Slowness: An Objective for Spike-Timing–Dependent Plasticity?
Publication Date
June 29, 2007
Journal
PLOS Computational Biology
Authors
Henning Sprekeler, Christian Michaelis & Laurenz Wiskott
Volume
3
Issue
6
Pages
e112
DOI
http://doi.org/10.1371/journal.pcbi.0030112
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.0030112
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/17604445
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1904380
Europe PMC
http://europepmc.org/abstract/MED/17604445
Web of Science
000249105500021
Mendeley
http://www.mendeley.com/research/slowness-objective-spiketimingdependent-plasticity
Events
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CiteULike | Further Information

Mendeley | Further Information

{"title"=>"Slowness: An Objective for Spike-Timing–Dependent Plasticity?", "type"=>"journal", "authors"=>[{"first_name"=>"Henning", "last_name"=>"Sprekeler"}, {"first_name"=>"Christian", "last_name"=>"Michaelis"}, {"first_name"=>"Laurenz", "last_name"=>"Wiskott"}], "year"=>2007, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553-734X", "pui"=>"350364122", "sgr"=>"84872232047", "isbn"=>"10.1371/journal.pcbi.0030112", "scopus"=>"2-s2.0-84872232047", "pmid"=>"17604445", "doi"=>"10.1371/journal.pcbi.0030112"}, "id"=>"16d3dbef-9a34-3f6c-8670-3e14f3125bb3", "abstract"=>"Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this \"temporal stability\" or \"slowness\" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the \"trace rule.\" The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.", "link"=>"http://www.mendeley.com/research/slowness-objective-spiketimingdependent-plasticity", "reader_count"=>138, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>9, "Librarian"=>1, "Researcher"=>29, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>47, "Student > Postgraduate"=>5, "Student > Master"=>17, "Other"=>6, "Student > Bachelor"=>7, "Lecturer"=>2, "Professor"=>8}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>9, "Librarian"=>1, "Researcher"=>29, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>47, "Student > Postgraduate"=>5, "Student > Master"=>17, "Other"=>6, "Student > Bachelor"=>7, "Lecturer"=>2, "Professor"=>8}, "reader_count_by_subject_area"=>{"Engineering"=>13, "Unspecified"=>5, "Mathematics"=>4, "Agricultural and Biological Sciences"=>43, "Medicine and Dentistry"=>4, "Neuroscience"=>13, "Physics and Astronomy"=>5, "Psychology"=>11, "Social Sciences"=>1, "Computer Science"=>38, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>13}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Neuroscience"=>{"Neuroscience"=>13}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>5}, "Psychology"=>{"Psychology"=>11}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>43}, "Computer Science"=>{"Computer Science"=>38}, "Linguistics"=>{"Linguistics"=>1}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>5}}, "reader_count_by_country"=>{"United States"=>11, "Japan"=>3, "United Kingdom"=>1, "Malaysia"=>1, "Portugal"=>1, "Switzerland"=>3, "Greece"=>1, "Canada"=>2, "Netherlands"=>1, "Belgium"=>1, "Finland"=>1, "Italy"=>1, "France"=>3, "Germany"=>3}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/948955"], "description"=>"<p>Finding the direction of least variance in the time derivative of the input (which is part of the SFA algorithm) can be replaced by finding the direction of maximum variance in an appropriately low-pass filtered version of the input signal.</p>", "links"=>[], "tags"=>["directions"], "article_id"=>619283, "categories"=>["Neuroscience"], "users"=>["Henning Sprekeler", "Christian Michaelis", "Laurenz Wiskott"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.0030112.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Choosing_Slow_Directions_of_the_Input_/619283", "title"=>"Choosing Slow Directions of the Input", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 12:23:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/949022"], "description"=>"<p>Input and output signals are filtered (downward arrows). The weight change is the result of applying the Hebbian learning rule on the filtered signals (square box and upward arrow). Thereby, the variance of the filtered version of the output is maximized without actually filtering the output during processing.</p>", "links"=>[], "tags"=>["hebbian"], "article_id"=>619348, "categories"=>["Neuroscience"], "users"=>["Henning Sprekeler", "Christian Michaelis", "Laurenz Wiskott"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.0030112.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Filtered_Hebbian_Learning_Rule_/619348", "title"=>"Filtered Hebbian Learning Rule", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 12:23:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/949108"], "description"=>"<p>The power spectrum\n\t\t\t\t\t\t\t\t is the Fourier transform of the effective learning window <i>W</i><sub>0</sub>, which in turn is the convolution of the learning window <i>W</i> and the EPSP <i>ε</i>. The figure shows the learning windows required for SFA for three different EPSP durations (<i>τ</i> = 4, 40, 400 ms). The maximal input frequency <i>ν</i><sub>max</sub> was 1 / (40 ms) in all plots.\n\t\t\t\t\t\t\t</p>", "links"=>[], "tags"=>["epsp"], "article_id"=>619437, "categories"=>["Neuroscience"], "users"=>["Henning Sprekeler", "Christian Michaelis", "Laurenz Wiskott"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.0030112.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relation_between_the_EPSP_and_the_Learning_Window_/619437", "title"=>"Relation between the EPSP and the Learning Window", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 12:24:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/949184"], "description"=>"<p>The plot compares the theoretically predicted learning window with experimental data from hippocampal pyramidal cells as published by Bi and Poo [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030112#pcbi-0030112-b016\" target=\"_blank\">16</a>] (larger plot in the middle). Instead of the ideal power spectrum\n\t\t\t\t\t\t\t\t with the abrupt cutoff at <i>ν</i><sub>max</sub> as stated in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030112#pcbi-0030112-e020\" target=\"_blank\">Equation 20</a>, a Cauchy function with <i>γ</i> = 1 / 15 ms was used (top left, the dashed line is\n\t\t\t\t\t\t\t\t for <i>ν</i><sub>max</sub> = 1 / (40 ms)). Again, the EPSP decay time was <i>τ</i> = 40 ms. This learning window corresponds to an implementation of the “trace rule” [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030112#pcbi-0030112-b001\" target=\"_blank\">1</a>,<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030112#pcbi-0030112-b004\" target=\"_blank\">4</a>,<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030112#pcbi-0030112-b006\" target=\"_blank\">6</a>] for a decay time of the exponential filter of 15 ms.\n\t\t\t\t\t\t\t</p>", "links"=>[], "tags"=>["neuroscience/theoretical neuroscience", "neuroscience/sensory systems"], "article_id"=>619510, "categories"=>["Neuroscience"], "users"=>["Henning Sprekeler", "Christian Michaelis", "Laurenz Wiskott"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.0030112.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_Learning_Window_with_Experimental_Data_/619510", "title"=>"Comparison of the Learning Window with Experimental Data", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 12:24:56"}

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Relative Metric

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