Catálogo de publicaciones - libros

We present a model for the ontogenesis of information routing architectures in the brain based on chemical markers guiding axon growth. The model produces all-to-all connectivity between given populations of input and output nodes using a minimum of cortical resources (links and intermediate nodes). The resulting structures are similar to architectures proposed in the literature, but with interesting qualitative differences making them biologically more plausible.

It is well documented that superior colliculus (SC) neurons integrate stimuli of different modalities (e.g., visual and auditory). In this work, a mathematical model of the integrative response of SC neurons is presented, to gain a deeper insight into the possible mechanisms implicated. The model includes two unimodal areas (auditory and visual, respectively) sending information to a third area (in the SC) responsible for multisensory integration. Each neuron is represented via a sigmoidal relationship and a first-order dynamic. Neurons in the same area interact via lateral synapses. Simulations show that the model can mimic various responses to different combinations of stimuli: i) an increase in the neuron response in presence of multisensory stimulation, ii) the inverse effectiveness principle; iii) the existence of within- and cross-modality suppression between spatially disparate stimuli. The model suggests that non linearities in neural responses and synaptic connections can explain several aspects of multisensory integration.

Neurotransmitter fields differ from neural fields in the underlying principle that the state variables are not the neuron action potentials, but the chemical concentration of neurotransmitters in the extracellular space. The dendritic arbor of a new electro-chemical neuron model performs a computation on the surrounding field of neurotransmitters. These fields may represent quantities such as position, force, momentum, or energy. Any computation performed by a neural network has a direct analog to a neurotransmitter field computation. While models that use action potentials as state variables may form associations using matrix operations on a large vector of neuron outputs, the neurotransmitter state model makes it possible for a small number of neurons, even a single neuron, to establish an association between an arbitrary pattern in the input field and an arbitrary output pattern. A single layer of neurons, in effect, performs the computation of a two-layer neural network.

Motivated by a better understanding of cerebral information processing, a lot of work has been done recently in bringing together connectionist numerical models and symbolic cognitive frameworks, allowing for a better modelling of some cerebral mechanisms. However, a gap still exists between models that describe functionally small neural populations and cognitive architectures that are used to predict cerebral activity. The model presented here tries to fill partly this gap. It uses existing knowledge of the brain structure to describe neuroimaging data in terms of interacting functional units. Its merits rely on an explicit handling of neural populations proximity in the brain, relating it to similarity between the pieces of information processed.

We investigate human motor learning in an unknown environment using a force measurement as the input to a computer controlled plant. We propose to use the Feedback Error Learning (FEL) framework to model the overt behavior of motor response to unexpected changes in plant parameters. This framework assumes a specific feedforward and feedback structure. The feedforward component predicts the required motor commands given the reference trajectory, and the feedback component stabilizes the system in case of imprecise estimates and initial conditions. To estimate the feedback gain, we employ a novel technique in which we probe the stability properties of the system by artificially inducing a time delay in the sensory feedback pathway. By altering the pole location of the plant during a sinusoidal tracking task, a feedforward learning bandwidth was computed for each subject which measures the ability to adaptively track time-varying changes in the plant dynamics. Lastly, we use the learning bandwidth to compute a learning rate with respect to the FEL model. This learning rate reflects the ability of the subjects’ internal model to adapt to changes in an unknown environment.

Many episodes are acquired in the hippocampus. An episode is expressed by a sequence of elements that are perceived in an event. Episodes are associated each other by events that contain information shared among the episodes. Sequences must be recalled individually, even if the sequences are overlapped at some representations. Therefore, sequence disambiguation is an essential function to dissociate overlapped sequences. In this study, we especially focus on the location-dependencies of the STDP effects on synaptic summation and the expression of AMPA receptor. We firstly show that the hippocampal CA3 is divided into two regions in which one region has spatial selectivity and the other has temporal selectivity. Moreover, we confirm that the divided CA3 could generate a code for sequence disambiguation in computer simulations. Consequently, we suggest that the CA3 can be divided into two regions characterized by their selectivity, and the divided CA3 contributes to sequence disambiguation.

Lateral and elastic interactions are known to build a topology in different systems. We demonstrate how the models with weak lateral interactions can be reduced to the models with corresponding weak elastic interactions. Namely, the batch version of soft topology-preserving map can be rigorously reduced to the elastic net. Owing to the latter, both models produce similar behaviour when applied to the TSP. Unlike, the incremental (online) version of soft topology-preserving map is reduced to the cortical map only in the limit of low temperature, which makes their behaviours different when applied to the ocular dominance formation.

The objective of this paper is to introduce briefly the various soft computing methodologies and to present various applications in medicine. The scope is to demonstrate the possibilities of applying soft computing to medicine related problems. The recent published knowledge about use of soft computing in medicine is observed from the literature surveyed and reviewed. This study detects which methodology or methodologies of soft computing are used frequently together to solve the special problems of medicine. According to database searches, the rates of preference of soft computing methodologies in medicine are found as 70% of fuzzy logic-neural networks, 27% of neural networks-genetic algorithms and 3% of fuzzy logic-genetic algorithms in our study results. So far, fuzzy logic-neural networks methodology was significantly used in clinical science of medicine. On the other hand neural networks-genetic algorithms and fuzzy logic-genetic algorithms methodologies were mostly preferred by basic science of medicine. The study showed that there is undeniable interest in studying soft computing methodologies in genetics, physiology, radiology, cardiology, and neurology disciplines.

Exploratory matrix factorization methods like ICA and LNMF are applied to identify marker genes and classify gene expression data sets into different categories for diagnostic purposes or group genes into functional categories for further investigation of related regulatory pathways. Gene expression levels of either human breast cancer (HBC) cell lines [5] mediating bone metastasis or cell lines from Niemann Pick C patients monitoring monocyte - macrophage differentiation are considered.

In todays bioinformatics, Mass spectrometry (MS) is the key technique for the identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to better understanding of spectrometry data and improved spectrum evaluation. We propose a neural network architecture of Local Linear Map (LLM)-type for peptide prototyping and learning locally tuned regression functions for peak intensity prediction in MALDI-TOF mass spectra. We obtain results comparable to those obtained by -Support Vector Regression and show how the LLM learning architecture provides a basis for peptide feature profiling and visualisation.