Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect
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{"title"=>"Structural synaptic plasticity has high memory capacity and can explain graded amnesia, catastrophic forgetting, and the spacing effect", "type"=>"journal", "authors"=>[{"first_name"=>"Andreas", "last_name"=>"Knoblauch", "scopus_author_id"=>"35725809900"}, {"first_name"=>"Edgar", "last_name"=>"Körner", "scopus_author_id"=>"7004103297"}, {"first_name"=>"Ursula", "last_name"=>"Körner", "scopus_author_id"=>"15725402500"}, {"first_name"=>"Friedrich T.", "last_name"=>"Sommer", "scopus_author_id"=>"7102471008"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84901473156", "pui"=>"373177815", "doi"=>"10.1371/journal.pone.0096485", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"84901473156", "pmid"=>"24858841"}, "id"=>"5da85826-e13d-3e3d-9291-b02ac6cb9dbb", "abstract"=>"Advertisement. ... \\n", "link"=>"http://www.mendeley.com/research/structural-synaptic-plasticity-high-memory-capacity-explain-graded-amnesia-catastrophic-forgetting-s", "reader_count"=>44, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Librarian"=>1, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>4, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Librarian"=>1, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>4, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>20, "Medicine and Dentistry"=>1, "Neuroscience"=>8, "Philosophy"=>1, "Physics and Astronomy"=>1, "Chemistry"=>1, "Computer Science"=>8}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>8}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>20}, "Computer Science"=>{"Computer Science"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Belgium"=>1, "United States"=>1, "Switzerland"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1509822"], "description"=>"<p>Model parameters for the simulations shown in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone-0096485-g004\" target=\"_blank\">Figs. 4</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone-0096485-g006\" target=\"_blank\">6</a>. All simulations used , and for and a homeostatic setting maintaining a constant anatomical connectivity where the number of eliminated synapses per time step was equal to the number of generated synapses, i.e., , where is the potential connectivity and is the fraction of unconsolidated silent synapses at time . Synapse model: “single”  =  at most a single synapse per neuron pair; “multi”  =  multiple synapses per neuron pair are possible. is anatomical connectivity of the synaptic projection from population to . is population size of and . is cell assembly size (i.e., number of one-entries in memory patterns). and denote completeness and add noise in input patterns (i.e., has correct and false one-entries). # blocks is number of memory blocks that are subsequentially stored. /block is the number of memories per memory block. , , are the state transition probabilities of the (potential) synapses.</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "cell biology", "Cellular types", "Animal cells", "neurons", "Molecular cell biology", "Biochemistry", "Neurochemistry", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Cellular neuroscience", "Synaptic plasticity", "Cognitive neuroscience", "Developmental neuroscience", "Learning and memory", "Specimen preparation and treatment", "Mechanical treatment of specimens", "Specimen disruption", "electroporation", "Computerized simulations"], "article_id"=>1035753, "categories"=>["Biological Sciences"], "users"=>["Andreas Knoblauch", "Edgar Körner", "Ursula Körner", "Friedrich T. Sommer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096485.t001", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_parameters_/1035753", "title"=>"Simulation parameters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-05-23 04:01:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1509820"], "description"=>"<p><b>A</b>, Networks without structural plasticity suffer from catastrophic forgetting (top), but networks with structural plasticity do not (bottom). Plots show output noise over time simulating networks of size and activity storing 25 memory blocks one after the other (only the interesting part between storage of blocks 6 and 21 are visible). Each curve (with a distinct color) corresponds to for noisy test patterns of a particular memory block with correct and false active units. The steep descent of each curve corresponds to the time when the Hippocampus started to replay the corresponding memory block for 5 time steps. <b>B</b>, Networks employing structural plasticity show Ribot gradients after a cortical lesion (top) due to gradients in effectual connectivity (bottom). The lesion was simulated by deactivating half of the neurons in population at time . <b>C</b>, Networks employing structural plasticity reproduce the spacing effect of learning. In the first simulation (blue) novel memories were rehearsed once for 20 time steps (blue arrow at ). In a second simulation (red) the same total rehearsal time was “spaced” or distributed to four brief intervals of five steps each (red arrows at , , , and ). Here the network achieves a higher effectual connectivity (bottom) and less retrieval noise (top). See Sections 2, 3 and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone-0096485-t001\" target=\"_blank\">Table 1</a> for further details and simulation parameters.</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "cell biology", "Cellular types", "Animal cells", "neurons", "Molecular cell biology", "Biochemistry", "Neurochemistry", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Cellular neuroscience", "Synaptic plasticity", "Cognitive neuroscience", "Developmental neuroscience", "Learning and memory", "Specimen preparation and treatment", "Mechanical treatment of specimens", "Specimen disruption", "electroporation", "Computerized simulations", "catastrophic", "ribot", "spacing"], "article_id"=>1035751, "categories"=>["Biological Sciences"], "users"=>["Andreas Knoblauch", "Edgar Körner", "Ursula Körner", "Friedrich T. Sommer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096485.g006", "stats"=>{"downloads"=>1, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_of_catastrophic_forgetting_Ribot_gradients_and_the_spacing_effect_/1035751", "title"=>"Simulation of catastrophic forgetting, Ribot gradients, and the spacing effect.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-23 04:01:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1509821"], "description"=>"<p>During development anatomical connectivity (thick solid) quickly increases reaching a peak level (around 2–3y in humans), where the initial increase is followed by a short period of stable connectivity (until age 5y in humans), a phase of significant decrease of connectivity until puberty, and finally a phase of stable connectivity during adulthood <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone.0096485-Huttenlocher1\" target=\"_blank\">[14]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone.0096485-Elston1\" target=\"_blank\">[51]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone.0096485-Huttenlocher2\" target=\"_blank\">[77]</a>. Recent experiments suggest a temporary novelty-driven (thick arrows) increase of connectivity during adulthood <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone.0096485-Fu1\" target=\"_blank\">[23]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone.0096485-Xu1\" target=\"_blank\">[68]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone.0096485-Yu1\" target=\"_blank\">[116]</a>. Our model of structural plasticity predicts that learning is fastest for high levels of anatomical connectivity and structural plasticity. Thus, memories acquired during early phases can reach higher levels of effectual connectivity (,; thin solid lines) compared to memories acquired during later phases (,). The resulting gradients in effectual connectivity can explain various memory phenomena (see Section 7 for details).</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "cell biology", "Cellular types", "Animal cells", "neurons", "Molecular cell biology", "Biochemistry", "Neurochemistry", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Cellular neuroscience", "Synaptic plasticity", "Cognitive neuroscience", "Developmental neuroscience", "Learning and memory", "Specimen preparation and treatment", "Mechanical treatment of specimens", "Specimen disruption", "electroporation", "Computerized simulations", "connectivity", "reflecting", "lifelong"], "article_id"=>1035752, "categories"=>["Biological Sciences"], "users"=>["Andreas Knoblauch", "Edgar Körner", "Ursula Körner", "Friedrich T. Sommer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096485.g007", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sketch_of_network_connectivity_reflecting_lifelong_structural_plasticity_/1035752", "title"=>"Sketch of network connectivity reflecting lifelong structural plasticity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-23 04:01:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1509819"], "description"=>"<p><b>A,</b> Contour plot of pattern capacity (number of stored memories) as a function of assembly size (number of active units in a memory vector) and effectual network connectivity assuming output noise level and noise-free input patterns (, ). <b>B,</b> Weight capacity for the same setting as in panel <b>A</b>. <b>C,</b> Total storage capacity including structural plasticity for the same setting as in <b>A</b>. Note that even modest increases of can strongly increase storage capacity, in particular for sparse neural activity (small ) <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone.0096485-Waydo1\" target=\"_blank\">[82]</a>. All data computed from Gaussian approximation of dendritic potential distributions (see appendix II. 2).</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "cell biology", "Cellular types", "Animal cells", "neurons", "Molecular cell biology", "Biochemistry", "Neurochemistry", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Cellular neuroscience", "Synaptic plasticity", "Cognitive neuroscience", "Developmental neuroscience", "Learning and memory", "Specimen preparation and treatment", "Mechanical treatment of specimens", "Specimen disruption", "electroporation", "Computerized simulations", "capacities", "finite", "willshaw", "having", "cortical", "macrocolumn"], "article_id"=>1035750, "categories"=>["Biological Sciences"], "users"=>["Andreas Knoblauch", "Edgar Körner", "Ursula Körner", "Friedrich T. Sommer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096485.g005", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Storage_capacities_for_a_finite_Willshaw_network_having_the_size_of_a_cortical_macrocolumn_/1035750", "title"=>"Storage capacities for a finite Willshaw network having the size of a cortical macrocolumn ().", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-23 04:01:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1509816"], "description"=>"<p><b>A</b>, State/transition model of a single potential synapse (see text for details). <b>B</b>, In the following we consider potential synapses in a network , for example, connecting two cortical neuron populations and . Memories correspond to associations between activity patterns and . We will specifically analyze how well noisy activity patterns can reactivate the corresponding memories in order to estimate storage capacity. <b>C, D</b>: LTM storage (solid) by structural plasticity requires repetitive reactivation of activity patterns in cortical populations and to provide an appropriate consolidation signal to the synapses. This may happen by repeated bottom-up stimulation (<b>D</b>) or, for episodic memories, by top-down replay (<b>C</b>) from a HC-type STM buffer (dashed). LTM = long-term memory; STM = short-term memory; HC = hippocampus.</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "cell biology", "Cellular types", "Animal cells", "neurons", "Molecular cell biology", "Biochemistry", "Neurochemistry", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Cellular neuroscience", "Synaptic plasticity", "Cognitive neuroscience", "Developmental neuroscience", "Learning and memory", "Specimen preparation and treatment", "Mechanical treatment of specimens", "Specimen disruption", "electroporation", "Computerized simulations", "plasticity"], "article_id"=>1035747, "categories"=>["Biological Sciences"], "users"=>["Andreas Knoblauch", "Edgar Körner", "Ursula Körner", "Friedrich T. Sommer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096485.g002", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_of_structural_plasticity_and_consolidation_/1035747", "title"=>"Model of structural plasticity and consolidation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-23 04:01:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1509815"], "description"=>"<p>Illustration of different connectivity measures for a synaptic network connecting neuron populations to (which may be identical for recurrent networks). <b>A, </b><i>Anatomical connectivity </i> and <i>potential connectivity </i> are fractions of neuron pairs connected by an actual (black circles) and potential synapse (blue rectangles), respectively. <b>B,</b> The <i>consolidation signal </i> specifies the ensemble of neuron pairs that request a synapse (, red circles) to support storage of a given memory set. The corresponding <i>effectual connectivity </i> is then the fraction of neuron pairs requesting a synapse that are already connected by an actual synapse. The <i>consolidation load </i> is the fraction of neuron pairs that request a synapse.</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "cell biology", "Cellular types", "Animal cells", "neurons", "Molecular cell biology", "Biochemistry", "Neurochemistry", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Cellular neuroscience", "Synaptic plasticity", "Cognitive neuroscience", "Developmental neuroscience", "Learning and memory", "Specimen preparation and treatment", "Mechanical treatment of specimens", "Specimen disruption", "electroporation", "Computerized simulations"], "article_id"=>1035746, "categories"=>["Biological Sciences"], "users"=>["Andreas Knoblauch", "Edgar Körner", "Ursula Körner", "Friedrich T. Sommer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096485.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Definitions_of_network_connectivity_/1035746", "title"=>"Definitions of network connectivity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-23 04:01:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1509818"], "description"=>"<p>Each curve shows the evolution of effectual connectivity as a function of time for different parameters (anatomical connectivity), (potential connectivity), (consolidation load), and (fraction of initially consolidated synapses). Data are from single microscopic network simulations (solid black; cf. Eq. 4; network size ) and macroscopic theory (dashed gray; Eq. 11). See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096485#pone-0096485-t001\" target=\"_blank\">Table 1</a> for further simulation parameters. <b>A</b>: for different consolidation loads and constant , , . <b>B</b>: for different fractions of initially consolidated synapses and constant , , . <b>C</b>: for different anatomical connectivities and constant , , .</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "cell biology", "Cellular types", "Animal cells", "neurons", "Molecular cell biology", "Biochemistry", "Neurochemistry", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Cellular neuroscience", "Synaptic plasticity", "Cognitive neuroscience", "Developmental neuroscience", "Learning and memory", "Specimen preparation and treatment", "Mechanical treatment of specimens", "Specimen disruption", "electroporation", "Computerized simulations", "effectual", "connectivity", "consolidation"], "article_id"=>1035749, "categories"=>["Biological Sciences"], "users"=>["Andreas Knoblauch", "Edgar Körner", "Ursula Körner", "Friedrich T. Sommer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096485.g004", "stats"=>{"downloads"=>1, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Increase_of_effectual_connectivity_during_memory_consolidation_with_ongoing_structural_plasticity_/1035749", "title"=>"Increase of effectual connectivity during memory consolidation with ongoing structural plasticity.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-23 04:01:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/1509817"], "description"=>"<p><b>A</b>, Memory storage by Hebbian weight plasticity (Eq. 5) in a fully connected network (). Address patterns are associated to content patterns where (here ). Each memory is represented by a binary activity vector of length having active units (which define the corresponding cell assembly). <b>B</b>, One-step retrieval of the first memory from a noisy query pattern having two of the four active units in (). Here can perfectly reactivate the corresponding memory pattern in population () applying a firing threshold on dendritic potentials . <b>C</b>, As a simple form of structural plasticity, silent synapses can be pruned <i>after</i> learning. The resulting network has only 28 (instead of 49) synapses corresponding to a lower anatomical connectivity , whereas the effectual connectivity is still . Thus, pruning does not change network function, but increases stored information per synapse. <b>D</b>, Ongoing structural plasticity can similarly increase storage capacity during more realistic learning in networks with low anatomical connectivity (here ). During each time step , Hebbian weight plasticity potentiates and consolidates synapses with non-zero consolidation signal (which equals of panel A), whereas the remaining silent synapses are eliminated and replaced by new synapses at random locations. Note that the resulting network at is the same as in panel C.</p>", "links"=>[], "tags"=>["anatomy", "nervous system", "cell biology", "Cellular types", "Animal cells", "neurons", "Molecular cell biology", "Biochemistry", "Neurochemistry", "Computational biology", "computational neuroscience", "Circuit models", "neuroscience", "Cellular neuroscience", "Synaptic plasticity", "Cognitive neuroscience", "Developmental neuroscience", "Learning and memory", "Specimen preparation and treatment", "Mechanical treatment of specimens", "Specimen disruption", "electroporation", "Computerized simulations", "willshaw-type", "associative"], "article_id"=>1035748, "categories"=>["Biological Sciences"], "users"=>["Andreas Knoblauch", "Edgar Körner", "Ursula Körner", "Friedrich T. Sommer"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0096485.g003", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Learning_in_Willshaw_type_associative_networks_/1035748", "title"=>"Learning in Willshaw-type associative networks.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-05-23 04:01:40"}

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Relative Metric

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