Self-regulated Complexity in Neural Networks
Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that netwo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hulata, Eyal [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2005 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer 2005 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Natural computing - Kluwer Academic Publishers, 2002, 4(2005), 4 vom: Sept., Seite 363-386 |
|---|---|
| Übergeordnetes Werk: |
volume:4 ; year:2005 ; number:4 ; month:09 ; pages:363-386 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11047-005-3668-5 |
|---|
| Katalog-ID: |
OLC2072669111 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2072669111 | ||
| 003 | DE-627 | ||
| 005 | 20230503195353.0 | ||
| 007 | tu | ||
| 008 | 200820s2005 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11047-005-3668-5 |2 doi | |
| 035 | |a (DE-627)OLC2072669111 | ||
| 035 | |a (DE-He213)s11047-005-3668-5-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 570 |a 004 |q VZ |
| 084 | |a 12 |2 ssgn | ||
| 084 | |a 54.28$jNichtelektronische Datenverarbeitung |2 bkl | ||
| 084 | |a 54.72$jKünstliche Intelligenz |2 bkl | ||
| 100 | 1 | |a Hulata, Eyal |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Self-regulated Complexity in Neural Networks |
| 264 | 1 | |c 2005 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer 2005 | ||
| 520 | |a Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics. | ||
| 650 | 4 | |a dynamical systems | |
| 650 | 4 | |a neural network | |
| 650 | 4 | |a neuroinformatics | |
| 650 | 4 | |a self-regulation | |
| 650 | 4 | |a structural complexity | |
| 650 | 4 | |a temporal organization | |
| 650 | 4 | |a time–frequency analysis | |
| 650 | 4 | |a wavelet packet | |
| 700 | 1 | |a Volman, Vladislav |4 aut | |
| 700 | 1 | |a Ben-Jacob, Eshel |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Natural computing |d Kluwer Academic Publishers, 2002 |g 4(2005), 4 vom: Sept., Seite 363-386 |w (DE-627)36367487X |w (DE-600)2110258-2 |w (DE-576)9363674878 |x 1567-7818 |7 nnns |
| 773 | 1 | 8 | |g volume:4 |g year:2005 |g number:4 |g month:09 |g pages:363-386 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11047-005-3668-5 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 936 | b | k | |a 54.28$jNichtelektronische Datenverarbeitung |q VZ |0 106418858 |0 (DE-625)106418858 |
| 936 | b | k | |a 54.72$jKünstliche Intelligenz |q VZ |0 10641240X |0 (DE-625)10641240X |
| 951 | |a AR | ||
| 952 | |d 4 |j 2005 |e 4 |c 09 |h 363-386 | ||
| author_variant |
e h eh v v vv e b j ebj |
|---|---|
| matchkey_str |
article:15677818:2005----::efeuaecmlxtin |
| hierarchy_sort_str |
2005 |
| bklnumber |
54.28$jNichtelektronische Datenverarbeitung 54.72$jKünstliche Intelligenz |
| publishDate |
2005 |
| allfields |
10.1007/s11047-005-3668-5 doi (DE-627)OLC2072669111 (DE-He213)s11047-005-3668-5-p DE-627 ger DE-627 rakwb eng 570 004 VZ 12 ssgn 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz bkl Hulata, Eyal verfasserin aut Self-regulated Complexity in Neural Networks 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2005 Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics. dynamical systems neural network neuroinformatics self-regulation structural complexity temporal organization time–frequency analysis wavelet packet Volman, Vladislav aut Ben-Jacob, Eshel aut Enthalten in Natural computing Kluwer Academic Publishers, 2002 4(2005), 4 vom: Sept., Seite 363-386 (DE-627)36367487X (DE-600)2110258-2 (DE-576)9363674878 1567-7818 nnns volume:4 year:2005 number:4 month:09 pages:363-386 https://doi.org/10.1007/s11047-005-3668-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.28$jNichtelektronische Datenverarbeitung VZ 106418858 (DE-625)106418858 54.72$jKünstliche Intelligenz VZ 10641240X (DE-625)10641240X AR 4 2005 4 09 363-386 |
| spelling |
10.1007/s11047-005-3668-5 doi (DE-627)OLC2072669111 (DE-He213)s11047-005-3668-5-p DE-627 ger DE-627 rakwb eng 570 004 VZ 12 ssgn 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz bkl Hulata, Eyal verfasserin aut Self-regulated Complexity in Neural Networks 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2005 Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics. dynamical systems neural network neuroinformatics self-regulation structural complexity temporal organization time–frequency analysis wavelet packet Volman, Vladislav aut Ben-Jacob, Eshel aut Enthalten in Natural computing Kluwer Academic Publishers, 2002 4(2005), 4 vom: Sept., Seite 363-386 (DE-627)36367487X (DE-600)2110258-2 (DE-576)9363674878 1567-7818 nnns volume:4 year:2005 number:4 month:09 pages:363-386 https://doi.org/10.1007/s11047-005-3668-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.28$jNichtelektronische Datenverarbeitung VZ 106418858 (DE-625)106418858 54.72$jKünstliche Intelligenz VZ 10641240X (DE-625)10641240X AR 4 2005 4 09 363-386 |
| allfields_unstemmed |
10.1007/s11047-005-3668-5 doi (DE-627)OLC2072669111 (DE-He213)s11047-005-3668-5-p DE-627 ger DE-627 rakwb eng 570 004 VZ 12 ssgn 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz bkl Hulata, Eyal verfasserin aut Self-regulated Complexity in Neural Networks 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2005 Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics. dynamical systems neural network neuroinformatics self-regulation structural complexity temporal organization time–frequency analysis wavelet packet Volman, Vladislav aut Ben-Jacob, Eshel aut Enthalten in Natural computing Kluwer Academic Publishers, 2002 4(2005), 4 vom: Sept., Seite 363-386 (DE-627)36367487X (DE-600)2110258-2 (DE-576)9363674878 1567-7818 nnns volume:4 year:2005 number:4 month:09 pages:363-386 https://doi.org/10.1007/s11047-005-3668-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.28$jNichtelektronische Datenverarbeitung VZ 106418858 (DE-625)106418858 54.72$jKünstliche Intelligenz VZ 10641240X (DE-625)10641240X AR 4 2005 4 09 363-386 |
| allfieldsGer |
10.1007/s11047-005-3668-5 doi (DE-627)OLC2072669111 (DE-He213)s11047-005-3668-5-p DE-627 ger DE-627 rakwb eng 570 004 VZ 12 ssgn 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz bkl Hulata, Eyal verfasserin aut Self-regulated Complexity in Neural Networks 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2005 Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics. dynamical systems neural network neuroinformatics self-regulation structural complexity temporal organization time–frequency analysis wavelet packet Volman, Vladislav aut Ben-Jacob, Eshel aut Enthalten in Natural computing Kluwer Academic Publishers, 2002 4(2005), 4 vom: Sept., Seite 363-386 (DE-627)36367487X (DE-600)2110258-2 (DE-576)9363674878 1567-7818 nnns volume:4 year:2005 number:4 month:09 pages:363-386 https://doi.org/10.1007/s11047-005-3668-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.28$jNichtelektronische Datenverarbeitung VZ 106418858 (DE-625)106418858 54.72$jKünstliche Intelligenz VZ 10641240X (DE-625)10641240X AR 4 2005 4 09 363-386 |
| allfieldsSound |
10.1007/s11047-005-3668-5 doi (DE-627)OLC2072669111 (DE-He213)s11047-005-3668-5-p DE-627 ger DE-627 rakwb eng 570 004 VZ 12 ssgn 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz bkl Hulata, Eyal verfasserin aut Self-regulated Complexity in Neural Networks 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 2005 Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics. dynamical systems neural network neuroinformatics self-regulation structural complexity temporal organization time–frequency analysis wavelet packet Volman, Vladislav aut Ben-Jacob, Eshel aut Enthalten in Natural computing Kluwer Academic Publishers, 2002 4(2005), 4 vom: Sept., Seite 363-386 (DE-627)36367487X (DE-600)2110258-2 (DE-576)9363674878 1567-7818 nnns volume:4 year:2005 number:4 month:09 pages:363-386 https://doi.org/10.1007/s11047-005-3668-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 54.28$jNichtelektronische Datenverarbeitung VZ 106418858 (DE-625)106418858 54.72$jKünstliche Intelligenz VZ 10641240X (DE-625)10641240X AR 4 2005 4 09 363-386 |
| language |
English |
| source |
Enthalten in Natural computing 4(2005), 4 vom: Sept., Seite 363-386 volume:4 year:2005 number:4 month:09 pages:363-386 |
| sourceStr |
Enthalten in Natural computing 4(2005), 4 vom: Sept., Seite 363-386 volume:4 year:2005 number:4 month:09 pages:363-386 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
dynamical systems neural network neuroinformatics self-regulation structural complexity temporal organization time–frequency analysis wavelet packet |
| dewey-raw |
570 |
| isfreeaccess_bool |
false |
| container_title |
Natural computing |
| authorswithroles_txt_mv |
Hulata, Eyal @@aut@@ Volman, Vladislav @@aut@@ Ben-Jacob, Eshel @@aut@@ |
| publishDateDaySort_date |
2005-09-01T00:00:00Z |
| hierarchy_top_id |
36367487X |
| dewey-sort |
3570 |
| id |
OLC2072669111 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2072669111</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503195353.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2005 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11047-005-3668-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2072669111</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11047-005-3668-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">12</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.28$jNichtelektronische Datenverarbeitung</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72$jKünstliche Intelligenz</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hulata, Eyal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Self-regulated Complexity in Neural Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2005</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer 2005</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">dynamical systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neuroinformatics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">self-regulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">structural complexity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">temporal organization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">time–frequency analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">wavelet packet</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Volman, Vladislav</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ben-Jacob, Eshel</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Natural computing</subfield><subfield code="d">Kluwer Academic Publishers, 2002</subfield><subfield code="g">4(2005), 4 vom: Sept., Seite 363-386</subfield><subfield code="w">(DE-627)36367487X</subfield><subfield code="w">(DE-600)2110258-2</subfield><subfield code="w">(DE-576)9363674878</subfield><subfield code="x">1567-7818</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2005</subfield><subfield code="g">number:4</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:363-386</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11047-005-3668-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.28$jNichtelektronische Datenverarbeitung</subfield><subfield code="q">VZ</subfield><subfield code="0">106418858</subfield><subfield code="0">(DE-625)106418858</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72$jKünstliche Intelligenz</subfield><subfield code="q">VZ</subfield><subfield code="0">10641240X</subfield><subfield code="0">(DE-625)10641240X</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2005</subfield><subfield code="e">4</subfield><subfield code="c">09</subfield><subfield code="h">363-386</subfield></datafield></record></collection>
|
| author |
Hulata, Eyal |
| spellingShingle |
Hulata, Eyal ddc 570 ssgn 12 bkl 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz misc dynamical systems misc neural network misc neuroinformatics misc self-regulation misc structural complexity misc temporal organization misc time–frequency analysis misc wavelet packet Self-regulated Complexity in Neural Networks |
| authorStr |
Hulata, Eyal |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)36367487X |
| format |
Article |
| dewey-ones |
570 - Life sciences; biology 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1567-7818 |
| topic_title |
570 004 VZ 12 ssgn 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz bkl Self-regulated Complexity in Neural Networks dynamical systems neural network neuroinformatics self-regulation structural complexity temporal organization time–frequency analysis wavelet packet |
| topic |
ddc 570 ssgn 12 bkl 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz misc dynamical systems misc neural network misc neuroinformatics misc self-regulation misc structural complexity misc temporal organization misc time–frequency analysis misc wavelet packet |
| topic_unstemmed |
ddc 570 ssgn 12 bkl 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz misc dynamical systems misc neural network misc neuroinformatics misc self-regulation misc structural complexity misc temporal organization misc time–frequency analysis misc wavelet packet |
| topic_browse |
ddc 570 ssgn 12 bkl 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz misc dynamical systems misc neural network misc neuroinformatics misc self-regulation misc structural complexity misc temporal organization misc time–frequency analysis misc wavelet packet |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Natural computing |
| hierarchy_parent_id |
36367487X |
| dewey-tens |
570 - Life sciences; biology 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Natural computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)36367487X (DE-600)2110258-2 (DE-576)9363674878 |
| title |
Self-regulated Complexity in Neural Networks |
| ctrlnum |
(DE-627)OLC2072669111 (DE-He213)s11047-005-3668-5-p |
| title_full |
Self-regulated Complexity in Neural Networks |
| author_sort |
Hulata, Eyal |
| journal |
Natural computing |
| journalStr |
Natural computing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science 000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2005 |
| contenttype_str_mv |
txt |
| container_start_page |
363 |
| author_browse |
Hulata, Eyal Volman, Vladislav Ben-Jacob, Eshel |
| container_volume |
4 |
| class |
570 004 VZ 12 ssgn 54.28$jNichtelektronische Datenverarbeitung bkl 54.72$jKünstliche Intelligenz bkl |
| format_se |
Aufsätze |
| author-letter |
Hulata, Eyal |
| doi_str_mv |
10.1007/s11047-005-3668-5 |
| normlink |
106418858 10641240X |
| normlink_prefix_str_mv |
106418858 (DE-625)106418858 10641240X (DE-625)10641240X |
| dewey-full |
570 004 |
| title_sort |
self-regulated complexity in neural networks |
| title_auth |
Self-regulated Complexity in Neural Networks |
| abstract |
Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics. © Springer 2005 |
| abstractGer |
Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics. © Springer 2005 |
| abstract_unstemmed |
Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics. © Springer 2005 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 |
| container_issue |
4 |
| title_short |
Self-regulated Complexity in Neural Networks |
| url |
https://doi.org/10.1007/s11047-005-3668-5 |
| remote_bool |
false |
| author2 |
Volman, Vladislav Ben-Jacob, Eshel |
| author2Str |
Volman, Vladislav Ben-Jacob, Eshel |
| ppnlink |
36367487X |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11047-005-3668-5 |
| up_date |
2024-07-03T15:46:20.044Z |
| _version_ |
1803573348320411648 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2072669111</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503195353.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2005 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11047-005-3668-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2072669111</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11047-005-3668-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">12</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.28$jNichtelektronische Datenverarbeitung</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72$jKünstliche Intelligenz</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Hulata, Eyal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Self-regulated Complexity in Neural Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2005</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer 2005</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recordings of spontaneous activity of in vitro neuronal networks reveal various phenomena on different time scales. These include synchronized firing of neurons, bursting events of firing on both cell and network levels, hierarchies of bursting events, etc. These findings suggest that networks’ natural dynamics are self-regulated to facilitate different processes on intervals in orders of magnitude ranging from fractions of seconds to hours. Observing these unique structures of recorded time-series give rise to questions regarding the diversity of the basic elements of the sequences, the information storage capacity of a network and the means of implementing calculations. Due to the complex temporal nature of the recordings, the proper methods of characterizing and quantifying these dynamics are on the time–frequency plane. We thus introduce time-series analysis of neuronal network’s synchronized bursting events applying the wavelet packet decomposition based on the Haar mother-wavelet. We utilize algorithms for optimal tiling of the time–frequency plane to signify the local and global variations within the sequence. New quantifying observables of regularity and complexity are identified based on both the homogeneity and diversity of the tiling (Hulata et al., 2004, Physical Review Letters 92: 198181–198104 ). These observables are demonstrated while exploring the regularity–complexity plane to fulfill the accepted criteria (yet lacking an operational definition) of Effective Complexity. The presented question regarding the sequences’ capacity of information is addressed through applying our observables on recorded sequences, scrambled sequences, artificial sequences produced with similar long-range statistical distributions and on outputs of neuronal models devised to simulate the unique networks’ dynamics.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">dynamical systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neuroinformatics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">self-regulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">structural complexity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">temporal organization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">time–frequency analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">wavelet packet</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Volman, Vladislav</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ben-Jacob, Eshel</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Natural computing</subfield><subfield code="d">Kluwer Academic Publishers, 2002</subfield><subfield code="g">4(2005), 4 vom: Sept., Seite 363-386</subfield><subfield code="w">(DE-627)36367487X</subfield><subfield code="w">(DE-600)2110258-2</subfield><subfield code="w">(DE-576)9363674878</subfield><subfield code="x">1567-7818</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2005</subfield><subfield code="g">number:4</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:363-386</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11047-005-3668-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.28$jNichtelektronische Datenverarbeitung</subfield><subfield code="q">VZ</subfield><subfield code="0">106418858</subfield><subfield code="0">(DE-625)106418858</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72$jKünstliche Intelligenz</subfield><subfield code="q">VZ</subfield><subfield code="0">10641240X</subfield><subfield code="0">(DE-625)10641240X</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">4</subfield><subfield code="j">2005</subfield><subfield code="e">4</subfield><subfield code="c">09</subfield><subfield code="h">363-386</subfield></datafield></record></collection>
|
| score |
7.399315 |