Does dynamical synchronization among neurons facilitate learning and enhance task performance?

Synchronization among groups of neurons is an interesting yet mysterious mechanism in the brain. We propose and demonstrate that the adjustable timing of neural activities can produce profound effect on learning and task implementation. On one hand, learning of more complex patterns becomes possible because of the enhanced capability of classification. On the other hand, implementation of a complex task is aided through active maintenance and control of multiple rules and items. This sheds light on the development of new intelligent system, as well as the cause of impaired learning and task performance in patients.

Synthesis and photophysical studies of chiral helical macrocyclic scaffolds via coordination-driven self-assembly of 1,8,9,16-tetraethynyltetraphenylene. formation of monometallic platinum(II) and dimetallic platinum(II)-ruthenium(II) complexes.

This paper is concerned with the synthesis and reactions of enantiopure 1,8,9,16-tetraethynyltetraphenylene (3). We obtained 3 in 34% yield through four steps starting from 1,8,9,16-tetrahydroxytetraphenylene (2a) via a functional group interconversion strategy. On the basis of this chiral "helical" building block, three rigid helical macrocycles 14, 15, and 22 were designed. Complexes 14 and 15 were constructed via coordination-driven self-assembly with platinum(II) complexes 8 and 9b, while 22 cannot be obtained successfully. Then macrocycle 28 was designed on the structural basis of 22 to which octyl chains were introduced, in the hope of improving the solubility of the complex. Macrocycle 28 was finally formed and was characterized by NMR spectroscopy, elemental analysis, and electrospray mass spectrometry. For the enantiopure 15 and 28, circular dichroism (CD) spectra also exhibited chiral properties. Complexes 27 and 28 both exhibited an intense emission band at 621 nm in acetonitrile at 298 K upon excitation at λ > 420 nm.

Visual perception of ambiguous figures: synchronization based neural models.

We develop and study two neural network models of perceptual alternations. Both models have a star-like architecture of connections with a central element connected to a set of peripheral elements. A particular perception is simulated in terms of partial synchronization between the central element and some sub-group of peripheral elements. The first model is constructed from phase oscillators and the mechanism of perceptual alternations is based on chaotic intermittency under fixed parameter values. Similar to experimental evidence, the distribution of times between perceptual alternations is represented by the gamma distribution. The second model is built of spiking neurons of the Hodgkin-Huxley type. The mechanism of perceptual alternations is based on plasticity of inhibitory synapses which increases the inhibition from the central unit to the neural assembly representing the current percept. As a result another perception is formed. Simulations show that the second model is in good agreement with behavioural data on switching times between percepts of ambiguous figures and with experimental results on binocular rivalry of two and four percepts.

Noise accelerates synchronization of coupled nonlinear oscillators.

For a chain of homogeneous nonlinear oscillators starting from different initial phases, a certain amount of time is required for the system to evolve to complete phase synchronization. The effect of independent noise in such a system was investigated, and an optimal noise intensity was found that minimized the average synchronization time. Both threshold noise and connection noise show similar effects. The features of the phenomenon and the underlying mechanism are discussed through the analysis of a two-unit system and the numerical studies of chains up to 30 units in length.

6,7-Bismethoxy-2,11-dihydroxytetraphenylene Derived Macrocycles: Synthesis, Structures, and Complexation with Fullerenes.

6,7-Bismethoxy-2,11-dihydroxytetraphenylene (1), a novel building block of tetraphenylene-derived macrocycles, was synthesized via palladium-catalyzed cross-coupling reactions and characterized by X-ray diffraction. The relevant macrocyclic hosts derived from 1 have well-defined structures with fixed conformations both in solution and solid state. They showed efficient and unique properties toward complexation with fullerenes C60 and C70 in toluene.

Postinhibitory rebound delay and weak synchronization in Hodgkin-Huxley neuronal networks.

Noise-induced weak synchronized oscillatory activities in a globally inhibitory coupled Hodgkin-Huxley neuronal network are studied numerically. A kind of intrinsic delay induced by the postinhibitory rebound is observed and is found to be important in determining the overall frequency of the network. Synchronization occurs in an optimal range of noise intensity with a bell-shaped curve when the inhibitory coupling strength is sufficiently strong. Comparisons with the results for the excitatory coupling are also addressed.

Theta-alpha cross-frequency synchronization facilitates working memory control - a modeling study.

Despite decades of research, the neural mechanism of central executive and working memory is still unclear. In this paper, we propose a new neural network model for the real-time control of working memory. The key idea is to consider separately the role of neural activation from that of oscillatory phase. Neural populations encoding different information would not confuse each other when the populations have different oscillatory phases. Depending on the current situation, relevant memories bind together through phase-locking between theta-frequency oscillation of a Central Unit and alpha-frequency oscillations of the relevant group of Memory Units. The Central Unit dynamically controls which Memory Units should be synchronized (and the encoded memory would be processed), and which units should be out of phase (the encoded memory is standby and would not be processed yet). Simulations of two working memory tasks are provided as examples. The model is in agreement with many recent experimental results of human scalp EEG analysis, which reported observations of neural synchronization and cross-frequency coupling during working memory tasks. This model offers a possible explanation of the underlying mechanism for these experiments.

Spiking neural network model for memorizing sequences with forward and backward recall.

We present an oscillatory network of conductance based spiking neurons of Hodgkin-Huxley type as a model of memory storage and retrieval of sequences of events (or objects). The model is inspired by psychological and neurobiological evidence on sequential memories. The building block of the model is an oscillatory module which contains excitatory and inhibitory neurons with all-to-all connections. The connection architecture comprises two layers. A lower layer represents consecutive events during their storage and recall. This layer is composed of oscillatory modules. Plastic excitatory connections between the modules are implemented using an STDP type learning rule for sequential storage. Excitatory neurons in the upper layer project star-like modifiable connections toward the excitatory lower layer neurons. These neurons in the upper layer are used to tag sequences of events represented in the lower layer. Computer simulations demonstrate good performance of the model including difficult cases when different sequences contain overlapping events. We show that the model with STDP type or anti-STDP type learning rules can be applied for the simulation of forward and backward replay of neural spikes respectively.

Selective attention model with spiking elements.

A new biologically plausible model of visual selective attention is developed based on synaptically coupled Hodgkin-Huxley neurons. The model is designed according to a two-layer architecture of excitatory and inhibitory connections which comprises two central neurons and a population of peripheral neurons. Two types of inhibition from the central neurons are present: fixed inhibition which is responsible for the formation of the attention focus, and short-term plastic inhibition which is responsible for the shift of attention. The regimes of synchronous dynamics associated with the development of the attentional focus are studied. In particular, the regime of partial synchronization between spiking activity of the central and peripheral neurons is interpreted as object selection to the focus of attention. It is shown that peripheral neurons with higher firing rates are selected preferentially by the attention system. The model correctly reproduces some observations concerning the mechanisms of attentional control, such as the coherence of spikes in the population of neurons included in the focus of attention, and the inhibition of neurons outside the focus of attention. Sequential selection of stimuli simultaneously present in the visual scene is demonstrated by the model in the frequency domain in both a formal example and a real image.

A neural model of selective attention and object segmentation in the visual scene: an approach based on partial synchronization and star-like architecture of connections.

A brain-inspired computational system is presented that allows sequential selection and processing of objects from a visual scene. The system is comprised of three modules. The selective attention module is designed as a network of spiking neurons of the Hodgkin-Huxley type with star-like connections between the central unit and peripheral elements. The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed. Such dynamics corresponds to the partial synchronization mode. It is shown that peripheral neurons with higher firing rates are preferentially drawn into partial synchronization. We show that local excitatory connections facilitate synchronization, while local inhibitory connections help distinguishing between two groups of peripheral neurons with similar intrinsic frequencies. The module automatically scans a visual scene and sequentially selects regions of interest for detailed processing and object segmentation. The contour extraction module implements standard image processing algorithms for contour extraction. The module computes raw contours of objects accompanied by noise and some spurious inclusions. At the next stage, the object segmentation module designed as a network of phase oscillators is used for precise determination of object boundaries and noise suppression. This module has a star-like architecture of connections. The segmented object is represented by a group of peripheral oscillators working in the regime of partial synchronization with the central oscillator. The functioning of each module is illustrated by an example of processing of the visual scene taken from a visual stream of a robot camera.

Modelling cardiac dynamics with integral pulse frequency modulated units.

We investigate a new model for the cardiac system. It embodies the main features of cardiac activity with great flexibility. It can be tuned to suit different cell types, and scaled up or down to represent either a single cell, an aggregate, or whole tissue. We demonstrate its use in the generation of a clinically realistic electrocardiogram (ECG) and other dynamical behaviours such as spiral waves. Our model is computationally economic - about 1000 times faster than ion conductance based models and is thus ideal for doing large scale simulations.