AU2015277645B2 - Memristive nanofiber neural netwoks - Google Patents
Memristive nanofiber neural netwoks Download PDFInfo
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- AU2015277645B2 AU2015277645B2 AU2015277645A AU2015277645A AU2015277645B2 AU 2015277645 B2 AU2015277645 B2 AU 2015277645B2 AU 2015277645 A AU2015277645 A AU 2015277645A AU 2015277645 A AU2015277645 A AU 2015277645A AU 2015277645 B2 AU2015277645 B2 AU 2015277645B2
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- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
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- 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
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- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
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- 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
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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
[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
"Memristive Neural Networks," which is incorporated by reference herein in its
entirety.
[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.
[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.
[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 11) (Nov. 11, 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 TiO 2 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 TiO 2 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 TiO 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. 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 TiO2 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 10OA-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 321A and
321B and output electrodes 324A and 324B may serve as conductive attachment
points between memristive nanofibers 10OA-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, TiO2 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 TiO2 outer shell 109 with a doped conductive TiO 2 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 (20)
1. A memristive neural network, comprising:
a first neural node;
a second neuralnode; 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.
11. 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 11, wherein the memristive
shell comprises TiO2 and the conductive core is doped with TiO 2-.
13. The memristive neural network of claim 1, wherein the conductive
core comprises TiO 2-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 neuralnode.
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 neuralnode.
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
TiO2 .
20. The method of claim 16, further comprising randomly coupling a
plurality of memristive fibers to a plurality of neural nodes.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201462014201P | 2014-06-19 | 2014-06-19 | |
| US62/014,201 | 2014-06-19 | ||
| PCT/US2015/034414 WO2015195365A1 (en) | 2014-06-19 | 2015-06-05 | Memristive nanofiber neural netwoks |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| AU2015277645A1 AU2015277645A1 (en) | 2016-12-22 |
| AU2015277645B2 true AU2015277645B2 (en) | 2021-01-28 |
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| Application Number | Title | Priority Date | Filing Date |
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| AU2015277645A Ceased AU2015277645B2 (en) | 2014-06-19 | 2015-06-05 | Memristive nanofiber neural netwoks |
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| 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)
| 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 |
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| US9418331B2 (en) * | 2013-10-28 | 2016-08-16 | Qualcomm Incorporated | Methods and apparatus for tagging classes using supervised learning |
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| 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 |
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2015
- 2015-06-05 BR BR112016029682A patent/BR112016029682A2/en not_active Application Discontinuation
- 2015-06-05 KR KR1020177000606A patent/KR20170019414A/en not_active Ceased
- 2015-06-05 JP JP2016573557A patent/JP6571692B2/en active Active
- 2015-06-05 EP EP15810294.7A patent/EP3158509A4/en not_active Ceased
- 2015-06-05 AU AU2015277645A patent/AU2015277645B2/en not_active Ceased
- 2015-06-05 WO PCT/US2015/034414 patent/WO2015195365A1/en not_active Ceased
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9418331B2 (en) * | 2013-10-28 | 2016-08-16 | Qualcomm Incorporated | Methods and apparatus for tagging classes using supervised learning |
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| MANAN SURI ET AL, "Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses", IEEE TRANSACTIONS ON ELECTRON DEVICES, US, (2013-06-04), vol. 60, no. 7, doi:10.1109/TED.2013.2263000, ISSN 0018-9383, pages 2402 - 2409 * |
Also Published As
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| JP2017527000A (en) | 2017-09-14 |
| EP3158509A1 (en) | 2017-04-26 |
| 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 |
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