Johannes Auth

Group(s): Neural Computation
Email:
johannes-maria.auth@phys.uni-goettingen.de
Phone: +49 551/ 39 10763
Room: E.01.104

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    Journal / Proceedings / Book
    Fauth, M. and Wörgötter, F. and Tetzlaff, C. (2015).
    The Formation of Multi-synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences. PLoS Comput Biol, e1004031, 11, 1. DOI: 10.1371/journal.pcbi.1004031.
    BibTeX:
    @article{fauthwoergoettertetzlaff2015,
      author = {Fauth, M. and Wörgötter, F. and Tetzlaff, C.},
      title = {The Formation of Multi-synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences},
      pages = {e1004031},
      journal = {PLoS Comput Biol},
      year = {2015},
      volume= {11},
      number = {1},
      month = {01},
      publisher = {Public Library of Science},
      url = http://dx.doi.org/10.1371%2Fjournal.pcbi.1004031},
      doi = 10.1371/journal.pcbi.1004031},
      abstract = titleAuthor Summary/title pThe connectivity between neurons is modified by different mechanisms. On a time scale of minutes to hours one finds synaptic plasticity, whereas mechanisms for structural changes at axons or dendrites may take days. One main factor determining structural changes is the weight of a connection, which, in turn, is adapted by synaptic plasticity. Both mechanisms, synaptic and structural plasticity, are influenced and determined by the activity pattern in the network. Hence, it is important to understand how activity and the different plasticity mechanisms influence each other. Especially how activity influences rewiring in adult networks is still an open question./p pWe present a model, which captures these complex interactions by abstracting structural plasticity with weight-dependent probabilities. This allows for calculating the distribution of the number of synapses between two neurons analytically. We report that biologically realistic connection patterns for different cortical layers generically arise with synaptic plasticity rules in which the synaptic weights grow with postsynaptic activity. The connectivity patterns also lead to different activity levels resembling those found in the different cortical layers. Interestingly such a system exhibits a hysteresis by which connections remain stable longer than expected, which may add to the stability of information storage in the network./p}}
    		
    Abstract: titleAuthor Summary/title pThe connectivity between neurons is modified by different mechanisms. On a time scale of minutes to hours one finds synaptic plasticity, whereas mechanisms for structural changes at axons or dendrites may take days. One main factor determining structural changes is the weight of a connection, which, in turn, is adapted by synaptic plasticity. Both mechanisms, synaptic and structural plasticity, are influenced and determined by the activity pattern in the network. Hence, it is important to understand how activity and the different plasticity mechanisms influence each other. Especially how activity influences rewiring in adult networks is still an open question./p pWe present a model, which captures these complex interactions by abstracting structural plasticity with weight-dependent probabilities. This allows for calculating the distribution of the number of synapses between two neurons analytically. We report that biologically realistic connection patterns for different cortical layers generically arise with synaptic plasticity rules in which the synaptic weights grow with postsynaptic activity. The connectivity patterns also lead to different activity levels resembling those found in the different cortical layers. Interestingly such a system exhibits a hysteresis by which connections remain stable longer than expected, which may add to the stability of information storage in the network./p
    Review:
    Fauth, M. and Wörgötter, F. and Tetzlaff, C. (2015).
    The Formation of Multi-synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences. PLoS Comput Biol, e1004031, 11, 1. DOI: 10.1371/journal.pcbi.1004031.
    BibTeX:
    @article{fauthwoergoettertetzlaff2015a,
      author = {Fauth, M. and Wörgötter, F. and Tetzlaff, C.},
      title = {The Formation of Multi-synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences},
      pages = {e1004031},
      journal = {PLoS Comput Biol},
      year = {2015},
      volume= {11},
      number = {1},
      institution = {Georg-August University Göttingen, Third Institute of Physics, Bernstein Center for Computational Neuroscience, Göttingen, Germany.},
      language = {english},
      month = {Jan},
      doi = 10.1371/journal.pcbi.1004031},
      abstract = Cortical connectivity emerges from the permanent interaction between neuronal activity and synaptic as well as structural plasticity. An important experimentally observed feature of this connectivity is the distribution of the number of synapses from one neuron to another, which has been measured in several cortical layers. All of these distributions are bimodal with one peak at zero and a second one at a small number (3-8) of synapses. In this study, using a probabilistic model of structural plasticity, which depends on the synaptic weights, we explore how these distributions can emerge and which functional consequences they have. We find that bimodal distributions arise generically from the interaction of structural plasticity with synaptic plasticity rules that fulfill the following biological realistic constraints: First, the synaptic weights have to grow with the postsynaptic activity. Second, this growth curve and/or the input-output relation of the postsynaptic neuron have to change sub-linearly (negative curvature). As most neurons show such input-output-relations, these constraints can be fulfilled by many biological reasonable systems. Given such a system, we show that the different activities, which can explain the layer-specific distributions, correspond to experimentally observed activities. Considering these activities as working point of the system and varying the pre- or postsynaptic stimulation reveals a hysteresis in the number of synapses. As a consequence of this, the connectivity between two neurons can be controlled by activity but is also safeguarded against overly fast changes. These results indicate that the complex dynamics between activity and plasticity will, already between a pair of neurons, induce a variety of possible stable synaptic distributions, which could support memory mechanisms.}}
    		
    Abstract: Cortical connectivity emerges from the permanent interaction between neuronal activity and synaptic as well as structural plasticity. An important experimentally observed feature of this connectivity is the distribution of the number of synapses from one neuron to another, which has been measured in several cortical layers. All of these distributions are bimodal with one peak at zero and a second one at a small number (3-8) of synapses. In this study, using a probabilistic model of structural plasticity, which depends on the synaptic weights, we explore how these distributions can emerge and which functional consequences they have. We find that bimodal distributions arise generically from the interaction of structural plasticity with synaptic plasticity rules that fulfill the following biological realistic constraints: First, the synaptic weights have to grow with the postsynaptic activity. Second, this growth curve and/or the input-output relation of the postsynaptic neuron have to change sub-linearly (negative curvature). As most neurons show such input-output-relations, these constraints can be fulfilled by many biological reasonable systems. Given such a system, we show that the different activities, which can explain the layer-specific distributions, correspond to experimentally observed activities. Considering these activities as working point of the system and varying the pre- or postsynaptic stimulation reveals a hysteresis in the number of synapses. As a consequence of this, the connectivity between two neurons can be controlled by activity but is also safeguarded against overly fast changes. These results indicate that the complex dynamics between activity and plasticity will, already between a pair of neurons, induce a variety of possible stable synaptic distributions, which could support memory mechanisms.
    Review:
    Fauth, M. and Wörgötter, F. and Tetzlaff, C. (2015).
    Formation and Maintenance of Robust Long-Term Information Storage in the Presence of Synaptic Turnover. PLoS Comput Biol, e1004684, 11, 12. DOI: 10.1371/journal.pcbi.1004684.
    BibTeX:
    @article{fauthwoergoettertetzlaff2015b,
      author = {Fauth, M. and Wörgötter, F. and Tetzlaff, C.},
      title = {Formation and Maintenance of Robust Long-Term Information Storage in the Presence of Synaptic Turnover},
      pages = {e1004684},
      journal = {PLoS Comput Biol},
      year = {2015},
      volume= {11},
      number = {12},
      institution = {Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel.},
      language = {eng},
      month = {Dec},
      doi = 10.1371/journal.pcbi.1004684},
      abstract = A long-standing problem is how memories can be stored for very long times despite the volatility of the underlying neural substrate, most notably the high turnover of dendritic spines and synapses. To address this problem, here we are using a generic and simple probabilistic model for the creation and removal of synapses. We show that information can be stored for several months when utilizing the intrinsic dynamics of multi- synapse connections. In such systems, single synapses can still show high turnover, which enables fast learning of new information, but this will not perturb prior stored information (slow forgetting), which is represented by the compound state of the connections. The model matches the time course of recent experimental spine data during learning and memory in mice supporting the assumption of multi-synapse connections as the basis for long-term storage.}}
    		
    Abstract: A long-standing problem is how memories can be stored for very long times despite the volatility of the underlying neural substrate, most notably the high turnover of dendritic spines and synapses. To address this problem, here we are using a generic and simple probabilistic model for the creation and removal of synapses. We show that information can be stored for several months when utilizing the intrinsic dynamics of multi- synapse connections. In such systems, single synapses can still show high turnover, which enables fast learning of new information, but this will not perturb prior stored information (slow forgetting), which is represented by the compound state of the connections. The model matches the time course of recent experimental spine data during learning and memory in mice supporting the assumption of multi-synapse connections as the basis for long-term storage.
    Review:

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