Self-stabilization of neuronal networks

Dammasch, I. E.; Wagner, Günter P.; Wolff, Joachim R.

doi:10.1007/bf00318417

Cited by 37 publications

(25 citation statements)

References 29 publications

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“…Lipton and Kater [45] showed that the deviation of the calcium concentration of a neuron from a certain target value determines its outgrowth. In line with previous modelling studies [10], [23], [24], [72], we defined a homeostatic value for which the axonal and dendritic offers (and with them the synaptic density) remains unchanged if equals . The rules for growth and retraction of continuous axonal supply and dendritic acceptance were taken from the modelling approach by Dammasch and Butz [23], [73] and can be described as follows: If the neuron has a too high membrane potential , hence a too high calcium concentration , the dendritic acceptance shrinks proportionally to a constant leading to a decrease of its input.…”

Section: Methodssupporting

confidence: 91%

“…The last two parameters determine the connectivity which is a generalisation of synaptic weights and the number of synapses between neurons. In line with previous experimental [27], [45], [50] and modelling studies [23], [24], [71], [72], the processes which determine the dynamics of this system can be summarized very briefly as: The activity of each neuron affects its calcium concentration. This, in turn, specifies the change of the dendritic and axonal offers, hence, the connectivity which will then gradually influence activity and so on.…”

Section: Methodsmentioning

confidence: 65%

“…Specifically we are asking the following questions: 1) do the investigated cell cultures undergo a significant transition in their activity states and how is this related to self-organized criticality and 2) can specific predictions be made with respect to network activity and connectivity which would explain the observed behavior. To address the second aspect we are designing a model to simulate network development, which is based on activity-dependent axonal and dendritic growth leading to homeostasis in neuronal activity [23]–[28].…”

Section: Introductionmentioning

confidence: 99%

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Self-Organized Criticality in Developing Neuronal Networks

et al. 2010

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Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV) of cortical cell cultures (n = 20) and find four different phases, related to their morphological maturation: An initial low-activity state (≈19 DIV) is followed by a supercritical (≈20 DIV) and then a subcritical one (≈36 DIV) until the network finally reaches stable criticality (≈58 DIV). Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro.

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Section: Methodssupporting

confidence: 91%

Section: Methodsmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

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Self-Organized Criticality in Developing Neuronal Networks

et al. 2010

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“…An important driving force for network rewiring – as a main principle – is the need of every neuron to keep its firing rate within a functional, homeostatic range (Turrigiano, 1999; Wolff and Wagner, 1983; Wolff et al, 1989). The first models for a homeostatic structural network formation were independently proposed by Dammasch et al (1986, 1988) and van Ooyen (van Ooyen and van Pelt, 1994; van Ooyen et al, 1995). We joined the concepts of these two models to create a novel neural network model for activity-dependent structural plasticity.…”

Section: Introductionmentioning

confidence: 99%

A model for cortical rewiring following deafferentation and focal stroke

Butz

Ooyen

Wörgötter

2009

Front. Comput. Neurosci.

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It is still unclear to what extent structural plasticity in terms of synaptic rewiring is the cause for cortical remapping after a lesion. Recent two-photon laser imaging studies demonstrate that synaptic rewiring is persistent in the adult brain and is dramatically increased following brain lesions or after a loss of sensory input (cortical deafferentation). We use a recurrent neural network model to study the time course of synaptic rewiring following a peripheral lesion. For this, we represent axonal and dendritic elements of cortical neurons to model synapse formation, pruning and synaptic rewiring. Neurons increase and decrease the number of axonal and dendritic elements in an activity-dependent fashion in order to maintain their activity in a homeostatic equilibrium.In this study we demonstrate that synaptic rewiring contributes to neuronal homeostasis during normal development as well as following lesions. We show that networks in homeostasis, which can therefore be considered as adult networks, are much less able to compensate for a loss of input. Interestingly, we found that paused stimulation of the networks are much more effective promoting reorganization than continuous stimulation. This can be explained as neurons quickly adapt to this stimulation whereas pauses prevents a saturation of the positive stimulation effect. These fi ndings may suggest strategies for improving therapies in neurologic rehabilitation.

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“…There are important earlier models of homeostatic structural plasticity, such as the compensation model by Dammasch et al (Dammasch et al, 1986, 1988; Cromme and Dammasch, 1989; Butz and Teuchert-Noodt, 2006; Butz et al, 2006) and the activity-dependent neurite outgrowth model by van Ooyen (van Ooyen and van Pelt, 1994; Van Ooyen et al, 1995). The latter model, which studied the reciprocal interactions between neuronal activity and network formation, successfully accounted for experimental data on developing cell cultures (van Ooyen and van Pelt, 1994; van Oss and van Ooyen, 1997; Abbott and Rohrkemper, 2007; Tetzlaff et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Homeostatic structural plasticity increases the efficiency of small-world networks

Butz

Steenbuck

Ooyen³

2014

Front. Synaptic Neurosci.

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In networks with small-world topology, which are characterized by a high clustering coefficient and a short characteristic path length, information can be transmitted efficiently and at relatively low costs. The brain is composed of small-world networks, and evolution may have optimized brain connectivity for efficient information processing. Despite many studies on the impact of topology on information processing in neuronal networks, little is known about the development of network topology and the emergence of efficient small-world networks. We investigated how a simple growth process that favors short-range connections over long-range connections in combination with a synapse formation rule that generates homeostasis in post-synaptic firing rates shapes neuronal network topology. Interestingly, we found that small-world networks benefited from homeostasis by an increase in efficiency, defined as the averaged inverse of the shortest paths through the network. Efficiency particularly increased as small-world networks approached the desired level of electrical activity. Ultimately, homeostatic small-world networks became almost as efficient as random networks. The increase in efficiency was caused by the emergent property of the homeostatic growth process that neurons started forming more long-range connections, albeit at a low rate, when their electrical activity was close to the homeostatic set-point. Although global network topology continued to change when neuronal activities were around the homeostatic equilibrium, the small-world property of the network was maintained over the entire course of development. Our results may help understand how complex systems such as the brain could set up an efficient network topology in a self-organizing manner. Insights from our work may also lead to novel techniques for constructing large-scale neuronal networks by self-organization.

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Self-stabilization of neuronal networks

Cited by 37 publications

References 29 publications

Self-Organized Criticality in Developing Neuronal Networks

Self-Organized Criticality in Developing Neuronal Networks

A model for cortical rewiring following deafferentation and focal stroke

Homeostatic structural plasticity increases the efficiency of small-world networks

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