Physical neural network liquid state machine utilizing nanotechnology


Ontology type: sgo:Patent     


Patent Info

DATE

2008-06-24T00:00

AUTHORS

Alex Nugent

ABSTRACT

A physical neural network is disclosed, which comprises a liquid state machine. The physical neural network is configured from molecular connections located within a dielectric solvent between pre-synaptic and post-synaptic electrodes thereof, such that the molecular connections are strengthened or weakened according to an application of an electric field or a frequency thereof to provide physical neural network connections thereof. A supervised learning mechanism is associated with the liquid state machine, whereby connections strengths of the molecular connections are determined by pre-synaptic and post-synaptic activity respectively associated with the pre-synaptic and post-synaptic electrodes, wherein the liquid state machine comprises a dynamic fading memory mechanism. More... »

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2746", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "name": "Alex Nugent", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/35023115", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000730567", 
          "https://doi.org/10.1038/35023115"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35023115", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000730567", 
          "https://doi.org/10.1038/35023115"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0957-4484/13/1/308", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006229098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp972026r", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016636426"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp972026r", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016636426"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.59.6896", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017715008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.59.6896", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017715008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00422-002-0351-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018196870", 
          "https://doi.org/10.1007/s00422-002-0351-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.123941", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018868771"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0169-4332(98)00506-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037841902"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature00854", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037943488", 
          "https://doi.org/10.1038/nature00854"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature00854", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037943488", 
          "https://doi.org/10.1038/nature00854"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.280.5367.1253", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043682209"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/0957-4484/13/1/301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045550569"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/scientificamerican1200-62", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046624726", 
          "https://doi.org/10.1038/scientificamerican1200-62"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1063821", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046634290"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1159/000049627", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047080662"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja0024439", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055717152"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja0024439", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055717152"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp0102365", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056045534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp0102365", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056045534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/nl034240z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056215583"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/nl034240z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056215583"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/nl0342500", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056215585"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/nl0342500", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056215585"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/nl034412s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056215625"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/nl034412s", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056215625"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.126811", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057690911"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.127078", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057691170"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.127079", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057691171"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1290272", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057692374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1373413", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057700218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1377627", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057700616"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1396632", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057702537"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.32.7621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060538980"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.32.7621", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060538980"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.37.302", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060545295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevb.37.302", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1060545295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/28.658723", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061143057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnano.2002.804744", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061711738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnano.2002.804744", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061711738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tnano.2002.804744", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061711738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1058782", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062444451"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1152/jn.2002.88.1.507", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1075078988"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2008-06-24T00:00", 
    "description": "

A physical neural network is disclosed, which comprises a liquid state machine. The physical neural network is configured from molecular connections located within a dielectric solvent between pre-synaptic and post-synaptic electrodes thereof, such that the molecular connections are strengthened or weakened according to an application of an electric field or a frequency thereof to provide physical neural network connections thereof. A supervised learning mechanism is associated with the liquid state machine, whereby connections strengths of the molecular connections are determined by pre-synaptic and post-synaptic activity respectively associated with the pre-synaptic and post-synaptic electrodes, wherein the liquid state machine comprises a dynamic fading memory mechanism.

", "id": "sg:patent.US-7392230-B2", "keywords": [ "Neural Network", "nanotechnology", "state machine", "molecular connection", "dielectric", "electrode", "electric field", "frequency", "network connection", "connection strength", "synaptic activity", "wherein", "memory mechanism" ], "name": "Physical neural network liquid state machine utilizing nanotechnology", "sameAs": [ "https://app.dimensions.ai/details/patent/US-7392230-B2" ], "sdDataset": "patents", "sdDatePublished": "2019-04-18T10:31", "sdLicense": "https://scigraph.springernature.com/explorer/license/", "sdPublisher": { "name": "Springer Nature - SN SciGraph project", "type": "Organization" }, "sdSource": "s3://com-uberresearch-data-patents-target-20190320-rc/data/sn-export/402f166718b70575fb5d4ffe01f064d1/0000100128-0000352499/json_export_03604.jsonl", "type": "Patent" } ]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/patent.US-7392230-B2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/patent.US-7392230-B2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/patent.US-7392230-B2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/patent.US-7392230-B2'


 

This table displays all metadata directly associated to this object as RDF triples.

130 TRIPLES      14 PREDICATES      57 URIs      21 LITERALS      2 BLANK NODES

Subject Predicate Object
1 sg:patent.US-7392230-B2 schema:about anzsrc-for:2746
2 schema:author N0d98e68514aa40fd8574fbea02b1897e
3 schema:citation sg:pub.10.1007/s00422-002-0351-0
4 sg:pub.10.1038/35023115
5 sg:pub.10.1038/nature00854
6 sg:pub.10.1038/scientificamerican1200-62
7 https://doi.org/10.1016/s0169-4332(98)00506-6
8 https://doi.org/10.1021/ja0024439
9 https://doi.org/10.1021/jp0102365
10 https://doi.org/10.1021/jp972026r
11 https://doi.org/10.1021/nl034240z
12 https://doi.org/10.1021/nl0342500
13 https://doi.org/10.1021/nl034412s
14 https://doi.org/10.1063/1.123941
15 https://doi.org/10.1063/1.126811
16 https://doi.org/10.1063/1.127078
17 https://doi.org/10.1063/1.127079
18 https://doi.org/10.1063/1.1290272
19 https://doi.org/10.1063/1.1373413
20 https://doi.org/10.1063/1.1377627
21 https://doi.org/10.1063/1.1396632
22 https://doi.org/10.1088/0957-4484/13/1/301
23 https://doi.org/10.1088/0957-4484/13/1/308
24 https://doi.org/10.1103/physrevb.32.7621
25 https://doi.org/10.1103/physrevb.37.302
26 https://doi.org/10.1103/physreve.59.6896
27 https://doi.org/10.1109/28.658723
28 https://doi.org/10.1109/tnano.2002.804744
29 https://doi.org/10.1126/science.1058782
30 https://doi.org/10.1126/science.1063821
31 https://doi.org/10.1126/science.280.5367.1253
32 https://doi.org/10.1152/jn.2002.88.1.507
33 https://doi.org/10.1159/000049627
34 schema:datePublished 2008-06-24T00:00
35 schema:description <p num="p-0001">A physical neural network is disclosed, which comprises a liquid state machine. The physical neural network is configured from molecular connections located within a dielectric solvent between pre-synaptic and post-synaptic electrodes thereof, such that the molecular connections are strengthened or weakened according to an application of an electric field or a frequency thereof to provide physical neural network connections thereof. A supervised learning mechanism is associated with the liquid state machine, whereby connections strengths of the molecular connections are determined by pre-synaptic and post-synaptic activity respectively associated with the pre-synaptic and post-synaptic electrodes, wherein the liquid state machine comprises a dynamic fading memory mechanism.</p>
36 schema:keywords Neural Network
37 connection strength
38 dielectric
39 electric field
40 electrode
41 frequency
42 memory mechanism
43 molecular connection
44 nanotechnology
45 network connection
46 state machine
47 synaptic activity
48 wherein
49 schema:name Physical neural network liquid state machine utilizing nanotechnology
50 schema:sameAs https://app.dimensions.ai/details/patent/US-7392230-B2
51 schema:sdDatePublished 2019-04-18T10:31
52 schema:sdLicense https://scigraph.springernature.com/explorer/license/
53 schema:sdPublisher N5310a6a6b5574e9790c0f346e029cc3d
54 sgo:license sg:explorer/license/
55 sgo:sdDataset patents
56 rdf:type sgo:Patent
57 N0d98e68514aa40fd8574fbea02b1897e rdf:first N294542daf08f4a0d90ee345a860f6cd4
58 rdf:rest rdf:nil
59 N294542daf08f4a0d90ee345a860f6cd4 schema:name Alex Nugent
60 rdf:type schema:Person
61 N5310a6a6b5574e9790c0f346e029cc3d schema:name Springer Nature - SN SciGraph project
62 rdf:type schema:Organization
63 anzsrc-for:2746 schema:inDefinedTermSet anzsrc-for:
64 rdf:type schema:DefinedTerm
65 sg:pub.10.1007/s00422-002-0351-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018196870
66 https://doi.org/10.1007/s00422-002-0351-0
67 rdf:type schema:CreativeWork
68 sg:pub.10.1038/35023115 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000730567
69 https://doi.org/10.1038/35023115
70 rdf:type schema:CreativeWork
71 sg:pub.10.1038/nature00854 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037943488
72 https://doi.org/10.1038/nature00854
73 rdf:type schema:CreativeWork
74 sg:pub.10.1038/scientificamerican1200-62 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046624726
75 https://doi.org/10.1038/scientificamerican1200-62
76 rdf:type schema:CreativeWork
77 https://doi.org/10.1016/s0169-4332(98)00506-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037841902
78 rdf:type schema:CreativeWork
79 https://doi.org/10.1021/ja0024439 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055717152
80 rdf:type schema:CreativeWork
81 https://doi.org/10.1021/jp0102365 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056045534
82 rdf:type schema:CreativeWork
83 https://doi.org/10.1021/jp972026r schema:sameAs https://app.dimensions.ai/details/publication/pub.1016636426
84 rdf:type schema:CreativeWork
85 https://doi.org/10.1021/nl034240z schema:sameAs https://app.dimensions.ai/details/publication/pub.1056215583
86 rdf:type schema:CreativeWork
87 https://doi.org/10.1021/nl0342500 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056215585
88 rdf:type schema:CreativeWork
89 https://doi.org/10.1021/nl034412s schema:sameAs https://app.dimensions.ai/details/publication/pub.1056215625
90 rdf:type schema:CreativeWork
91 https://doi.org/10.1063/1.123941 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018868771
92 rdf:type schema:CreativeWork
93 https://doi.org/10.1063/1.126811 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057690911
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1063/1.127078 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057691170
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1063/1.127079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057691171
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1063/1.1290272 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057692374
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1063/1.1373413 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057700218
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1063/1.1377627 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057700616
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1063/1.1396632 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057702537
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1088/0957-4484/13/1/301 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045550569
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1088/0957-4484/13/1/308 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006229098
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1103/physrevb.32.7621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060538980
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1103/physrevb.37.302 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060545295
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1103/physreve.59.6896 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017715008
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1109/28.658723 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061143057
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1109/tnano.2002.804744 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061711738
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1126/science.1058782 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062444451
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1126/science.1063821 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046634290
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1126/science.280.5367.1253 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043682209
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1152/jn.2002.88.1.507 schema:sameAs https://app.dimensions.ai/details/publication/pub.1075078988
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1159/000049627 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047080662
130 rdf:type schema:CreativeWork
 




Preview window. Press ESC to close (or click here)


...