Self-stabilization of neuronal networks

Abstract

This study is concerned with synaptic reorganization in local neuronal networks. Within networks of 30 neurons, an initial disequilibrium in connectivity has to be compensated by reorganization of synapses. Such plasticity is not a genetically determined process, but depends on results of neuronal interaction. Neurobiological experiments have lead to a model of the behavior of individual neurons during neuroplastic reorganization, formalized as a "synaptogenetic rule" that governs changes in the amount of synaptic elements on each neuron. When this synaptogenetic rule is applied to a system of neurons, there is some freedom left to the choice of further conditions. In this study it is examined, which assumptions additional to the synaptogenetic rule are essential in order to obtain morphogenetic stability. By explicating these assumptions, their plausibility can be tested. It is analysed, in which respect these conditions are important, in which part of the model they exert their influence, and what kind of instability and degeneration happens if the assumptions are violated. Our essentials for reaching morphogenetic stability are: (1) A network structure that guarantees the possibility of oscillations, (2) a compensation algorithm that guarantees a smooth morphogenesis, (3) kinetic parameters that guarantee convergence in the synaptic elements' change, and (4) a synaptic modification rule that prohibits Hebb-like as well as anti-Hebb-like synaptic changes. It is concluded that many structural features of the mammalian cerebral cortex are in accordance with the requirements of the model.

A model for cortical rewiring following deafferentation and focal stroke

“…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.…”

Neuronal Lysosome Accumulation in Degrading Synapses of Sensory-Motor and Limbic Subsystems in the Duck <i>Anas platyrhynchos</i><i>:</i> Indication of Rearrangements during Avian Brain Development?

“…However, certain factors can be suggested which might make degradation a developmental necessity. It may be of some selective advantage in critical periods of synaptogenesis to remove either transmitter molecules or even complete synaptic components in favour of a more specific set of synapses [9], thus managing functional integration of separately developing subsystems of neu roñal networks by a self-stabilization principle [44,45],…”

Enhanced Synaptic Connectivity in the Dentate Gyrus during Epileptiform Activity: Network Simulation

“…The neural network implemented for the simulation study is based on the McCulloch-Pitts formalism [15, 20], including the Compensation Algorithm for synaptogenesis, proposed by Dammasch et al [19, 21], and Hebbian and anti-Hebbian rules. The cell population represents the main types described in the dentate gyrus (DG) as well as its connectivity (Figure 1).…”

“…The morphogenetic state of neuron represents the neuronal capacity to form, stabilize, or degrade pre- or postsynaptic elements or synapses [20, 21]. The possible interactions between these three states follow the synaptogenesis compensation theory proposed by Wolff and Wagner [17].…”