Time-invariant person-specific frequency templates in human brain activity.

The various human brain tasks are performed at different locations and time scales. Yet, we discovered the existence of time-invariant (above an essential time scale) partitioning of the brain activity into personal state-specific frequency bands. For that, we perform temporal and ensemble averaging of best wavelet packet bases from multielectrode electroencephalogram recordings. These personal frequency bands provide new templates for quantitative analyses of brain function, e.g., normal versus epileptic activity.

Adult, sex-specific behavior characterized by elevated neuronal functional complexity.

Adult, sex-specific behaviors are good models for context-specific behavioral patterns. Here, we focus on a unique example: locust oviposition. The neural network's rhythmic output can be activated at all life stages, including embryonic, in both females and males. All recorded patterns, independent of age and sex, showed similar basic statistics. Activity density plots and spectral analysis, however, revealed that oscillations in burst rates recorded from females show greater variation quantities than those recorded from males. Furthermore, only the neural output recorded from sexually mature females was characterized by significantly elevated functional complexity. Thus, while the neural network has inherent potential to generate motor activities at different levels of complexity, only the proper internal-environmental and behavioral context triggers expression of its full potential of information capacity.

Self-regulated complexity in cultured neuronal networks.

New quantified observables of complexity are identified and utilized to study sequences (time series) recorded during the spontaneous activity of different size cultured networks. The sequence is mapped into a tiled time-frequency domain that maximizes the information about local time-frequency resolutions. The sequence regularity is associated with the domain homogeneity and its complexity with its local and global variations. Shuffling the recorded sequence lowers its complexity down to artificially constructed ones. The new observables are utilized to identify self-regulation motifs in observed complex network activity.

Hidden neuronal correlations in cultured networks.

Utilization of a clustering algorithm on neuronal spatiotemporal correlation matrices recorded during a spontaneous activity of in vitro networks revealed the existence of hidden correlations: the sequence of synchronized bursting events (SBEs) is composed of statistically distinguishable subgroups each with its own distinct pattern of interneuron spatiotemporal correlations. These findings hint that each of the SBE subgroups can serve as a template for coding, storage, and retrieval of a specific information.

A method for spike sorting and detection based on wavelet packets and Shannon's mutual information.

Studying the dynamics of neural activity via electrical recording, relies on the ability to detect and sort neural spikes recorded from a number of neurons by the same electrode. We suggest the wavelet packets decomposition (WPD) as a tool to analyze neural spikes and extract their main features. The unique quality of the wavelet packets-adaptive coverage of both time and frequency domains using a set of localized packets, facilitate the task. The best basis algorithm utilizing the Shannon's information cost function and local discriminant basis (LDB) using mutual information are employed to select a few packets that are sufficient for both detection and sorting of spikes. The efficiency of the method is demonstrated on data recorded from in vitro 2D neural networks, placed on electrodes that read data from as many as five neurons. Comparison between our method and the widely used principal components method and a sorting technique based on the ordinary wavelet transform (WT) shows that our method is more efficient both in separating spikes from noise and in resolving overlapping spikes.

Long term behavior of lithographically prepared in vitro neuronal networks.

We measured the long term spontaneous electrical activity of neuronal networks with different sizes, grown on lithographically prepared substrates and recorded with multi-electrode-array technology. The time sequences of synchronized bursting events were used to characterize network dynamics. All networks exhibit scale-invariant Lévy distributions and long-range correlations. These observations suggest that different-size networks self-organize to adjust their activities over many time scales. As predictions of current models differ from our observations, this calls for revised models.