Computational neurogenetic modeling: a methodology to study gene interactions underlying neural oscillations

We present new results from Computational Neurogenetic Modeling to aid discoveries of complex gene interactions underlying oscillations in neural systems. Interactions of genes in neurons affect the dynamics of the whole neural network model through neuronal parameters, which change their values as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and neuronal parameters, particular target states of the neural network operation can be achieved, and statistics about gene interaction matrix can be extracted. In such a way it is possible to model the role of genes and their interactions in different brain states and conditions. Experiments with human EEG data are presented as an illustration of this methodology and also, as a source for the discovery of unknown interactions between genes in relation to their impact on brain activity. © 2006 IEEE

A computational neurogenetic model of a spiking neuron

The paper presents a novel, biologically plausible spiking neuronal model that includes a dynamic gene network. Interactions of genes in neurons affect the dynamics of the neurons and the whole network through neuronal parameters that change as a function of gene expression. The proposed model is used to build a spiking neural network (SNN) illustrated on a real EEC data case study problem. The paper also presents a novel computational approach to brain neural network modeling that integrates dynamic gene networks with a neural network model. Interaction of genes in neurons affects the dynamics of the whole neural network through neuronal parameters, which are no longer constant, but change as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and ANN parameters, particular target states of the neural network operation can be achieved, and statistics about gene intercation matrix can be extracted. It is illustrated by means of a simple neurogenetic model of a spiking neural network (SNN). The behavior of SNN is evaluated by means of the local field potential, thus making it possible to attempt modeling the role of genes in different brain states, where EEC data is available to test the model. We use standard signal processing techniques like FFT to evaluate the SNN output to compare it with real human EEC data. © 2005 IEEE

String pattern recognition using evolving spiking neural networks and quantum inspired particle swarm optimization

This paper proposes a novel method for string pattern recognition using an Evolving Spiking Neural Network (ESNN) with Quantum-inspired Particle Swarm Optimization (QiPSO). This study reveals an interesting concept of QiPSO by representing information as binary structures. The mechanism optimizes the ESNN parameters and relevant features using the wrapper approach simultaneously. The N-gram kernel is used to map Reuters string datasets into high dimensional feature matrix which acts as an input to the proposed method. The results show promising string classification results as well as satisfactory QiPSO performance in obtaining the best combination of ESNN parameters and in identifying the most relevant features

Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

Computational modeling with spiking neural networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed

Autologous bone marrow stem cell intralesional transplantation repairing bisphosphonate related osteonecrosis of the jaw

<p>Abstract</p> <p>Purpose</p> <p>Bisphosphonate - related osteonecrosis of the JAW (BRONJ) is a well known side effect of bisphosphonate therapies in oncologic and non oncologic patients. Since to date no definitive consensus has been reached on the treatment of BRONJ, novel strategies for the prevention, risk reduction and treatment need to be developed. We report a 75 year old woman with stage 3 BRONJ secondary to alendronate and pamidronate treatment of osteoporosis. The patient was unresponsive to recommended treatment of the disease, and her BRONJ was worsening. Since bone marrow stem cells are know as being multipotent and exhibit the potential for differentiation into different cells/tissue lineages, including cartilage, bone and other tissue, we performed autologous bone marrow stem cell transplantation into the BRONJ lesion of the patient.</p> <p>Methods</p> <p>Under local anesthesia a volume of 75 ml of bone marrow were harvested from the posterior superior iliac crest by aspiration into heparinized siringes. The cell suspension was concentrated, using Ficoll - Hypaque^®centrifugation procedures, in a final volume of 6 ml. Before the injection of stem cells into the osteonecrosis, the patient underwent surgical toilet, local anesthesia was done and spongostan was applied as a carrier of stem cells suspension in the bone cavity, then 4 ml of stem cells suspension and 1 ml of patient's activated platelet-rich plasma were injected in the lesion of BRONJ.</p> <p>Results</p> <p>A week later the residual spongostan was removed and two weeks later resolution of symptoms was obtained. Then the lesion improved with progressive superficialization of the mucosal layer and CT scan, performed 15 months later, shows improvement also of bone via concentric ossification: so complete healing of BRONJ (stage 0) was obtained in our patient, and 30 months later the patient is well and without signs of BRONJ.</p> <p>Conclusion</p> <p>To our knowledge this is the first case of BRONJ successfully treated with autologous stem cells transplantation with a complete response.</p

Computational neurogenetic modelling: gene networks within neural networks

This paper introduces a novel connectionist approach to neural network modelling that integrates dynamic gene networks within neurons with a neural network model. Interaction of genes in neurons affects the dynamics of the whole neural network. Through tuning the gene interaction network and the initial gene/protein expression values, different states of the neural network operation can be achieved. A generic computational neurogenetic model is introduced that implements this approach. It is illustrated by means of a simple neurogenetic model of a spiking neural network (SNN). Functioning of the SNN can be evaluated for instance by the field potentials, thus making it possible to attempt modelling the role of genes in different brain states such as epilepsy, schizophrenia, and other states, where EEG data is available to test the model predictions