Rafael Lahoz-Beltra

An AM Radio Receiver Designed With a Genetic Algorithm Based on a Bacterial Conjugation Genetic Operator

Most methods in evolutionary computation are biologically inspired by chromosome crossover and mutation, two of the main sources of genetic variability in biological populations, as well as in genetic algorithms. In fact, this is a very important feature of the biological populations and their counterpart in genetic algorithms since the efficiency of the Darwinian natural selection depends on the degree of genetic variation that is achieved with the genetic mechanism or operator responsible for the population variability. Furthermore, in Nature, several other genetic mechanisms are used by populations as sources of variability such as bacterial conjugation, that is the transfer of genetic material between bacteria. In this paper, we introduce a biologically inspired conjugation operator simulating the genetic mechanism exhibited by bacterial colonies. The efficiency of the bacterial conjugation operator is illustrated designing with a genetic algorithm based on this operator an AM radio receiver, optimizing the main features of the electronic components of the AM radio circuit, as well as those of the radio enclosure.

Cytoskeletal involvement in neuronal learning: a review

This paper introduces the ideas of neural networks in the context of currently recognized cellular structures within neurons. Neural network models and paradigms require adaptation of synapses for learning to occur in the network. Some models of learning paradigms require information to move from axon to dendrite. This motivated us to examine the possibility of intracellular signaling to mediate such signals. The cytoskeleton forms a substrate for intracellular signaling via material transport and other putative mechanisms. Furthermore, many experimental results suggest a link between the cytoskeleton and cognitive processing. In this paper we review research on intracellular signaling in the context of neural network learning.

Molecular automata assembly: principles and simulation of bacterial membrane construction

The motivation to understand the basic rules and principles governing molecular self-assembly may be relevant to explain in the context of molecular biology the self-organization and biological functions exhibited within cells. This paper presents a molecular automata model to simulate molecular self-assembly introducing the concept of molecular programming to simulate the biological function or operation performed by an assembled molecular state machine. The method is illustrated modelling Escherichia coli membrane construction including the assembly and operation of ATP synthase as well as the assembly of the bacterial flagellar motor. Flagellar motor operation was simulated using a different approach based on state machine definition used in virtual reality systems. The proposed methodology provides a modelling framework for simulation of biological functions performed by cellular components and other biological systems suitable to be modelled as molecular state machines.

Cheating for problem solving: a genetic algorithm with social interactions

We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, i.e. animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoners dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm.

Validation of laughter for diagnosis and evaluation of depression.

BACKGROUND In the medical field, laughter has been studied for its beneficial effects on health and as a therapeutic method to prevent and treat major medical diseases. However, very few works, if any, have explored the predictive potential of laughter and its potential use as a diagnostic tool. METHOD We registered laughs of depressed patients (n=30) and healthy controls (n=20), in total 934 laughs (517 from patients and 417 from controls). All patients were tested by the Hamilton Depression Rating Scale (HDRS). The processing was made in Matlab, with calculation of 8 variables per laugh plosive. General and discriminant analysis distinguished patients, controls, gender, and the association between laughter and HDRS test. RESULTS Depressed patients and healthy controls differed significantly on the type of laughter, with 88% efficacy. According to the Hamilton scale, 85.47% of the samples were correctly classified in males, and 66.17% in women, suggesting a tight relationship between laughter and the depressed condition. LIMITATIONS (i) The compilation of humorous videos created to evoke laughter implied quite variable chances of laughter production. (ii) Some laughing subjects might not feel comfortable when recording. (iii) Evaluation of laughter episodes depended on personal inspection of the records. (iv) Sample size was relatively small and may not be representative of the general population afflicted by depression. CONCLUSIONS Laughter may be applied as a diagnostic tool in the onset and evolution of depression and, potentially, of neuropsychiatric pathologies. The sound structures of laughter reveal the underlying emotional and mood states in interpersonal relationships.

Intracellular mechanisms in neuronal learning: adaptive models

The cytoskeletal intraneuronal structure and some candidate mechanisms for signaling within nerve cells are described. Models were developing for the interaction of the cytoskeleton with cell membranes, synapses, and an internal signaling model that renders back-error propagation biologically plausible. Orientation-selective units observed in the primate motor cortex may be organized by such internal signaling mechanisms. The impact on sensorimotor systems and learning is discussed. It is concluded that the cytoskeletons anatomical presence suggested that it plays a potentially key role in neuronal learning. The cytoskeleton could participate in synaptic processes by supporting the synapse and possibly by sending intracellular signals as well. Paradigms for adaptational mechanisms and information processing can be modeled utilizing the cytoskeleton and cytoskeletal signals.<<ETX>>

Cognitive and Emotional Contents of Laughter: Framing a New Neurocomputational Approach

Laughter, one of the most intriguing reactions of individuals, is an important emotional component of intelligences adaptive processes. Laughter spontaneously appears as an instinctive “gut†reaction; but it is also a cognitive phenomenon (humour), it is social, it has positive-negative valence, and it may wrap itself onto other emotional contents. Laughter becomes one of the most interesting instances to discuss the common information processing that underlies emotions and intelligence. In this article a new core hypothesis on the neurodynamics of laughter and its behavioural repercussions is discussed. The “sentic forms†hypothesis developed by Manfred Clynes for sensory-motor tactile communication is generalized neurodynamically in order to understand the problem-solving characteristics of laughter and the unusual sound features that it presents in our species. Laughter, far from being a curious evolutionary relic or a trivial innate behaviour, should be considered as a highly efficient tool for cognitive-emotional-social problem solving. Explaining laughter becomes a first-class neurodynamic and neurocomputational challenge.

Evolutionary Daisyworld models: A new approach to studying complex adaptive systems

This paper presents a model of a population of error-prone self-replicative species (replicators) that interact with its environment. The population evolves by natural selection in an environment whose change is caused by the evolutionary process itself. For simplicity, the environment is described by a single scalar factor, i.e. its temperature. The formal formulation of the model extends two basic models of Ecology and Evolutionary Biology, namely, Daisyworld and Quasispecies models. It is also assumed that the environment can also change due to external perturbations that are summed up as an external noise. Unlike previous models, the population size self-regulates, so no ad hoc population constraints are involved. When species replication is error-free, i.e. without mutation, the system dynamics can be described by an (n + 1)-dimensional system of differential equations, one for each of the species initially present in the system, and another for the evolution of the environment temperature. Analytical results can be obtained straightforwardly in low-dimensional cases. In these examples, we show the stabilizing effect of thermal white noise on the system behavior. The error-prone self-replication, i.e. with mutation, is studied computationally. We assume that species can mutate two independent parameters: its optimal growth temperature and its influence on the environment temperature. For different mutation rates the system exhibits a large variety of behaviors. In particular, we show that a quasispecies distribution with an internal sub-distribution appears, facilitating species adaptation to new environments. Finally, this ecologically inspired evolutionary model is applied to study the origin and evolution of public opinion.

Biological learning with cytoskeletal signaling

Cytoskeletal signaling provides a medium for internal neuronal signaling that could play a key role in biological learning. Signaling within a neuron along microtubules is plausible according to a number of theoretical models and experimental observations. If used by biological neurons, this type of signaling would provide the missing link for the biological implementation of many learning models, including back-error propagation. Back-error propagation is widely used and demonstration of its plausibility allows uniting artificial learning models with realistic neural models. Additional learning models can be modeled with biologically plausible mechanisms that involve internal cytoskeletal signaling, such as the sigma-pi architecture. Biologically plausible implementations of these learning models are described.<<ETX>>

The Entropy of Laughter: Discriminative Power of Laughter's Entropy in the Diagnosis of Depression

Laughter is increasingly present in biomedical literature, both in analytical neurological aspects and in applied therapeutic fields. The present paper, bridging between the analytical and the applied, explores the potential of a relevant variable of laughter’s acoustic signature—entropy—in the detection of a widespread mental disorder, depression, as well as in gauging the severity of its diagnostic. In laughter, the Shannon–Wiener entropy of the distribution of sound frequencies, which is one of the key features distinguishing its acoustic signal from the utterances of spoken language, has not been a specific focus of research yet, although the studies of human language and of animal communication have pointed out that entropy is a very important factor regarding the vocal/acoustic expression of emotions. As the experimental survey of laughter in depression herein undertaken shows, it was possible to discriminate between patients and controls with an 82.1% accuracy just by using laughter’s entropy and by applying the decision tree procedure. These experimental results, discussed in the light of the current research on laughter, point to the relevance of entropy in the spontaneous bona fide extroversion of mental states toward other individuals, as the signal of laughter seems to imply. This is in line with recent theoretical approaches that rely on the optimization of a neuro-informational free energy (and associated entropy) as the main “stuff” of brain processing.