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The formation of multi-synaptic connections by the interaction of synaptic and structural plasticity and their functional consequences.

Fauth M, Wörgötter F, Tetzlaff C - PLoS Comput. Biol. (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Georg-August University Göttingen, Third Institute of Physics, Bernstein Center for Computational Neuroscience, Göttingen, Germany.

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.

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Model can account for experimental data for suitable pre- and postsynaptic activities.(A) The probability distribution of the number of synapses between two neurons from experiment ([36], red) is similar to the distribution resulting from the proposed model (blue) at vj = 0.656 and vi(S = 0) = 0.2975. (B) The activity confidence regions, where error of the experimental outcome lies within the most probable 95% (yellow) or 66% (green) of the trials, when randomly sampling from model distribution, spans over a broad range of activities. (C) Colour code shows the Monte-Carlo p-values for the hypothesis that experimental data comes from model distribution. For comparability, postsynaptic influence I was transformed to the postsynaptic activity for S = 0 in all figures. (Parameters: BCM rule with synaptic scaling with θ = 0.08, vtss = 0.1, κ = 9.0, structural plasticity: P = 12, ln pbuild = −16, a = 2.0, ρ = 0.125)
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pcbi.1004031.g004: Model can account for experimental data for suitable pre- and postsynaptic activities.(A) The probability distribution of the number of synapses between two neurons from experiment ([36], red) is similar to the distribution resulting from the proposed model (blue) at vj = 0.656 and vi(S = 0) = 0.2975. (B) The activity confidence regions, where error of the experimental outcome lies within the most probable 95% (yellow) or 66% (green) of the trials, when randomly sampling from model distribution, spans over a broad range of activities. (C) Colour code shows the Monte-Carlo p-values for the hypothesis that experimental data comes from model distribution. For comparability, postsynaptic influence I was transformed to the postsynaptic activity for S = 0 in all figures. (Parameters: BCM rule with synaptic scaling with θ = 0.08, vtss = 0.1, κ = 9.0, structural plasticity: P = 12, ln pbuild = −16, a = 2.0, ρ = 0.125)

Mentions: For this model, we calculate the long-term equilibrium distributions p[S] for a broad range of presynaptic activities vj and postsynaptic influences I (on a 446 x 357 grid). In Fig. 4A, one example equilibrium distribution for vj = 0.656 and vi[S = 0] = 0.2975 is compared with the experimental distribution.


The formation of multi-synaptic connections by the interaction of synaptic and structural plasticity and their functional consequences.

Fauth M, Wörgötter F, Tetzlaff C - PLoS Comput. Biol. (2015)

Model can account for experimental data for suitable pre- and postsynaptic activities.(A) The probability distribution of the number of synapses between two neurons from experiment ([36], red) is similar to the distribution resulting from the proposed model (blue) at vj = 0.656 and vi(S = 0) = 0.2975. (B) The activity confidence regions, where error of the experimental outcome lies within the most probable 95% (yellow) or 66% (green) of the trials, when randomly sampling from model distribution, spans over a broad range of activities. (C) Colour code shows the Monte-Carlo p-values for the hypothesis that experimental data comes from model distribution. For comparability, postsynaptic influence I was transformed to the postsynaptic activity for S = 0 in all figures. (Parameters: BCM rule with synaptic scaling with θ = 0.08, vtss = 0.1, κ = 9.0, structural plasticity: P = 12, ln pbuild = −16, a = 2.0, ρ = 0.125)
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4295841&req=5

pcbi.1004031.g004: Model can account for experimental data for suitable pre- and postsynaptic activities.(A) The probability distribution of the number of synapses between two neurons from experiment ([36], red) is similar to the distribution resulting from the proposed model (blue) at vj = 0.656 and vi(S = 0) = 0.2975. (B) The activity confidence regions, where error of the experimental outcome lies within the most probable 95% (yellow) or 66% (green) of the trials, when randomly sampling from model distribution, spans over a broad range of activities. (C) Colour code shows the Monte-Carlo p-values for the hypothesis that experimental data comes from model distribution. For comparability, postsynaptic influence I was transformed to the postsynaptic activity for S = 0 in all figures. (Parameters: BCM rule with synaptic scaling with θ = 0.08, vtss = 0.1, κ = 9.0, structural plasticity: P = 12, ln pbuild = −16, a = 2.0, ρ = 0.125)
Mentions: For this model, we calculate the long-term equilibrium distributions p[S] for a broad range of presynaptic activities vj and postsynaptic influences I (on a 446 x 357 grid). In Fig. 4A, one example equilibrium distribution for vj = 0.656 and vi[S = 0] = 0.2975 is compared with the experimental distribution.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Georg-August University Göttingen, Third Institute of Physics, Bernstein Center for Computational Neuroscience, Göttingen, Germany.

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.

Show MeSH
Related in: MedlinePlus