Peer-to-peer evolutionary computation: a study of viability

Incluye Prefacio y Conclusiones escritos en español e inglésTesis Univ. Granada. Departamento de Arquitectura y Tecnología de Computadores. Leída el 27 de mayo de 201

Applications of Evolutionary Computation. 24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings

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A Simple Model of Non-Spiking Neurons

Due to the ubiquity of spiking neurons in neuronal processes, various simple spiking neuron models have been proposed as an alternative to conductance-based models (a.k.a. Hodgkin-Huxley type models), known to be computationally expensive and difficult to treat mathematically. However, to the best of our knowledge, there is no equivalent in the literature of a simple and lightweight model for describing the voltage behavior of non-spiking neurons, which also are ubiquitous in a large variety of nervous tissues in both vertebrate and invertebrate species, and play a central role in information processing. This paper proposes a simple model that reproduces the experimental qualitative behavior of known types of non-spiking neurons. The proposed model, which differs fundamentally from classic simple spiking models unable to characterize non-spiking dynamics due to their intrinsic structure, is derived from the bifurcation study of conductance-based models of non-spiking neurons. Since such neurons display a high sensitivity to noise, the model aims at capturing the experimental distribution of single neuron responses rather than perfectly replicating a single given experimental voltage trace. We show that such a model: (i) can be used as a building block for realistic simulations of large non-spiking neuronal networks, and (ii) is endowed with generalization capabilities, granted by design

Genealogical patterns in evolutionary algorithms

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Genealogical patterns in evolutionary algorithms

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Systematic generation of biophysically detailed models with generalization capability for non-spiking neurons

Unlike spiking neurons which compress continuous inputs into digital signals for transmitting information via action potentials, non-spiking neurons modulate analog signals through graded potential responses. Such neurons have been found in a large variety of nervous tissues in both vertebrates and invertebrates species, and proved to play a central role in neuronal information processing. If general and vast efforts have been made for many years to model spiking neurons using conductance-based models (CBMs), very few methods have been developed for non-spiking neurons. When a CBM is built to characterize the neuron behavior, it should be endowed with generalization capabilities (i.e. the ability to predict acceptable neuronal responses to different novel stimuli not used during the model's building). Yet, since CBMs contain a large number of parameters, it may typically suffer from a lack of such a capability. In this paper, we propose a new systematic approach based on multi-objective optimization which builds general non-spiking models with generalization capabilities. The proposed approach only requires macroscopic experimental data from which all the model parameters are simultaneously determined without compromise. Such an approach is applied on three non-spiking neurons of the nematode Caenorhabditis elegans (C. elegans), a well-known model organism in neuroscience that predominantly transmits information through non-spiking signals. These three neurons, named RIM, AIY and AFD, represent, to date, the three possible forms of non-spiking neuronal responses of C. elegans

Load Balancing at the Edge of Chaos: How Self-Organized Criticality Can Lead to Energy-Efficient Computing

International audienceThis paper investigates a self-organized critical approach for dynamically load-balancing computational workloads. The proposed model is based on the Bak-Tang-Wiesenfeld sandpile: a cellular automaton that works in a critical regime at the edge of chaos. In analogy to grains of sand, tasks arrive and pile up on the different processing elements or sites of the system. When a pile exceeds a certain threshold, it collapses and initiates an avalanche of migrating tasks, i.e. producing load-balancing. We show that the frequency of such avalanches is in power-law relation with their sizes, a scale-invariant fingerprint of self-organized criticality that emerges without any tuning of parameters. Such an emergent pattern has organic properties such as the self-organization of tasks into resources or the self-optimization of the computing performance. The conducted experimentation also reveals that the system has a critical attractor in the point in which the arrival rate of tasks equals the processing power of the system. Taking advantage of this fact, we hypothesize that the processing elements can be turned on and off depending on the state of the workload as to maximize the utilization of resources. An interesting side effect is that the overall energy consumption of the system is minimized without compromising the quality of service