Color image processing in a cellular neural-network environment

When low-level hardware simulations of cellular neural networks (CNNs) are very costly for exploring new applications, the use of a behavioral simulator becomes indispensable. This paper presents a software prototype capable of performing image processing applications using CNNs. The software is based on a CNN multilayer structure in which each primary color is assigned to a unique layer. This allows an added flexibility as different processing applications can be performed in parallel. To be able to handle a full range of color tones, two novel color mapping schemes were derived. In the proposed schemes the color information is obtained from the cell's state rather than from its output. This modification is necessary because for many templates CNN has only binary stable outputs from which only either a fully saturated or a black color can be obtained. Additionally, a postprocessor capable of performing pixelwise logical operations among color layers was developed to enhance the results obtained from CNN. Examples in the areas of medical image processing, image restoration, and weather forecasting are provided to demonstrate the robustness of the software and the vast potential of CN

Neuronal cell signal analysis: spike detection algorithm development for microelectrode array recordings

Neural signal acquisition and processing techniques are rising trends among wide scientific and commercial areas. Microelectrode array (MEA) technology makes it possible to access and record the electrical activity of neural cells. In this work, human pluripotent stem cell (hPSC) -derived neuronal populations were grown on MEA plates. The activity of the cells was recorded and the research about modern signal processing methods for the neural spike detection was performed. A list of approaches was selected for detailed investigation and the most efficient one was chosen as the new technique for permanent use in the research group. The performed laboratory activities involved cell culture plating, regular medium changes, spontaneous activity recordings and pharmacological manipulations. The data acquired from pharmacological experiments were used for the comparison between the old and new spike detection algorithms in terms of the numbers of the detected events. The Stationary Wavelet Transform-based Teager Energy Operator (SWTTEO) shows prominent performance in the tests with synthetic data. The use of the proposed algorithm in conjunction with the common amplitude-based thresholding enables to lower the threshold and to detect more spikes without an excessive number of false positives. This mode is applicable for real cell data. The detection method was considered superior and was further distributed for the processing of all neural data of the research group which include signals acquired from neuronal populations derived from human embryonic and induced pluripotent stem cells (hESCs and iPSCs) as well as rat cells

How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network

Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)–dentate gyrus (DG)–CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC–DG–CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV+-INs) in the DG, is an efficient pattern separator. Both external gamma-modulated inhibition and internal lateral inhibition mediated by PV+-INs substantially contributed to pattern separation. Both local connectivity and fast signaling at GC–PV+-IN synapses were important for maximum effectiveness. Similarly, mossy fiber synapses with conditional detonator properties contributed to pattern separation. By contrast, perforant path synapses with Hebbian synaptic plasticity and direct EC–CA3 connection shifted the network towards pattern completion. Our results demonstrate that the specific properties of cells and synapses optimize higher-order computations in biological networks and might be useful to improve the deep learning capabilities of technical networks

Noise-induced memory in extended excitable systems

We describe a form of memory exhibited by extended excitable systems driven by stochastic fluctuations. Under such conditions, the system self-organizes into a state characterized by power-law correlations thus retaining long-term memory of previous states. The exponents are robust and model-independent. We discuss novel implications of these results for the functioning of cortical neurons as well as for networks of neurons.Comment: 4 pages, latex + 5 eps figure

Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

The central nervous system needs to coordinate multiple muscles during postural control. Functional coordination is established through the neural circuitry that interconnects different muscles. Here we used multivariate information decomposition of multichannel EMG acquired from 14 healthy participants during postural tasks to investigate the neural interactions between muscles. A set of information measures were estimated from an instantaneous linear regression model and a time-lagged VAR model fitted to the EMG envelopes of 36 muscles. We used network analysis to quantify the structure of functional interactions between muscles and compared them across experimental conditions. Conditional mutual information and transfer entropy revealed sparse networks dominated by local connections between muscles. We observed significant changes in muscle networks across postural tasks localized to the muscles involved in performing those tasks. Information decomposition revealed distinct patterns in task-related changes: unimanual and bimanual pointing were associated with reduced transfer to the pectoralis major muscles, but an increase in total information compared to no pointing, while postural instability resulted in increased information, information transfer and information storage in the abductor longus muscles compared to normal stability. These findings show robust patterns of directed interactions between muscles that are task-dependent and can be assessed from surface EMG recorded during static postural tasks. We discuss directed muscle networks in terms of the neural circuitry involved in generating muscle activity and suggest that task-related effects may reflect gain modulations of spinal reflex pathways

PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics