Review: Nicotinic acetylcholine receptors to regulate important brain activity-what occurs at the molecular level?

Herein, we briefly review the role of nicotinic acetylcholine receptors in regulating important brain activity by controlled release of acetylcholine from subcortical neuron groups, focusing on a microscopic viewpoint and considering the nonlinear dynamics of biological macromolecules associated with neuron activity and how they give rise to advanced brain functions of brain.

On the Nature of Functional Differentiation: The Role of Self-Organization with Constraints.

The focus of this article is the self-organization of neural systems under constraints. In 2016, we proposed a theory for self-organization with constraints to clarify the neural mechanism of functional differentiation. As a typical application of the theory, we developed evolutionary reservoir computers that exhibit functional differentiation of neurons.

Physiological properties of Cantor coding-like iterated function system in the hippocampal CA1 network.

Cantor coding provides an information coding scheme for temporal sequences of events. In the hippocampal CA3-CA1 network, Cantor coding-like mechanism was observed in pyramidal neurons and the relationship between input pattern and recorded responses could be described as an iterated function system. However, detailed physiological properties of the system in CA1 remain unclear.

Functional differentiations in evolutionary reservoir computing networks.

We propose an extended reservoir computer that shows the functional differentiation of neurons. The reservoir computer is developed to enable changing of the internal reservoir using evolutionary dynamics, and we call it an evolutionary reservoir computer. To develop neuronal units to show specificity, depending on the input information, the internal dynamics should be controlled to produce contracting dynamics after expanding dynamics.

Constrained chaos in three-module neural network enables to execute multiple tasks simultaneously.

Constrained chaos introduced into a three-module neural network having feedforward inter-module structure could have potential abilities to execute multiple tasks simultaneously. Each module consists of a large number of binary state (±1) neurons. The entire activity pattern (neuron state) is updated by recurrent rule under certain external input to the first module and input to post-module from pre-module.

Mathematical modeling for evolution of heterogeneous modules in the brain.

Modular architecture has been found in most cortical areas of mammalian brains, but little is known about its evolutionary origin. It has been proposed by several researchers that maximizing information transmission among subsystems can be used as a principle for understanding the development of complex brain networks. In this paper, we study how heterogeneous modules develop in coupled-map networks via a genetic algorithm, where selection is based on maximizing bidirectional information transmission.

Information flow in heterogeneously interacting systems.

Motivated by studies on the dynamics of heterogeneously interacting systems in neocortical neural networks, we studied heterogeneously-coupled chaotic systems. We used information-theoretic measures to investigate directions of information flow in heterogeneously coupled Rössler systems, which we selected as a typical chaotic system. In bi-directionally coupled systems, spontaneous and irregular switchings of the phase difference between two chaotic oscillators were observed.

Transitory memory retrieval in a biologically plausible neural network model.

A number of memory models have been proposed. These all have the basic structure that excitatory neurons are reciprocally connected by recurrent connections together with the connections with inhibitory neurons, which yields associative memory (i.e.

Transitory behaviors in diffusively coupled nonlinear oscillators.

We study collective behaviors of diffusively coupled oscillators which exhibit out-of-phase synchrony for the case of weakly interacting two oscillators. In large populations of such oscillators interacting via one-dimensionally nearest neighbor couplings, there appear various collective behaviors depending on the coupling strength, regardless of the number of oscillators. Among others, we focus on an intermittent behavior consisting of the all-synchronized state, a weakly chaotic state and some sorts of metachronal waves.

A mathematical model for Cantor coding in the hippocampus.

Recent studies suggest that the hippocampus is crucial for memory of sequentially organized information. Cantor coding in hippocampal CA1 is theoretically hypothesized to provide a scheme for encoding temporal sequences of events. Here, in order to investigate this Cantor coding in detail, we construct a CA1 network model consisting of conductance-based model neurons.

Iterated function systems in the hippocampal CA1.

How does the information of spatiotemporal sequence stemming from the hippocampal CA3 area affect the postsynaptic membrane potentials of the hippocampal CA1 neurons? In a recent study, we observed hierarchical clusters of the distribution of membrane potentials of CA1 neurons, arranged according to the history of input sequences (Fukushima et al Cogn Neurodyn 1(4):305-316, 2007). In the present paper, we deal with the dynamical mechanism generating such a hierarchical distribution. The recording data were investigated using return map analysis.

Spatial clustering property and its self-similarity in membrane potentials of hippocampal CA1 pyramidal neurons for a spatio-temporal input sequence.

To clarify how the information of spatiotemporal sequence of the hippocampal CA3 affects the postsynaptic membrane potentials of single pyramidal cells in the hippocampal CA1, the spatio-temporal stimuli was delivered to Schaffer collaterals of the CA3 through a pair of electrodes and the post-synaptic membrane potentials were recorded using the patch-clamp recording method. The input-output relations were sequentially analyzed by applying two measures; "spatial clustering" and its "self-similarity" index. The membrane potentials were hierarchically clustered in a self-similar manner to the input sequences.

Chaotic itinerancy as a mechanism of irregular changes between synchronization and desynchronization in a neural network.

We investigate the dynamic character of a network of electrotonically coupled cells consisting of class I point neurons, in terms of a finite dimensional dynamical system. We classify a subclass of class I point neurons, called class I* point neurons. Based on this classification, we use a reduced Hindmarsh-Rose (H-R) model, which consists of two dynamical variables, to construct a network model consisting of electrotonically coupled H-R neurons.