Liquid state machine built of Hodgkin-Huxley neurons-pattern recognition and informational entropy

Neural networks built of Hodgkin-Huxley neurons are examined. Such structures behave like Liquid State Machines. They can effectively process geometrical patterns shown to artificial retina into precisely defined output. The analysis of output responses is performed in two ways: by means of Artificial Neural Network and by calculating informational entropy

Computational ability of LSM ensemble in the model of mammalian visual system

Ensembles of artificial Hodgkin-Huxley neural microcircuits are examined. The networks discussed in this article simulate the cortex of the primate visual system. We use a modular architecture of the cortex divided into columns. The results of parallel simulations based on the liquid computing theory are presented in some detail. Separation ability of groups of neural microcircuits is observed. We show that such property may be useful for explaining some pattern recognition phenomena

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

Investigating Mammalian Visual System with methods of informational theory

We examine a simple model of mammalian visual system. This structure is simulated by means of several hundred Hodgkin-Huxley neurons. We investigate signal processing properties of the model. Some methods taken from informational theory are applied to the analysis of Primary Visual Cortex' dynamics. Discussion of efficiency of such methods in two dimensional movement detection is presented in some detail

Hebbian encoding in the biological visual system

We examined neural networks built of several hundred Hodgkin-Huxley neurons. The main aim of the research described below was to simulate memory processes occurring in hippocampus and biological visual system. In our model we chose the ancient Chinese I-Ching Oracle as a set of input patterns. Maps of Hebbian weights appearing on the output device of the model can be analysed by artificial neural networks playing a role of some kind of visual consciousness

Liquid computing and analysis of sound signals

Liquid Computing Theory is a proposal of modelling the behaviour of neural microcircuits.It focuses on creating a group of neurons, known as a liquid layer, responsible for preprocessingof the signal that is being analysed. Specific information is achieved by the readout layers, task orientedgroups of neurons, taught to extract particular information from the state of liquid layer. TheLSMs have been used to analyse sound signals. The liquid layer was implemented in the PCSIM Simulator,and the readout layer has been prepared in the JNNS simulator. It could successfully recognisecertain sounds despite noises. Those results encourage further research of the computational potentialof Liquid State Machines including working in parallel with many readout layers

Znaczenie Kliniczne Obliczeniowych Modeli Mózgu W Rehabilitacji Neurologicznej

Despite quick development of the newest neurorehabilitation methods and techniques there is a need for experimentally validated models of motor learning, neural control of movements, functional recovery, therapy control strategies.Computational models are perceived as another way for optimization and objectivization of the neurorehabilitation. Fully understanding of the neural repair is needed for simulation of reorganization and remodeling of neural networks as the effect of neurorehabilitation. Better understanding can significantly influence both traditional forms of the therapy (neurosurgery, drug therapy, neurorehabilitation, etc.) and use of the advanced Assitive Technology (AT) solutions, e.g. brain-computer interfaces (BCIs) and neuroprostheses [49, 50] or artificial brain stimulation.Pomimo szybkiego rozwoju najnowszych metod i technik rehabilitacyjnych istnieje potrzeba tworzenia eksperymentalnie weryfikowalnych modeli motorycznego uczenia się, nerwowej kontroli ruchu, funkcjonalnego powrotu do zdrowia oraz strategii terapeutycznych.Modele obliczeniowe są uważanie za kolejny ze sposobów optymalizacji i obiektywizacji rehabilitacji neurologicznej. Pełne zrozumienie naprawy struktur nerwowych wymaga modelowania reorganizacji i przemodelowania sieci neuronowych następujących w efekcie rehabilitacji neurologicznej. Lepsze zrozumienie ww. procesów może znacząco wpłynąć zarówno na tradycyjne formy terapii (neurochirurgię, farmakoterapię, rehabilitację neurologiczną i inne), jak również użycie zaawansowanych rozwiązań technologii wspomagających, takich jak interfejsy mózg-komputer i neuroprotezy, jak również sztucznej stymulacji mózgu

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Simple cyclic movements as a distinct autism feature - computational approach

Diversity of symptoms in autism dictates a broad definition of Autism Spectrum of Disorders(ASD). Each year percentage of children diagnosed with ASD is growing. One common diag-nostic feature in individuals with ASD is the tendency to atypical simple cyclic movements.The motor brain activity seems to generate periodic attractor state that is hard to escape.Despite numerous studies scientists and clinicians do not know exactly if ASD is a result ofa simple but general mechanism, or a complex set of mechanisms, both on neural, molecularand system levels. Simulations using biologically relevant neural network model presentedhere may help to reveal simplest mechanisms that may be responsible for specific behavior.Abnormal neural fatigue mechanisms may be responsible for motor as well as many if notall other symptoms observed in ASD

The Dynamical Renaissance in Neuroscience

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin-Huxley and FitzHugh-Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience