
  • the memristive shell may comprise Ti0 2 and/or any other suitable material with memristive properties.
  • the memristive nanofibers in the memristive neural network may form randomized connections between the neural nodes.
  • the probability of two neurons being connected decreases as the distance between neural nodes increases.
  • patterned electric fields may be used so that particular connection types are more likely to be formed between neural nodes when the connections are made.
  • a neural network may be formed using patterned electric fields or other suitable methods so that multiple layers of memristive nanofibers are created.
  • Such a neural network may also comprise connections that facilitate transmission of signals between various layers.
  • the layers and communication paths between layers are modeled after a neocortex of a brain.
  • Memristive neural networks in accordance with various embodiments of the present disclosure may provide various types of benefits.
  • such a memristive neural network may be capable of spike-timing-dependent plasticity (STDP).
  • STDP spike-timing-dependent plasticity
  • the memristive neural network may comprise random, spatially dependent connections.
  • the memristive neural network may comprise inhibitory outputs and/or recurrent connections.
  • the memristive neural networks in accordance with various embodiments of the present disclosure may have properties that are similar to biological neural networks.
  • Each memristive nanofiber 100 of a memristive neural network may comprise one or more electrodes 103, a conductive core 106, and a memristive shell 109.
  • An electrode 103 of the memristive nanofiber 100 may serve as a conductive attachment point between the memristive nanofiber 100 and an input or output terminal of a neural node.
  • the conductive core 106 of the memristive nanofiber 100 in some embodiments may comprise ⁇ 2 and/or any other suitable material with memristive properties.
  • the memristive shell 109 may at least partially surround the conductive core 106 and thereby form a synapse between two or more neural nodes.
  • the memristive shell 109 may cause the memristive nanofiber 100 to form a connection that increases or decreases in strength in response to the past signals that have traveled through the memristive nanofiber 100.
  • the memristive shell 109 may comprise Ti0 2 and/or any other suitable material, such as polyaniline.
  • the memristive nanofibers 100 can be used as memristive connections 206A-206J between CMOS-based neuron arrays 203A-203E in the nanofiber-based memristive neural network 200.
  • the memristive nanofibers 100 may form randomized memristive connections 206A-206J between inputs 212A- 212E and outputs 209A-209E of the CMOS-based neuron arrays 203A-203E.
  • the nanofiber-based memristive neural network 200 depicts examples of memristive nanofibers 100, referred to herein as memristive nanofibers 100A-100D, comprising conductive cores 106, referred to herein as the conductive cores 106A-106D, and memristive shells 109, referred to herein as the memristive shells 109A-109D.
  • the memristive nanofibers 100A-100D may be used as memristive connections 206A-206J between CMOS neurons 327A and 327B located on the silicon substrate 330.
  • Each memristive shell 109A-109D partially surrounds each conductive core 106A-106D and thereby forms a synapse 318A-318D between two or more neural nodes.
  • the input electrodes 321 A and 321 B and output electrodes 324A and 324B may serve as conductive attachment points between memristive nanofibers 100A-100D and input terminals 212A-212E or output terminals 209A-209E of the neural nodes.
  • FIG. 4 shown is a simulation 403 of an example of a circuit 406 of a nanofiber-based memristive neural connection according to various embodiments of the present disclosure.
  • the circuit 406 depicts a nanofiber-based memristive neural connection including memristive shells 109A-109D, voltage source V1 421 , voltage source V2 409, resistor R3 412, resistor R4 415, and resistor R1 418.
  • the simulation 403 shows that driving current through a nanofiber- based memristive neural connection will not cause the effects from opposing memristors on a nanofiber to cancel each other out.
  • FIG. 5 shown is a flowchart illustrating one example of a method of creating a nanofiber-based memristive neural network 200 (FIG. 2) according to various embodiments of the present disclosure.
  • stoichiometric nanofibers are synthesized using a precursor.
  • the stoichiometric nanofibers may comprise, for example, Ti0 2 and/or any other suitable material.
  • the precursor may be for example, titanium isopropoxide, titanium butoxide, or another suitable precursor.
  • a core-shell memristive nanofiber 100 (FIG. 1 ) is created.
  • the core-shell memristive nanofiber 100 (FIG. 1 ) may be created by electrospinning a stoichiometric Ti0 2 outer shell 109 with a doped conductive Ti0 2 -x core 106.
  • electrodes 103 may be deposited on the core-shell memristive nanofiber 100 (FIG. 1 ).
  • memristive properties are verified and a spike-timing dependent plasticity is implemented to create a computational model based on the nanofiber network response.
  • a physical prototype of a memristive nanofiber neural network 200 using CMOS neurons 327A-327B is created. Thereafter, the process ends.
  • Disjunctive language used herein such as the phrase "at least one of X, Y, or Z," unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language does not imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Software Systems (AREA)
  • Neurology (AREA)
  • Chemical & Material Sciences (AREA)
  • Materials Engineering (AREA)
  • Metal-Oxide And Bipolar Metal-Oxide Semiconductor Integrated Circuits (AREA)
  • Semiconductor Memories (AREA)
  • Micromachines (AREA)

Abstract

Disclosed are various embodiments of memristive neural networks comprising neural nodes. Memristive nanofibers are used to form artificial synapses in the neural networks. Each memristive nanofiber may couple one or more neural nodes to one or more other neural nodes.

Description

MEMRISTIVE NANOFIBER NEURAL NETWORKS
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a non-provisional of, and claims priority to, U.S. Provisional Application No. 62/014,201 , filed on June 19, 2014 and titled "Memri stive Neural Networks," which is incorporated by reference herein in its entirety.
BACKGROUND
[0002] A memristor is a two-terminal device that changes its resistance in response to the amount of electrical current that has previously flown through the device. Memristors may be used in crossbar neural network architectures. In a crossbar neural network, multiple memristors are connected in a perpendicular crossbar array with memristor synapses at each crossing. However, crossbar neural network architectures may require the use of complex designs in order to counteract parasitic leak paths. Additionally, redundant synapses do not exist in crossbar neural networks. Furthermore, a recurrent connection in a crossbar neural network requires complex circuit layouts, and from a footprint point of view, crossbar designs scale quadratically in size with the number of neurons.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 is a drawing of a core-shell memristive nanofiber according to various embodiments of the present disclosure.
[0004] FIG. 2 is a drawing of an example of a nanofiber-based memristive neural network according to various embodiments of the present disclosure. [0005] FIG. 3 is a drawing of an example of a nanofiber-based memristive neural network according to various embodiments of the present disclosure.
[0006] FIG. 4 is a drawing of an example of a simulation of a circuit layout of a nanofiber-based memristive neural network according to various embodiments of the present disclosure.
[0007] FIG. 5 is a flowchart illustrating an example of a method of creating a nanofiber-based memristive neural network according to various embodiments of the present disclosure.
DETAILED DESCRIPTION
[0008] The present disclosure is directed towards neural networks that use memristive fibers. Generally, a neural network may comprise populations of simulated neurons with weighted connections between them. A neural network in accordance with various embodiments of the present disclosure may comprise an array of neural nodes that are interconnected using randomized connections of memristive fibers. Such a neural node may comprise, for example, a Complimentary Metal-Oxide-Semiconductor (CMOS) Leaky Integrate-and-Fire (LIF) neural circuit, or any other suitable type of neural circuit. Each neural node may output one or more signals that are responsive to one or more input signals that the neural node has received. For example, upon one or more input current signals reaching a threshold value, a neural node may output a voltage spike to one or more output paths.
[0009] As mentioned above, a neural network may also comprise memristive nanofibers. Memristive nanofibers may be used to form artificial synapses in neural networks. Each memristive nanofiber may couple one or more neural nodes to one or more other neural nodes. In this way, one or more output signals may be transmitted from a particular neural node to one or more other neural nodes. The particular neural nodes to which particular memristive nanofibers are connected may be randomized. In this regard, the particular neural nodes to which the memristive nanofibers are connected are not predetermined prior to the memristive fibers being connected to the one or more neural nodes. As a result of the connections being randomized, the network obtained may exhibit sparse, random connectivity, which has been shown to increase the performance and efficiency of neural networks. Thus, the neural network may be used, for example, to model a Liquid State Machine (LSM). Further description regarding LSMs is provided in Wolfgang Maass et al., Real-Time Computing without Stable States: A New Framework for Neural Computation Based on Perturbations, Neural Computation (Volume 14, Issue 1 1 ) (Nov. 1 1 , 2002), which is incorporated by reference herein in its entirety.
[0010] Each memristive nanofiber of the memristive neural network may comprise a conductive core, a memristive shell, and one or more electrodes. Memristive nanofibers having a conductive core, memristive shell, and one or more electrodes may be formed using electrospinning or any other suitable method. An electrode of the memristive nanofiber may serve as a conductive attachment point between the memristive nanofiber and an input or output terminal of a neural node. The conductive core of the memristive nanofiber in some embodiments may comprise T1O2 and/or any other suitable material. The memristive shell may at least partially surround the conductive core and thereby form a synapse between two or more neural nodes. In this regard, the memristive shell may cause the memristive nanofiber to form a connection that increases or decreases in strength in response to the past signals that have traveled through the memristive nanofiber. The memristive shell may comprise Ti02 and/or any other suitable material with memristive properties.
[0011] As previously mentioned, in some embodiments, the memristive nanofibers in the memristive neural network may form randomized connections between the neural nodes. Thus, the probability of two neurons being connected decreases as the distance between neural nodes increases. Additionally, patterned electric fields may be used so that particular connection types are more likely to be formed between neural nodes when the connections are made. Additionally, a neural network may be formed using patterned electric fields or other suitable methods so that multiple layers of memristive nanofibers are created. Such a neural network may also comprise connections that facilitate transmission of signals between various layers. In one particular embodiment, the layers and communication paths between layers are modeled after a neocortex of a brain.
[0012] Memristive neural networks in accordance with various embodiments of the present disclosure may provide various types of benefits. For example, such a memristive neural network may be capable of spike-timing-dependent plasticity (STDP). Additionally, the memristive neural network may comprise random, spatially dependent connections. Furthermore, the memristive neural network may comprise inhibitory outputs and/or recurrent connections. As such, the memristive neural networks in accordance with various embodiments of the present disclosure may have properties that are similar to biological neural networks.
[0013] With reference to FIG. 1 , shown is an example of a core-shell memristive nanofiber 100 according to various embodiments of the present disclosure. Each memristive nanofiber 100 of a memristive neural network may comprise one or more electrodes 103, a conductive core 106, and a memristive shell 109. An electrode 103 of the memristive nanofiber 100 may serve as a conductive attachment point between the memristive nanofiber 100 and an input or output terminal of a neural node. The conductive core 106 of the memristive nanofiber 100 in some embodiments may comprise ΤΊΟ2 and/or any other suitable material with memristive properties. The memristive shell 109 may at least partially surround the conductive core 106 and thereby form a synapse between two or more neural nodes. In this regard, the memristive shell 109 may cause the memristive nanofiber 100 to form a connection that increases or decreases in strength in response to the past signals that have traveled through the memristive nanofiber 100. The memristive shell 109 may comprise Ti02 and/or any other suitable material, such as polyaniline.
[0014] With reference to FIG. 2, shown is an example of a nanofiber-based memristive neural network 200 according to various embodiments of the present disclosure. The memristive nanofibers 100 (FIG. 1 ) can be used as memristive connections 206A-206J between CMOS-based neuron arrays 203A-203E in the nanofiber-based memristive neural network 200. The memristive nanofibers 100 may form randomized memristive connections 206A-206J between inputs 212A- 212E and outputs 209A-209E of the CMOS-based neuron arrays 203A-203E.
[0015] With reference to FIG. 3, shown is a drawing of an example of a nanofiber-based memristive neural network 200 according to various embodiments of the present disclosure. The nanofiber-based memristive neural network 200 depicts examples of memristive nanofibers 100, referred to herein as memristive nanofibers 100A-100D, comprising conductive cores 106, referred to herein as the conductive cores 106A-106D, and memristive shells 109, referred to herein as the memristive shells 109A-109D. The memristive nanofibers 100A-100D may be used as memristive connections 206A-206J between CMOS neurons 327A and 327B located on the silicon substrate 330. Each memristive shell 109A-109D partially surrounds each conductive core 106A-106D and thereby forms a synapse 318A-318D between two or more neural nodes. The input electrodes 321 A and 321 B and output electrodes 324A and 324B may serve as conductive attachment points between memristive nanofibers 100A-100D and input terminals 212A-212E or output terminals 209A-209E of the neural nodes.
[0016] With reference to FIG. 4, shown is a simulation 403 of an example of a circuit 406 of a nanofiber-based memristive neural connection according to various embodiments of the present disclosure. The circuit 406 depicts a nanofiber-based memristive neural connection including memristive shells 109A-109D, voltage source V1 421 , voltage source V2 409, resistor R3 412, resistor R4 415, and resistor R1 418. The simulation 403 shows that driving current through a nanofiber- based memristive neural connection will not cause the effects from opposing memristors on a nanofiber to cancel each other out.
[0017] With reference to FIG. 5, shown is a flowchart illustrating one example of a method of creating a nanofiber-based memristive neural network 200 (FIG. 2) according to various embodiments of the present disclosure. Beginning with box 503, stoichiometric nanofibers are synthesized using a precursor. The stoichiometric nanofibers may comprise, for example, Ti02 and/or any other suitable material. The precursor may be for example, titanium isopropoxide, titanium butoxide, or another suitable precursor.
[0018] Next, in box 506, a core-shell memristive nanofiber 100 (FIG. 1 ) is created. The core-shell memristive nanofiber 100 (FIG. 1 ) may be created by electrospinning a stoichiometric Ti02 outer shell 109 with a doped conductive Ti02-x core 106. Then, in box 509, electrodes 103 may be deposited on the core-shell memristive nanofiber 100 (FIG. 1 ).
[0019] Next, in box 512, memristive properties are verified and a spike-timing dependent plasticity is implemented to create a computational model based on the nanofiber network response. Then, in box 515, a physical prototype of a memristive nanofiber neural network 200 using CMOS neurons 327A-327B is created. Thereafter, the process ends.
[0020] Disjunctive language used herein, such as the phrase "at least one of X, Y, or Z," unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language does not imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0021] The above-described embodiments of the present disclosure are merely examples of implementations set forth for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above- described embodiments without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure.

Claims

CLAIMS Therefore, the following is claimed:
1 . A memristive neural network, comprising:
a first neural node;
a second neural node; and
a memristive fiber that couples the first neural node to the second neural node, wherein the memristive fiber comprises a conductive core and a memristive shell, wherein the conductive core forms a communications path between the first neural node and the second neural node, wherein the memristive shell forms a memristor synapse between the first neural node and the second neural node.
2. The memristive neural network of claim 1 , wherein the first neural node and the second neural node are among a plurality of neural nodes in a neural node array.
3. The memristive neural network of claim 2, wherein each of the neural nodes comprises a respective Leaky Integrate-and-Fire (LIF) Complimentary Metal-Oxide-Semiconductor (CMOS) neural circuit.
4. The memristive neural network of claim 2, wherein the memristive fiber is among a plurality of memristive fibers in a memristive fiber network, wherein at least a subset of the plurality of memristive fibers in the memristive fiber network are randomly coupled to at least a subset of the plurality of neural nodes in the neural node array.
5. The memristive neural network of claim 4, wherein the memristive fiber network comprises at least one recurrent connection.
6. The memristive neural network of claim 4, wherein the memristive fiber network comprises at least one inhibitory output for at least one of the plurality of neural nodes.
7. The memristive neural network of claim 4, wherein the memristive fiber network comprises a plurality of memristive fiber layers.
8. The memristive neural network of claim 7, wherein the memristive fiber network comprises at least one connection that facilitates a transmission of at least one signal between multiple ones of the plurality of memristive fiber layers.
9. The memristive neural network of claim 1 , wherein the memristive fiber comprises:
a first electrode that couples to the first neural node; and a second electrode that couples to the second neural node.
10. The memristive neural network of claim 1 , wherein a Liquid State Machine (LSM) is modeled by at least the first neural node, the second neural node, and the memristive fiber.
1 1 . The memristive neural network of claim 1 , wherein the memristive fiber is electrospun to facilitate coupling between the first neural node and the second neural node.
12. The memristive neural network of claim 1 1 , wherein the memristive shell comprises Ti02 and the conductive core is doped with Ti02-x.
13. The memristive neural network of claim 1 , wherein the conductive core comprises Ti02-x.
14. The memristive neural network of claim 1 , wherein the first neural node outputs at least one signal in response to receiving at least one input signal.
15. The memristive neural network of claim 1 , wherein the memristive fiber is one of a plurality of memristive fibers, and wherein individual ones of the plurality of memristive fibers form randomized connections between the first neural node and the second neural node.
16. A method, comprising:
providing a first neural node;
providing a second neural node;
coupling the first neural node to the second neural node using at least a memristive fiber, wherein the memristive fiber comprises a conductive core and a memristive shell, wherein the conductive core forms a communications path between the first neural node and the second neural node, wherein the memristive shell forms a memristor synapse between the first neural node and the second neural node.
17. The method of claim 16, wherein the first neural node and the second neural node are among a plurality of neural nodes in a neural node array.
18. The method of claim 16, wherein the first neural node and the second neural node comprises a respective Leaky Integrate-and-Fire (LIF) Complimentary Metal-Oxide-Semiconductor (CMOS) neural circuit.
19. The method of claim 18, wherein the conductive core comprises
20. The method of claim 16, further comprising randomly coupling a plurality of memristive fibers to a plurality of neural nodes.
EP15810294.7A 2014-06-19 2015-06-05 Memristive nanofiber neural networks Ceased EP3158509A4 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201462014201P 2014-06-19 2014-06-19
PCT/US2015/034414 WO2015195365A1 (en) 2014-06-19 2015-06-05 Memristive nanofiber neural netwoks

Publications (2)

Publication Number Publication Date
EP3158509A1 true EP3158509A1 (en) 2017-04-26
EP3158509A4 EP3158509A4 (en) 2018-02-28

Family

ID=54935975

Family Applications (1)

Application Number Title Priority Date Filing Date
EP15810294.7A Ceased EP3158509A4 (en) 2014-06-19 2015-06-05 Memristive nanofiber neural networks

Country Status (6)

Country Link
EP (1) EP3158509A4 (en)
JP (1) JP6571692B2 (en)
KR (1) KR20170019414A (en)
AU (1) AU2015277645B2 (en)
BR (1) BR112016029682A2 (en)
WO (1) WO2015195365A1 (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10198691B2 (en) 2014-06-19 2019-02-05 University Of Florida Research Foundation, Inc. Memristive nanofiber neural networks
CN107533668B (en) 2016-03-11 2021-01-26 慧与发展有限责任合伙企业 Hardware accelerator and method for calculating node values of a neural network
EP3631800A4 (en) * 2017-05-22 2021-04-07 University of Florida Research Foundation DEEP LEARNING IN TWO PART MEMRISTIVE NETWORKS
WO2019195660A1 (en) 2018-04-05 2019-10-10 Rain Neuromorphics Inc. Systems and methods for efficient matrix multiplication
US11450712B2 (en) 2020-02-18 2022-09-20 Rain Neuromorphics Inc. Memristive device
CN120046673B (en) * 2025-04-23 2025-09-12 武汉工程大学 Memristor neural network circuit with partially reinforced operability condition reflection

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR0185757B1 (en) * 1994-02-14 1999-05-15 정호선 Learning method of choas circular neural net
JPH09185596A (en) * 1996-01-08 1997-07-15 Ricoh Co Ltd Coupling coefficient updating method in pulse density signal processing network
US7392230B2 (en) * 2002-03-12 2008-06-24 Knowmtech, Llc Physical neural network liquid state machine utilizing nanotechnology
US7359888B2 (en) * 2003-01-31 2008-04-15 Hewlett-Packard Development Company, L.P. Molecular-junction-nanowire-crossbar-based neural network
WO2008042900A2 (en) * 2006-10-02 2008-04-10 University Of Florida Research Foundation, Inc. Pulse-based feature extraction for neural recordings
EP2230633A1 (en) * 2009-03-17 2010-09-22 Commissariat à l'Énergie Atomique et aux Énergies Alternatives Neural network circuit comprising nanoscale synapses and CMOS neurons
US8050078B2 (en) * 2009-10-27 2011-11-01 Hewlett-Packard Development Company, L.P. Nanowire-based memristor devices
US8433665B2 (en) * 2010-07-07 2013-04-30 Qualcomm Incorporated Methods and systems for three-memristor synapse with STDP and dopamine signaling
KR20140071813A (en) * 2012-12-04 2014-06-12 삼성전자주식회사 Resistive Random Access Memory Device formed on Fiber and Manufacturing Method of the same
US9418331B2 (en) * 2013-10-28 2016-08-16 Qualcomm Incorporated Methods and apparatus for tagging classes using supervised learning

Also Published As

Publication number Publication date
JP2017527000A (en) 2017-09-14
AU2015277645B2 (en) 2021-01-28
JP6571692B2 (en) 2019-09-04
BR112016029682A2 (en) 2018-07-10
KR20170019414A (en) 2017-02-21
AU2015277645A1 (en) 2016-12-22
WO2015195365A1 (en) 2015-12-23
EP3158509A4 (en) 2018-02-28

Similar Documents

Publication Publication Date Title
US11941515B2 (en) Memristive nanofiber neural networks
AU2015277645B2 (en) Memristive nanofiber neural netwoks
CN103580668B (en) A kind of associative memory circuit based on memristor
CN103455843B (en) A kind of feedback-type artificial neural network training method and computing system
CN103282919B (en) The electronic synapse of intensified learning
Gambuzza et al. Memristor-based adaptive coupling for consensus and synchronization
US9342780B2 (en) Systems and methods for modeling binary synapses
CN105976022B (en) Circuit structure, artificial neural network and method for simulating synapses with circuit structure
US20140032464A1 (en) Multi-compartment neurons with neural cores
JP2021507349A (en) A method for storing weights in a crosspoint device of a resistance processing unit array, its crosspoint device, a crosspoint array for performing a neural network, its system, and a method for performing a neural network. Method
CN109034379B (en) Neuron and neuron circuit built by brain-like device memristor
Zins et al. Neuromorphic computing: A path to artificial intelligence through emulating human brains
KR102126791B1 (en) Neural networks using cross-point array and pattern readout method thereof
US10049321B2 (en) Anti-hebbian and hebbian computing with thermodynamic RAM
EP3631800A1 (en) Deep learning in bipartite memristive networks
CN208922326U (en) A kind of prominent haptic configuration of the neural network based on memristor array
JP2009026181A (en) neural network
Goldberg et al. Analog VLSI spiking neural network with address domain probabilistic synapses
Wei et al. A neuromorphic core based on threshold switching memristor with asynchronous address event representation circuits
CN115983357B (en) Bionic neural network chip
KR102671259B1 (en) Neuromorphic Device Having Inverting Circuits
Ochs et al. Neural network topology formation using memristive Jaumann structures
KR102130532B1 (en) Kernel Hardware Device
Yi et al. When a Neuron and its Self-Cascade System Coupled by a Memristor Synapse
CN214376576U (en) Neuron built by brain-like device memristor and neuron circuit

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

17P Request for examination filed

Effective date: 20170118

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
A4 Supplementary search report drawn up and despatched

Effective date: 20180126

RIC1 Information provided on ipc code assigned before grant

Ipc: G06N 3/063 20060101AFI20180122BHEP

Ipc: G06N 3/04 20060101ALI20180122BHEP

Ipc: G11C 11/56 20060101ALI20180122BHEP

17Q First examination report despatched

Effective date: 20190405

REG Reference to a national code

Ref country code: DE

Ref legal event code: R003

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION HAS BEEN REFUSED

18R Application refused

Effective date: 20201201