Digital filters for firing rate estimation

Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and de...
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Autor*in:	Paulin, Michael G. [verfasserIn]

Format:	Artikel
Sprache:	Englisch

Erschienen:	1992

Schlagwörter:	Estimate Algorithm Firing Rate Neural Activity Rate Estimate Spike Train

Anmerkung:	© Springer-Verlag 1992

Übergeordnetes Werk:	Enthalten in: Biological cybernetics - Springer-Verlag, 1975, 66(1992), 6 vom: Apr., Seite 525-531
Übergeordnetes Werk:	volume:66 ; year:1992 ; number:6 ; month:04 ; pages:525-531

Links:	Volltext

DOI / URN:	10.1007/BF00204117

Katalog-ID:	OLC2052692711

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520			\|a Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram.
650		4	\|a Estimate Algorithm
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allfields	10.1007/BF00204117 doi (DE-627)OLC2052692711 (DE-He213)BF00204117-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Paulin, Michael G. verfasserin aut Digital filters for firing rate estimation 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1992 Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram. Estimate Algorithm Firing Rate Neural Activity Rate Estimate Spike Train Enthalten in Biological cybernetics Springer-Verlag, 1975 66(1992), 6 vom: Apr., Seite 525-531 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:66 year:1992 number:6 month:04 pages:525-531 https://doi.org/10.1007/BF00204117 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 66 1992 6 04 525-531
spelling	10.1007/BF00204117 doi (DE-627)OLC2052692711 (DE-He213)BF00204117-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Paulin, Michael G. verfasserin aut Digital filters for firing rate estimation 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1992 Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram. Estimate Algorithm Firing Rate Neural Activity Rate Estimate Spike Train Enthalten in Biological cybernetics Springer-Verlag, 1975 66(1992), 6 vom: Apr., Seite 525-531 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:66 year:1992 number:6 month:04 pages:525-531 https://doi.org/10.1007/BF00204117 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 66 1992 6 04 525-531
allfields_unstemmed	10.1007/BF00204117 doi (DE-627)OLC2052692711 (DE-He213)BF00204117-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Paulin, Michael G. verfasserin aut Digital filters for firing rate estimation 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1992 Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram. Estimate Algorithm Firing Rate Neural Activity Rate Estimate Spike Train Enthalten in Biological cybernetics Springer-Verlag, 1975 66(1992), 6 vom: Apr., Seite 525-531 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:66 year:1992 number:6 month:04 pages:525-531 https://doi.org/10.1007/BF00204117 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 66 1992 6 04 525-531
allfieldsGer	10.1007/BF00204117 doi (DE-627)OLC2052692711 (DE-He213)BF00204117-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Paulin, Michael G. verfasserin aut Digital filters for firing rate estimation 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1992 Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram. Estimate Algorithm Firing Rate Neural Activity Rate Estimate Spike Train Enthalten in Biological cybernetics Springer-Verlag, 1975 66(1992), 6 vom: Apr., Seite 525-531 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:66 year:1992 number:6 month:04 pages:525-531 https://doi.org/10.1007/BF00204117 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 66 1992 6 04 525-531
allfieldsSound	10.1007/BF00204117 doi (DE-627)OLC2052692711 (DE-He213)BF00204117-p DE-627 ger DE-627 rakwb eng 570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Paulin, Michael G. verfasserin aut Digital filters for firing rate estimation 1992 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1992 Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram. Estimate Algorithm Firing Rate Neural Activity Rate Estimate Spike Train Enthalten in Biological cybernetics Springer-Verlag, 1975 66(1992), 6 vom: Apr., Seite 525-531 (DE-627)129556351 (DE-600)220699-7 (DE-576)015013545 0340-1200 nnns volume:66 year:1992 number:6 month:04 pages:525-531 https://doi.org/10.1007/BF00204117 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC FID-BIODIV SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_40 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_101 GBV_ILN_105 GBV_ILN_259 GBV_ILN_267 GBV_ILN_2002 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2057 GBV_ILN_2088 GBV_ILN_2237 GBV_ILN_2409 GBV_ILN_2410 GBV_ILN_4012 GBV_ILN_4028 GBV_ILN_4082 GBV_ILN_4103 GBV_ILN_4193 GBV_ILN_4219 GBV_ILN_4302 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4310 GBV_ILN_4318 GBV_ILN_4324 GBV_ILN_4700 AR 66 1992 6 04 525-531
language	English
source	Enthalten in Biological cybernetics 66(1992), 6 vom: Apr., Seite 525-531 volume:66 year:1992 number:6 month:04 pages:525-531
sourceStr	Enthalten in Biological cybernetics 66(1992), 6 vom: Apr., Seite 525-531 volume:66 year:1992 number:6 month:04 pages:525-531
format_phy_str_mv	Article
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topic_facet	Estimate Algorithm Firing Rate Neural Activity Rate Estimate Spike Train
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container_title	Biological cybernetics
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author	Paulin, Michael G.
spellingShingle	Paulin, Michael G. ddc 570 ssgn 12 fid BIODIV misc Estimate Algorithm misc Firing Rate misc Neural Activity misc Rate Estimate misc Spike Train Digital filters for firing rate estimation
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topic_title	570 VZ 570 000 VZ 12 ssgn BIODIV DE-30 fid Digital filters for firing rate estimation Estimate Algorithm Firing Rate Neural Activity Rate Estimate Spike Train
topic	ddc 570 ssgn 12 fid BIODIV misc Estimate Algorithm misc Firing Rate misc Neural Activity misc Rate Estimate misc Spike Train
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title	Digital filters for firing rate estimation
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title_sort	digital filters for firing rate estimation
title_auth	Digital filters for firing rate estimation
abstract	Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram. © Springer-Verlag 1992
abstractGer	Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram. © Springer-Verlag 1992
abstract_unstemmed	Abstract When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram. © Springer-Verlag 1992
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title_short	Digital filters for firing rate estimation
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