Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Computational Astrocyence: Astrocytes encode inhibitory activity into the frequency and spatial extent of their calcium elevations

Deciphering the complex interactions between neurotransmission and astrocytic $Ca^{2+}$ elevations is a target promising a comprehensive understanding of brain function. While the astrocytic response to excitatory synaptic activity has been extensively studied, how inhibitory activity results to intracellular $Ca^{2+}$ waves remains elusive. In this study, we developed a compartmental astrocytic model that exhibits distinct levels of responsiveness to inhibitory activity. Our model suggested that the astrocytic coverage of inhibitory terminals defines the spatial and temporal scale of their $Ca^{2+}$ elevations. Understanding the interplay between the synaptic pathways and the astrocytic responses will help us identify how astrocytes work independently and cooperatively with neurons, in health and disease.Comment: 4 pages, 3 figures, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI '19

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Dynamics and sparsity in latent threshold factor models: A study in multivariate EEG signal processing

We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models, extending prior Bayesian modeling of multiple EEG series and highlighting the practical utility of the latent thresholding concept in multivariate, non-stationary time series analysis.Comment: 27 pages, 13 figures, link to external web site for supplementary animated figure

Üner Tan Syndrome: Review and Emergence of Human Quadrupedalism in Self-Organization,\ud Attractors and Evolutionary Perspectives\ud

The first man reported in the world literature exhibiting habitual quadrupedal locomotion was discovered by a British traveler and writer on the famous Baghdat road near Havsa/Samsun on the middle Black-Sea coast of Turkey (Childs, 1917). Interestingly, no single case with human quadrupedalism was reported in the scientific literature after Child's first description in 1917 until the first report on the Uner Tan syndrome (UTS: quadrupedalism, mental retardation, and impaired speech or no speech)in 2005 (Tan, 2005, 2006). Between 2005 and 2010, 10 families exhibiting the syndrome were discovered in Turkey with 33 cases: 14 women (42.4%) and 19 men (57.6%). Including a few cases from other countries, there were 25 men (64.1%)and 14 women (35.9%). The number of men significantly exceeded the number of women (p < .05). Genetics alone did not seem to be informative for the origins of many syndromes, including the Uner Tan syndrome. From the viewpoint of dynamical systems theory, there may not be a single factor including the neural and/or genetic codes that predetermines the emergence of the human quadrupedalism.Rather, it may involve a self-organization process, consisting of many decentralized and local interactions among neuronal, genetic, and environmental subsystems. The most remarkable characteristic of the UTS, the diagonal-sequence quadrupedalism is well developed in primates. The evolutionarily advantage of this gait is not known. However, there seems to be an evolutionarily advantage of this type of locomotion for primate evolution, with regard to the emergence of complex neural circuits with related highly complex structures. Namely, only primates with diagonal-sequence quadrupedal locomotion followed an evolution favoring larger brains, highly developed cognitive abilities with hand skills, and language, with erect posture and bipedal locomotion, creating the unity of human being. It was suggested that UTS may be considered a further example for Darwinian diseases, which may be associated with an evolutionary understanding of the disorders using evolutionary principles, such as the natural selection. On the other hand, the human quadrupedalism was proposed to be a phenotypic example of evolution of reverse, i.e., the reacquisition by derived populations of the same character states as those of ancestor populations. It was also suggested that the emergence of the human quadrupedalism may be related to self-organizing processes occurring in complex systems, which select or attract one preferred behavioral state or locomotor trait out of many possible attractor states. Concerning the locomotor patterns, the dynamical systems in brain and body of the developing child may prefer some kind of locomotion, according to interactions of the internal components and the environmental conditions, without a direct role of any causative factor(s), such as genetic or neural codes, consistent with the concept of self-organization, suggesting no single element may have a causal priority

Cell-to-Cell Communication in Learning and Memory: From Neuro- and Glio-Transmission to Information Exchange Mediated by Extracellular Vesicles

Most aspects of nervous system development and function rely on the continuous crosstalk between neurons and the variegated universe of non-neuronal cells surrounding them. The most extraordinary property of this cellular community is its ability to undergo adaptive modifications in response to environmental cues originating from inside or outside the body. Such ability, known as neuronal plasticity, allows long-lasting modifications of the strength, composition and efficacy of the connections between neurons, which constitutes the biochemical base for learning and memory. Nerve cells communicate with each other through both wiring (synaptic) and volume transmission of signals. It is by now clear that glial cells, and in particular astrocytes, also play critical roles in both modes by releasing different kinds of molecules (e.g., D-serine secreted by astrocytes). On the other hand, neurons produce factors that can regulate the activity of glial cells, including their ability to release regulatory molecules. In the last fifteen years it has been demonstrated that both neurons and glial cells release extracellular vesicles (EVs) of different kinds, both in physiologic and pathological conditions. Here we discuss the possible involvement of EVs in the events underlying learning and memory, in both physiologic and pathological condition

Disturbances in the spontaneous attribution of social meaning in schizophrenia

Background. Schizophrenia patients show disturbances on a range of tasks that assess mentalizing or 'Theory of Mind' (ToM). However, these tasks are often developmentally inappropriate, make large demands on verbal abilities and explicit problem-solving skills, and involve after-the-fact reflection as opposed to spontaneous mentalizing. Method. To address these limitations, 55 clinically stable schizophrenia out-patients and 44 healthy controls completed a validated Animations Task designed to assess spontaneous attributions of social meaning to ambiguous abstract visual stimuli. In this paradigm, 12 animations depict two geometric shapes' interacting' with each other in three conditions: (1) ToM interactions that elicit attributions of mental states to the agents, (2) Goal-Directed (GO) interactions that elicit attributions of simple actions, and (3) Random scenes in which no interaction occurs. Verbal descriptions of each animation are rated for the degree of Intentionality attributed to the agents and for accuracy. Results. Patients had lower Intentionality ratings than controls for ToM and GO scenes but the groups did not significantly differ for Random scenes. The descriptions of the patients less closely matched the situations intended by the developers of the task. Within the schizophrenia group, performance on the Animations Task showed minimal associations with clinical symptoms. Conclusions. Patients demonstrated disturbances in the spontaneous attribution of mental states to abstract visual stimuli that normally evoke such attributions. Hence, in addition to previously established impairment on mentalizing tasks that require logical inferences about others' mental states, individuals with schizophrenia show disturbances in implicit aspects of mentalizing

The importance of quantum decoherence in brain processes

Based on a calculation of neural decoherence rates, we argue that that the degrees of freedom of the human brain that relate to cognitive processes should be thought of as a classical rather than quantum system, i.e., that there is nothing fundamentally wrong with the current classical approach to neural network simulations. We find that the decoherence timescales ~10^{-13}-10^{-20} seconds are typically much shorter than the relevant dynamical timescales (~0.001-0.1 seconds), both for regular neuron firing and for kink-like polarization excitations in microtubules. This conclusion disagrees with suggestions by Penrose and others that the brain acts as a quantum computer, and that quantum coherence is related to consciousness in a fundamental way.Comment: Minor changes to match accepted PRE version. 15 pages with 5 figs included. Color figures and links at http://www.physics.upenn.edu/~max/brain.html or from [email protected]. Physical Review E, in pres