• Corpus ID: 51688050

Growth strategy determines network performance

  title={Growth strategy determines network performance},
  author={Ana P. Mill{\'a}n and Joaqu{\'i}n J. Torres and S. Johnson and Joaqu{\'i}n Marro},
  journal={arXiv: Physics and Society},
The interplay between structure and function is crucial in determining some emerging properties of many natural systems. Here we use an adaptive neural network model inspired in observations of synaptic pruning that couples activity and topological dynamics and reproduces experimental temporal profiles of synaptic density, including an initial transient period of relatively high synaptic connectivity. Using a simplified framework, we prove that the existence of this transient is critical in… 
2 Citations

Figures from this paper

Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases

By establishing a biologically reasonable neuronal network model, this work shows that despite a decline in the synaptic density, the connectivity, and efficiency of neuronal networks can be improved and finds that both the scale-free characteristic of neuron networks and the emergence of hub neurons rely on the spatial distance between neurons.

How Memory Conforms to Brain Development

A recently proposed brain developing model is considered to study how mechanisms responsible for the evolution of brain structure affect and are affected by memory storage processes, and it is reported that oscillations in the activity of the system among the memorized patterns can occur, depending on parameters, reminding mind dynamical processes.



Concurrence of form and function in developing networks and its role in synaptic pruning

A coupled model of network development and memory is studied, and it is found that due to the feedback networks with some initial memory capacity evolve into heterogeneous structures with high memory performance.

Evolving networks and the development of neural systems

It is shown how it is possible to analyse a very general scenario in which nodes can gain or lose edges according to any function of local and/or global degree information, and finds that simple biologically motivated assumptions lead to very good agreement with experimental data.

Dynamics and Effective Topology Underlying Synchronization in Networks of Cortical Neurons

The activity of early-to-fire neurons reliably forecasts an upcoming network spike and provides means for expedited propagation between assemblies, and theory predicts that scale-free topology allows for synchronization time that does not increase markedly with network size; this prediction is supported.

Influence of topology on the performance of a neural network

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

It is shown that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning plays a critical role in optimizing network structure.

Neuronal Regulation: A Mechanism for Synaptic Pruning During Brain Maturation

It is proved that neuronal regulation implements near-optimal synaptic modification and maintains the performance of a network undergoing massive synaptic pruning, supporting the possibility that neural regulation complements the action of Hebbian synaptic changes in the self-organization of the developing brain.

Complex Network Geometry and Frustrated Synchronization

This work reveals the rich interplay between network geometry and synchronization of coupled oscillators in the context of a simplicial complex model of manifolds called Complex Network Manifold and shows that the networks display frustrated synchronization for a wide range of the coupling strength of the oscillators.

Synaptic changes in the brain of subjects with schizophrenia

  • G. FaludiK. Mirnics
  • Psychology, Medicine
    International Journal of Developmental Neuroscience
  • 2011

The topology of large Open Connectome networks for the human brain

This work applies statistical model selection to characterize the degree distributions of graphs containing up to nodes and edges of the structural human connectome, finding that a three-parameter generalized Weibull distribution is a good fit to most of the observed degree distributions.