The calcitron model and calcium-based plasticity rules.
<p><b>(A)</b> Sources of Ca<sup>2+</sup> at the synapse. Local glutamate release from an activated presynaptic axon binds to an NMDA receptor in the postsynaptic dendritic spine, enabling local Ca<sup>2+</sup> influx. Depolarization of the neuron opens volta...
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2025
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| _version_ | 1852023178121969664 |
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| author | Toviah Moldwin (10866708) |
| author2 | Li Shay Azran (20636768) Idan Segev (32620) |
| author2_role | author author |
| author_facet | Toviah Moldwin (10866708) Li Shay Azran (20636768) Idan Segev (32620) |
| author_role | author |
| dc.creator.none.fl_str_mv | Toviah Moldwin (10866708) Li Shay Azran (20636768) Idan Segev (32620) |
| dc.date.none.fl_str_mv | 2025-01-29T18:38:59Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1012754.g001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_Calcitron_model_and_calcium-based_plasticity_rules_/28306016 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Cell Biology Neuroscience Physiology Science Policy Infectious Diseases Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified provide supervisory signals like neuron model derived rules relate calcium control hypothesis machine learning researchers relates synaptic plasticity implement homeostatic plasticity different rules might simple neuron model div >< p unsupervised learning tasks theoretical learning algorithms learning rules unsupervised recognition theoretical neuroscientists different contexts calcium source calcium influx shot learning plasticity thresholds plasticity protocols dependent plasticity wide range synapses ), synapse ), study shows study bridges postsynaptic spike mechanistically implemented local </ inspired one heterosynaptic </ four sources excitatory synapse effectively perform diverse array dependent </ dendritic spines biological mechanisms biological counterparts always clear |
| dc.title.none.fl_str_mv | The calcitron model and calcium-based plasticity rules. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p><b>(A)</b> Sources of Ca<sup>2+</sup> at the synapse. Local glutamate release from an activated presynaptic axon binds to an NMDA receptor in the postsynaptic dendritic spine, enabling local Ca<sup>2+</sup> influx. Depolarization of the neuron opens voltage-gated calcium channels (VGCCs), enabling calcium influx from global signals. (Glutamate also binds to AMPA receptors, enabling Na<sup>+</sup> influx, and depolarization also affects NMDAR conductance.) <b>(B)</b> Possible sources of Ca<sup>2+</sup> influx in a neuron. Ca<sup>2+</sup> can enter due to presynaptic input (), heterosynaptically-induced depolarization of VGCCs (), the backpropagating action potential () or a supervisory signal, such as a calcium plateau induced by input to the apical tuft () (Neuron image courtesy of Dean Geckt and MICrONs Consortium [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012754#pcbi.1012754.ref080" target="_blank">80</a>]). <b>(C)</b> The four Ca<sup>2+</sup> sources in a point neuron model. Each Ca<sup>2+</sup> source is associated with a respective coefficient ( determining how much Ca<sup>2+</sup> comes from each source. <b>(D)</b> The calcium control hypothesis. [Ca<sup>2+</sup>] below induces no change, [Ca<sup>2+</sup>] between and induces depression, and [Ca<sup>2+</sup>] above induces potentiation. <b>(E)</b> Weight change as a function of calcium in the linear version of Ca<sup>2+</sup>-based plasticity, as in (D), shown as phase plane. Magnitude of weight change is independent of current weight. Blue indicates depression, red indicates potentiation, white indicates no change. <b>(F)</b> Step stimulus to show the plastic effect of different levels of . is either raised to a depressive level (, blue line) or to a potentiative level (, red line) for several timesteps, then reduced to 0. S and E refer to the start and end of the calcium step. <b>(G)</b> Dynamics of the linear rule in response to the step stimulus from (F). Synaptic weights increase or decrease linearly in response to the potentiative or depressive levels of calcium (red and blue traces, respectively), then remain stable after calcium is turned off. <b>(H)</b> Fixed points (black) and learning rates (pink) in the asymptotic fixed point – learning rate (FPLR) version of the calcium control hypothesis. <b>(I)</b> Weight change as a function of [Ca<sup>2+</sup>] for different values of the present synaptic weight. Darker colors indicate higher weights. <b>(J)</b> Phase plane of weight changes for the FPLR rule. <b>(K)</b> Stimulus to demonstrate FPLR rule, identical to F. <b>(L)</b> Dynamics of the FPLR rule. Synaptic weights potentiate or depress asymptotically toward the potentiative (w<sub>max</sub>, 1) or depressive (w<sub>min</sub>, 0) fixed point.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_fec65cdc7cede602e6bb03b1db6b8828 |
| identifier_str_mv | 10.1371/journal.pcbi.1012754.g001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28306016 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The calcitron model and calcium-based plasticity rules.Toviah Moldwin (10866708)Li Shay Azran (20636768)Idan Segev (32620)BiophysicsCell BiologyNeurosciencePhysiologyScience PolicyInfectious DiseasesSpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedprovide supervisory signalslike neuron modelderived rules relatecalcium control hypothesismachine learning researchersrelates synaptic plasticityimplement homeostatic plasticitydifferent rules mightsimple neuron modeldiv >< punsupervised learning taskstheoretical learning algorithmslearning rulesunsupervised recognitiontheoretical neuroscientistsdifferent contextscalcium sourcecalcium influxshot learningplasticity thresholdsplasticity protocolsdependent plasticitywide rangesynapses ),synapse ),study showsstudy bridgespostsynaptic spikemechanistically implementedlocal </inspired oneheterosynaptic </four sourcesexcitatory synapseeffectively performdiverse arraydependent </dendritic spinesbiological mechanismsbiological counterpartsalways clear<p><b>(A)</b> Sources of Ca<sup>2+</sup> at the synapse. Local glutamate release from an activated presynaptic axon binds to an NMDA receptor in the postsynaptic dendritic spine, enabling local Ca<sup>2+</sup> influx. Depolarization of the neuron opens voltage-gated calcium channels (VGCCs), enabling calcium influx from global signals. (Glutamate also binds to AMPA receptors, enabling Na<sup>+</sup> influx, and depolarization also affects NMDAR conductance.) <b>(B)</b> Possible sources of Ca<sup>2+</sup> influx in a neuron. Ca<sup>2+</sup> can enter due to presynaptic input (), heterosynaptically-induced depolarization of VGCCs (), the backpropagating action potential () or a supervisory signal, such as a calcium plateau induced by input to the apical tuft () (Neuron image courtesy of Dean Geckt and MICrONs Consortium [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012754#pcbi.1012754.ref080" target="_blank">80</a>]). <b>(C)</b> The four Ca<sup>2+</sup> sources in a point neuron model. Each Ca<sup>2+</sup> source is associated with a respective coefficient ( determining how much Ca<sup>2+</sup> comes from each source. <b>(D)</b> The calcium control hypothesis. [Ca<sup>2+</sup>] below induces no change, [Ca<sup>2+</sup>] between and induces depression, and [Ca<sup>2+</sup>] above induces potentiation. <b>(E)</b> Weight change as a function of calcium in the linear version of Ca<sup>2+</sup>-based plasticity, as in (D), shown as phase plane. Magnitude of weight change is independent of current weight. Blue indicates depression, red indicates potentiation, white indicates no change. <b>(F)</b> Step stimulus to show the plastic effect of different levels of . is either raised to a depressive level (, blue line) or to a potentiative level (, red line) for several timesteps, then reduced to 0. S and E refer to the start and end of the calcium step. <b>(G)</b> Dynamics of the linear rule in response to the step stimulus from (F). Synaptic weights increase or decrease linearly in response to the potentiative or depressive levels of calcium (red and blue traces, respectively), then remain stable after calcium is turned off. <b>(H)</b> Fixed points (black) and learning rates (pink) in the asymptotic fixed point – learning rate (FPLR) version of the calcium control hypothesis. <b>(I)</b> Weight change as a function of [Ca<sup>2+</sup>] for different values of the present synaptic weight. Darker colors indicate higher weights. <b>(J)</b> Phase plane of weight changes for the FPLR rule. <b>(K)</b> Stimulus to demonstrate FPLR rule, identical to F. <b>(L)</b> Dynamics of the FPLR rule. Synaptic weights potentiate or depress asymptotically toward the potentiative (w<sub>max</sub>, 1) or depressive (w<sub>min</sub>, 0) fixed point.</p>2025-01-29T18:38:59ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1012754.g001https://figshare.com/articles/figure/The_Calcitron_model_and_calcium-based_plasticity_rules_/28306016CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/283060162025-01-29T18:38:59Z |
| spellingShingle | The calcitron model and calcium-based plasticity rules. Toviah Moldwin (10866708) Biophysics Cell Biology Neuroscience Physiology Science Policy Infectious Diseases Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified provide supervisory signals like neuron model derived rules relate calcium control hypothesis machine learning researchers relates synaptic plasticity implement homeostatic plasticity different rules might simple neuron model div >< p unsupervised learning tasks theoretical learning algorithms learning rules unsupervised recognition theoretical neuroscientists different contexts calcium source calcium influx shot learning plasticity thresholds plasticity protocols dependent plasticity wide range synapses ), synapse ), study shows study bridges postsynaptic spike mechanistically implemented local </ inspired one heterosynaptic </ four sources excitatory synapse effectively perform diverse array dependent </ dendritic spines biological mechanisms biological counterparts always clear |
| status_str | publishedVersion |
| title | The calcitron model and calcium-based plasticity rules. |
| title_full | The calcitron model and calcium-based plasticity rules. |
| title_fullStr | The calcitron model and calcium-based plasticity rules. |
| title_full_unstemmed | The calcitron model and calcium-based plasticity rules. |
| title_short | The calcitron model and calcium-based plasticity rules. |
| title_sort | The calcitron model and calcium-based plasticity rules. |
| topic | Biophysics Cell Biology Neuroscience Physiology Science Policy Infectious Diseases Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified provide supervisory signals like neuron model derived rules relate calcium control hypothesis machine learning researchers relates synaptic plasticity implement homeostatic plasticity different rules might simple neuron model div >< p unsupervised learning tasks theoretical learning algorithms learning rules unsupervised recognition theoretical neuroscientists different contexts calcium source calcium influx shot learning plasticity thresholds plasticity protocols dependent plasticity wide range synapses ), synapse ), study shows study bridges postsynaptic spike mechanistically implemented local </ inspired one heterosynaptic </ four sources excitatory synapse effectively perform diverse array dependent </ dendritic spines biological mechanisms biological counterparts always clear |