| 1. |
- Liljenström, Hans
(författare)
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Attention Modulation of Sensory Systems
- 2009
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Konferensbidrag (refereegranskat)abstract
- Attention and arousal seems to enhance the efficiency of neural information processing. We use computer simulations of various sensory systems in order to investigate how the neurodynamics of these systems can be modulated for optimal performance in an unknown and changing environment. Using an inter-scale model of the insect antennal lobe, we demonstrate how attention modulation can change the sensitivity for sex pheromones. We also study how neural oscillations in mammalian olfactory cortex can serve to amplify weak signals and sustain an input pattern for more accurate information processing, and how chaotic-like behaviour could increase the sensitivity in initial, exploratory states. Finally, we use a neural network model of visual cortex area V4, in order to investigate potential cellular and network mechanisms for visual attention, reproducing experimental findings of attention induced gamma-frequency synchronization
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| 2. |
- Liljenström, Hans
(författare)
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On Color Texture Generating Based on Simplified KIII Model
- 2009
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Konferensbidrag (refereegranskat)abstract
- KIII model is an olfactory neural networks bionic model proposed by Walter J. Freeman. Its architecture simulates that of olfactory neural system, which is different from other artificial neural networks. Through simplifying KIII model, a color texture generating algorithm is proposed combining with RGB. In RGB space, the tricolor of each pixel (red, green and blue) is used as the model input and the model output is composed as the tricolor of corresponding pixel in generated texture image. Experimental results show that simplified KIII model can generate beautiful color texture images
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| 3. |
- Liljenström, Hans
(författare)
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Phase Transitions in Mesoscopic Brain Dynamics : Implications for Cognition and Consciousness
- 2009
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Konferensbidrag (refereegranskat)abstract
- Mesoscopic brain dynamics, typically studied with electroand magnetoencephalography (EEG and MEG) display a rich complexity of oscillatory and chaotic-like states, including many different frequencies, amplitudes and phases. Presumably, these different dynamical states correspond to different mental states and functions, and to study transitions between such states could give us valuable insight in brain-mind relations that should also be of clinical interest. We use computational methods to address these problems, with an objective to find relations between structure, dynamics and function. In particular, we have developed models of paleo- and neocortical structures, in order to study their mesoscopic neurodynamics, as a link between the microscopic neuronal and macroscopic mental events and processes. In this presentation, I will describe different types of models, where the emphasis is on network connectivity and structure, but also including molecular and cellular properties at varying detail, depending on the particular problem and experimental data available. We use these models to study how phase transitions can be induced in the mesoscopic neurodynamics of cortical networks by internal (natural) and external (artificial) factors. We relate and discuss the models and simulation results to macroscopic phenomena, such as arousal, attention, anaesthesia, learning, and mental disorders
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| 4. |
- Liljenström, Hans, et al.
(författare)
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Signatures of Depression in Non-Stationary Biometric Time Series
- 2009
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Ingår i: Computational Intelligence and Neuroscience. - : Hindawi Limited. - 1687-5265 .- 1687-5273. ; 2009
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Tidskriftsartikel (refereegranskat)abstract
- This paper is based on a discussion that was held during a special session on models of mental disorders, at the NeuroMath meeting in Stockholm, Sweden, in September 2008. At this occasion, scientists from different countries and different fields of research presented their research and discussed open questions with regard to analyses and models of mental disorders, in particular depression. The content of this paper emerged from these discussions and in the presentation we briefly link biomarkers (hormones), bio-signals (EEG) and biomaps (brain-maps via EEG) to depression and its treatments, via linear statistical models as well as nonlinear dynamic models. Some examples involving EEG-data are presented
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| 5. |
- Wahlund, B, et al.
(författare)
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Seizure (Ictal)--EEG characteristics in subgroups of depressive disorder in patients receiving electroconvulsive therapy (ECT)--a preliminary study and multivariate approach
- 2009
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Ingår i: Computational intelligence and neuroscience. - : Hindawi Limited. - 1687-5273 .- 1687-5265. ; , s. 965209-
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Tidskriftsartikel (refereegranskat)abstract
- Objectives. Examine frequency distributions of ictal EEG after ECT stimulation in diagnostic subgroups of depression.Methods. EEG registration was consecutively monitored in 33 patients after ECT stimulation. Patients were diagnosed according to DSM IV and subdivided into: (1) major depressive disorder with psychotic features(n=7), (2) unipolar depression(n=20), and (3) bipolar depression(n=6).Results. Results indicate that the diagnostically subgroups differ in their ictal EEG frequency spectrumml: (1) psychotic depression has a high occurrence of delta and theta waves, (2) unipolar depression has high occurrence of delta, theta and gamma waves, and (3) bipolar depression has a high occurrence of gamma waves. A linear discriminant function separated the three clinical groups with an accuracy of 94%.Conclusion. Psychotic depressed patients differ from bipolar depression in their frequency based on probability distribution of ictal EEG. Psychotic depressed patients show more prominent slowing of EEG than nonpsychotic depressed patients. Thus the EEG results may be supportive in classifying subgroups of depression already at the start of the ECT treatment.
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