Nanoconnectomic upper bound on the variability of synaptic plasticity

Bartol, Thomas M.; Bromer, Cailey; Kinney, Justin P.; Chirillo, Micheal A; Bourne, Jennifer N.; Harris, Kristen M.; Sejnowski, Terrence J.

doi:10.7554/elife.10778

Cited by 241 publications

(312 citation statements)

References 54 publications

(84 reference statements)

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“…Such numbers should of course be interpreted with extreme caution, but it is at least consistent that all these analyses, including the one performed here, cohere and suggest that the number of states of synaptic strength available to a synapse may be of order 10. A very recent study, based on a full reconstruction of a small volume of hippocampal neuropil, corroborates this order by finding evidence for 26 distinguishable synaptic strengths (Bartol et al, 2015).…”

Section: Discussionmentioning

confidence: 77%

The Enhanced Rise and Delayed Fall of Memory in a Model of Synaptic Integration: Extension to Discrete State Synapses

Elliott

2016

Neural Computation

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Integrate-and-express models of synaptic plasticity propose that synapses may act as low-pass filters, integrating synaptic plasticity induction signals in order to discern trends before expressing synaptic plasticity. We have previously shown that synaptic filtering strongly controls destabilising fluctuations in developmental models. When applied to palimpsest memory systems that learn new memories by forgetting old ones, we have also shown that with binarystrength synapses, integrative synapses lead to an initial memory signal rise before its fall back to equilibrium. Such an initial rise is in dramatic contrast to non-integrative synapses, in which the memory signal falls monotonically. We now extend our earlier analysis of palimpsest memories with synaptic filters to consider the more general case of discrete state, multi-level synapses. We derive exact results for the memory signal dynamics and then consider various simplifying approximations. We show that multi-level synapses enhance the initial rise in the memory signal and then delay its subsequent fall by inducing a plateau-like region in the memory signal. Such dynamics significantly increase memory lifetimes, defined by a signal-to-noise ratio (SNR). We derive expressions for optimal choices of synaptic parameters (filter size, number of strength states, number of synapses) that maximise SNR memory lifetimes.However, we find that with memory lifetimes defined via mean-first-passage times, such optimality conditions do not exist, suggesting that optimality may be an artifact of SNRs.2

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Section: Discussionmentioning

confidence: 77%

The Enhanced Rise and Delayed Fall of Memory in a Model of Synaptic Integration: Extension to Discrete State Synapses

Elliott

2016

Neural Computation

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“…The three-dimensional reconstructions were made from these sections [13]. The tissue analysed was from the middle of the stratum radiatum of hippocampal CA1 from three adult male rats (55-65 days old).…”

Section: Resultsmentioning

confidence: 99%

“…An important recent study [13] provided the first estimate of the number of stable states that could be encoded by synapse size. This study took advantage of the fact that some axons make two synapses onto the same target cell, synapses that are likely to have similar activity history.…”

Section: Introductionmentioning

confidence: 99%

Gradation (approx. 10 size states) of synaptic strength by quantal addition of structural modules

Liu

Hagan

Lisman

2017

Phil. Trans. R. Soc. B

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Memory storage involves activity-dependent strengthening of synaptic transmission, a process termed long-term potentiation (LTP). The late phase of LTP is thought to encode long-term memory and involves structural processes that enlarge the synapse. Hence, understanding how synapse size is graded provides fundamental information about the information storage capability of synapses. Recent work using electron microscopy (EM) to quantify synapse dimensions has suggested that synapses may structurally encode as many as 26 functionally distinct states, which correspond to a series of proportionally spaced synapse sizes. Other recent evidence using superresolution microscopy has revealed that synapses are composed of stereotyped nanoclusters of a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors and scaffolding proteins; furthermore, synapse size varies linearly with the number of nanoclusters. Here we have sought to develop a model of synapse structure and growth that is consistent with both the EM and super-resolution data. We argue that synapses are composed of modules consisting of matrix material and potentially one nanocluster. LTP induction can add a trans-synaptic nanocluster to a module, thereby converting a silent module to an AMPA functional module. LTP can also add modules by a linear process, thereby producing an approximately 10-fold gradation in synapse size and strength.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.

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“…To put this in context, the capacity of the human brain has been estimated at 1 petabyte (10 15 )2 and thus to store this accumulation of data, 1 billion individuals would be required. Historically, the utilization of this data has been low but the future potential is well established 1.…”

Section: Introductionmentioning

confidence: 99%

Automated data mining of a plan‐check database and example application

Dunn¹,

Jolly²

2018

J Applied Clin Med Phys

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PurposeThe aim of this work was to present the development and example application of an automated data mining software platform that preforms bulk analysis of results and patient data passing through the 3D plan and delivery QA system, Mobius3D.MethodsPython, matlab, and Java were used to create an interface that reads JavaScript Object Notation (JSON) created for every approved Mobius3D pre‐treatment plan‐check. The aforementioned JSON files contain all the information for every pre‐treatment QA check performed by Mobius3D, including all 3D dose, CT, structure set information, as well as all plan information and patient demographics. Two Graphical User Interfaces (GUIs) were created, the first is called Mobius3D‐Database (M3D‐DB) and presents the check results in both filterable tabular and graphical form. These data are presented for all patients and includes mean dose differences, 90% coverage, 3D gamma pass rate percentages, treatment sites, machine, beam energy, Multi‐Leaf Collimator (MLC) mode, treatment planning system (TPS), plan names, approvers, dates and times. Group statistics and statistical process control levels are then calculated based on filter settings. The second GUI, called Mobius3D organ at risk (M3DOAR), analyzes dose‐volume histogram data for all patients and all Organs‐at‐Risk (OAR). The design of the software is such that all treatment parameters and treatment site information are able to be filtered and sorted with the results, plots, and statistics updated.ResultsThe M3D‐DB software can summarize and filter large numbers of plan‐checks from Mobius3D. The M3DOAR software is also able to analyze large amounts of dose‐volume data for patient groups which may prove useful in clinical trials, where OAR doses for large numbers of patients can be compared and correlated. Target DVHs can also be analyzed en mass.ConclusionsThis work demonstrates a method to extract the large amount of treatment data for every patient that is stored by Mobius3D but not easily accessible. With scripting, it is possible to mine this data for research and clinical trials as well as patient and TPS QA.

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Nanoconnectomic upper bound on the variability of synaptic plasticity

Cited by 241 publications

References 54 publications

The Enhanced Rise and Delayed Fall of Memory in a Model of Synaptic Integration: Extension to Discrete State Synapses

The Enhanced Rise and Delayed Fall of Memory in a Model of Synaptic Integration: Extension to Discrete State Synapses

Gradation (approx. 10 size states) of synaptic strength by quantal addition of structural modules

Automated data mining of a plan‐check database and example application

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