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Section DYNAMICS IN LIFE SCIENCES, NEUROSCIENCE APPLICATIONS WORKSHOP
References
1. WOJCIK J, SCHWABEDAL J., CLEWLEY R. & SHILNIKOV A. (2014): Key bifurcations of bursting polyrhythms in 3-cell central pattern generators, PloS ONE, 9 (4), e92918.
2. LOZANO A., RODRIGUEZ M. & BARRIO R. (2016): Control strategies of 3-cell Central Pattern Generator via global stimuli, Scientific Reports, in press.
3. BARRIO R. & SHILNIKOV A. (2011): Parameter-sweeping techniques for temporal dynamics of neuronal systems: case study of the Hindmarsh-Rose model, J Mathematical Neuroscience, 1 (6), 1-22.
4. BARRIO R., MARTINEZ MA., SERRANO S. & SHILNIKOV A. (2014): Macro and micro-chaotic structures in the Hindmarsh-Rose model of bursting neurons. Chaos, 24 (2), 023128.
5. BARRIO R., RODRIGUEZ S., SERRANO S. & SHILNIKOV A. (2015): Mechanism of quasi-periodic lag jitter in bursting rhythms by a neuronal network, EPL, 112 (3), 38002.
Frequency and Hemispheric Specialization of Brain Activity in Convergent and Divergent Thinking: the Intelligence Effect
O.M. Razumnikova1,2 *, K.D. Krivonogova1, A.A. Yashanina1,2
1 Novosibirsk State Technical University, Novosibirsk, Russia;
2 State Research Institute of Physiology and Basic Medicine, Novosibirsk, Russia. * Presenting e-mail: [email protected]
The actual problem of current research in cognitive activity is the study of the organization of different forms of thinking. J. P. Guilford introduced the concepts of convergent and divergent thinking, representing a fundamentally different form of solution to the problem: in the first case, the goal is to find the only correct solution to the problem, while the second - generation of set of alternative ideas [2]. A quantitative measure of the success of convergent thinking (CT) can be considered as the level of intelligence, since it used the criterion of measuring the only answer; the effectiveness of divergent thinking (DT) can estimate the parameters of creativity: fluency generation of ideas and originality. Transition from convergent to divergent thinking in creative problem solving, as well as different points of view on the relationship of intelligence and creativity [4, 7] induce the question about the causes of these differences, which can be resolved at the neurophysiological level. In this regard, the aim of the work was to study the relation of intelligence and changes of bioelectrical activity of the cerebral cortex in convergent and divergent thinking.
The study involved 46 students of NSTU. Verbal, figurative and arithmetic components of intelligence were determined by Amthauer's test. To register 19-channell EEG, the program "Mitsar EEG-201" (St. Petersburg, Russia) was used. EEG was recorded in three functional states: baseline, in situations of CT (sequential addition in the mind of the prime numbers) and DT (decision of heuristic problem). Throughout all experimental conditions (resting, CT, and DT) participants had their eyes closed. Data processing was based on 30 artifact-free EEG signals with Han windowed epochs of 2 s. The averaged spectral power density for the six frequency ranges from delta to beta 2 using fast Fourier transformation was calculated.
CT-induced EEG changes were found in the delta and theta range with increased power delta rhythm, significant for left-hemispheric activity and theta rhythm - the right hemisphere. Delta oscillations during DT increased in both the left and right hemisphere, another EEG correlate of DT was to strengthen the right-hemispheric alpha 2oscillations. The analysis of regional effects showed that the increase of CT associated delta rhythm was represented in the frontal areas, whereas DT - covering widespread cortical areas. The right hemispheric increase of alpha2 rhythm during DT was presented in posterior cortex whereas the left hemispheric effect as compared to the CT was generalized. Given that the change in the power of delta rhythm is associated with "internal" attention while enhancing cognitive load [3], we can conclude that the solution of the heuristic task requires the use of large brain resources, combining the functions of both hemispheres, and changes in the theta rhythm in the CT can be explained by increased support attention needed to perform arithmetic operations and preservation in the working memory of intermediate summation results [6]. DT related changes of alpha2 activity are consistent with the concepts of "defocused" or internal attention required finding an original idea [1,5].
It was found significant positive relationship between the arithmetic intelligence and the success of CT (calculated amount) (see Figure), and between verbal or figurative and spatial components of the intelligence and efficiency of DT (originality of ideas).
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Section DYNAMICS IN LIFE SCIENCES, NEUROSCIENCE APPLICATIONS WORKSHOP
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a) b)
Figure 1. Relationships between convergent thinking efficiency (CT) and arithmetic intelligence (IQar) (a) and between efficiency of divergent thinking (DT) and spatial intelligence (IQs) (b)
Different patterns of correlation between IQ and EEG characteristics, registered at the CT or DT were obtained. The right hemispheric delta rhythm during CT was negatively associated with IQar. The alpha 2 in DT correlated with IQs and verbal intelligence component (IQv), this negative relationship was shown for the activity and the left and right hemispheres. Negative correlations between IQv and bilateral alpha 2 were significant for the CT.
In line with the detected correlation between IQ and an efficiency of CT and DT on the one hand, or EEG correlates of these forms of thinking - on the other, it is possible to conclude that the degree of functional activation cortex varies depending on the intellectual abilities, and a large activation represents lower IQ. Hemispheric brain activity during DM associated with IQv and IQs whereas during CT - with IQar and IQv. These effects indicate that the different components of IQ modulated thinking strategies to achieve efficient execution of tasks of different types.
References
1. M. Benedek, et al., Neuropsychologia, 2014, 56, 393-400.
2. J.P. Guilford The nature of human intelligence. New York, 1967.
3. T. Harmony, T.Fernandez, and J. Silva, Intern. J. Psychophysiol., 1996, 24, 161-171.
4. E. Jauk, et al., Intelligence, 2013, 41(4), 212-221.
5. C. Martindale, D. Hines, Biol. Psychol., 1975, 3, 91-100.
6. S. Micheloyannis, Human Brain Mapping, 2009, 30, 200-208.
7. F. Preckel, H. Holling, and M. Wiesem, Pers. Individ. Differ., 2006, 40, 159-170.
Modelling Noise-Induced Escape Problems in Networks
J. L. Creaser*, P. Ashwin, K. Tsaneva-Atanasova
EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK. * Presenting e-mail: [email protected]
Mathematical models of excitable cells, such as neurones, are often characterised by different dynamic regimes, such as alternating excited and rest states. The transient dynamics responsible for the transition between dynamic states are often discounted or overlooked. Analysis of the transition between dynamic states is crucial to understanding the evolution of epileptic seizures or the initiation of tremors associated with Parkinson's disease.
We consider a phenomenological model of seizure initiation of coupled bi-stable oscillators (represented by a sub-critical bifurcation normal form) with noise. Using dynamical systems analysis and numerical simulations we investigate emergent transient dynamics for small motif networks of this model. Specifically, we build small dynamically perturbed motif networks and consider the effect of network structure, noise and separation of timescales on the exit (escape)-time problem.
