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      Arguments for Nested Patterns in Neural Ensembles

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          Abstract

          This paper describes a relatively simple way of allowing a brain model to self-organise its concept patterns through nested structures. Time is a key element and a simulator would be able to show how patterns may form and then fire in sequence, as part of a search or thought process. It uses a very simple equation to show how the inhibitors in particular, can switch off certain areas, to allow other areas to become the prominent ones and thereby define the current brain state. This allows for a small amount of control over what appears to be a chaotic structure inside of the brain. It is attractive because it is still mostly mechanical and therefore can be added as an automatic process, or the modelling of that. The paper also describes how the nested pattern structure can be used as a basic counting mechanism.

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          Most cited references 6

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          Neural network dynamics.

          Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dynamics: (a) sustained responses to transient stimuli, which provide a model of working memory; (b) oscillatory network activity; and (c) chaotic activity, which models complex patterns of background spiking in cortical and other circuits. We also review propagation of stimulus-driven activity through spontaneously active networks. Exploring these aspects of neural network dynamics is critical for understanding how neural circuits produce cognitive function.
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            Regional brain blood flow in man during acute changes in arterial blood gases.

            Despite the importance of blood flow on brainstem control of respiratory and autonomic function, little is known about regional cerebral blood flow (CBF) during changes in arterial blood gases.We quantified: (1) anterior and posterior CBF and reactivity through a wide range of steady-state changes in the partial pressures of CO2 (PaCO2) and O2 (PaO2) in arterial blood, and (2) determined if the internal carotid artery (ICA) and vertebral artery (VA) change diameter through the same range.We used near-concurrent vascular ultrasound measures of flow through the ICA and VA, and blood velocity in their downstream arteries (the middle (MCA) and posterior (PCA) cerebral arteries). Part A (n =16) examined iso-oxic changes in PaCO2, consisting of three hypocapnic stages (PaCO2 =∼15, ∼20 and ∼30 mmHg) and four hypercapnic stages (PaCO2 =∼50, ∼55, ∼60 and ∼65 mmHg). In Part B (n =10), during isocapnia, PaO2 was decreased to ∼60, ∼44, and ∼35 mmHg and increased to ∼320 mmHg and ∼430 mmHg. Stages lasted ∼15 min. Intra-arterial pressure was measured continuously; arterial blood gases were sampled at the end of each stage. There were three principal findings. (1) Regional reactivity: the VA reactivity to hypocapnia was larger than the ICA, MCA and PCA; hypercapnic reactivity was similar.With profound hypoxia (35 mmHg) the relative increase in VA flow was 50% greater than the other vessels. (2) Neck vessel diameters: changes in diameter (∼25%) of the ICA was positively related to changes in PaCO2 (R2, 0.63±0.26; P<0.05); VA diameter was unaltered in response to changed PaCO2 but yielded a diameter increase of +9% with severe hypoxia. (3) Intra- vs. extra-cerebral measures: MCA and PCA blood velocities yielded smaller reactivities and estimates of flow than VA and ICA flow. The findings respectively indicate: (1) disparate blood flow regulation to the brainstem and cortex; (2) cerebrovascular resistance is not solely modulated at the level of the arteriolar pial vessels; and (3) transcranial Doppler ultrasound may underestimate measurements of CBF during extreme hypoxia and/or hypercapnia.
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              Ant algorithms and stigmergy

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                Author and article information

                Journal
                2014-03-25
                2014-04-29
                Article
                1403.6274

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                Proceedings of the Science and Information Conference (SAI'14), August 27-29, 2014, pp. 488 - 492
                Preprint
                cs.NE q-bio.NC

                Neural & Evolutionary computing, Neurosciences

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