A Simple Cell Model with Multiple Spatial Frequency Selectivity and Linear/Non-Linear Response Properties

A model is described for cortical simple cells. Simple cells are selective for local contrast polarity, signaling light-dark and dark-light transitions. The proposed new architecture exhibits both linear and non-linear properties of simple cells. Linear responses are obtained by integration of the input stimulus within subfields of the cells, and by combinations of them. Non-linear behavior can be seen in the selectivity for certain features that can be characterized by the spatial arrangement of activations generated by initial on- and off-cells (center-surround). The new model also exhibits spatial frequency selectivity with the generation of multi-scale properties being based on a single-scale band-pass input that is generated by the initial (retinal) center-surround processing stage.German BMFT grant (413-5839-01 IN 101 C/1); CNPq and NUTES/UFRJ, Brazi

Inertial Load Compensation by a Model Spinal Circuit During Single Joint Movement

Office of Naval Research (N00014-92-J-1309); CONACYT (Mexico) (63462

CMOS realization of a quantized-output classifier circuit

In this paper a CMOS implementation of a multi-input data classifier with several output levels and a different architecture is presented. The proposed circuit operates in current-mode and can classify several types of analog vector data. The classifier circuit’s new architecture consists of the interconnections of core cells each possessing a current-voltage converter, an inverter followed by a NOR gate and a voltage-current output stage. Using 0.35µm TSMC technology parameters, SPICE simulation results for a classifier with two inputs are included to verify the expected results

Learning, Generalization, and Functional Entropy in Random Automata Networks

It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that feedforward Boolean networks can learn to perform specific simple tasks and generalize well if only a subset of the learning examples is provided for learning. Here, we extend this body of work and show experimentally that random Boolean networks (RBNs), where both the interconnections and the Boolean transfer functions are chosen at random initially, can be evolved by using a state-topology evolution to solve simple tasks. We measure the learning and generalization performance, investigate the influence of the average node connectivity $K$, the system size $N$, and introduce a new measure that allows to better describe the network's learning and generalization behavior. We show that the connectivity of the maximum entropy networks scales as a power-law of the system size $N$. Our results show that networks with higher average connectivity $K$ (supercritical) achieve higher memorization and partial generalization. However, near critical connectivity, the networks show a higher perfect generalization on the even-odd task

SIRENA: A CAD environment for behavioural modelling and simulation of VLSI cellular neural network chips

This paper presents SIRENA, a CAD environment for the simulation and modelling of mixed-signal VLSI parallel processing chips based on cellular neural networks. SIRENA includes capabilities for: (a) the description of nominal and non-ideal operation of CNN analogue circuitry at the behavioural level; (b) performing realistic simulations of the transient evolution of physical CNNs including deviations due to second-order effects of the hardware; and, (c) evaluating sensitivity figures, and realize noise and Monte Carlo simulations in the time domain. These capabilities portray SIRENA as better suited for CNN chip development than algorithmic simulation packages (such as OpenSimulator, Sesame) or conventional neural networks simulators (RCS, GENESIS, SFINX), which are not oriented to the evaluation of hardware non-idealities. As compared to conventional electrical simulators (such as HSPICE or ELDO-FAS), SIRENA provides easier modelling of the hardware parasitics, a significant reduction in computation time, and similar accuracy levels. Consequently, iteration during the design procedure becomes possible, supporting decision making regarding design strategies and dimensioning. SIRENA has been developed using object-oriented programming techniques in C, and currently runs under the UNIX operating system and X-Windows framework. It employs a dedicated high-level hardware description language: DECEL, fitted to the description of non-idealities arising in CNN hardware. This language has been developed aiming generality, in the sense of making no restrictions on the network models that can be implemented. SIRENA is highly modular and composed of independent tools. This simplifies future expansions and improvements.Comisión Interministerial de Ciencia y Tecnología TIC96-1392-C02-0

The level and distribution of the GABA(B)R1 AND GABA(B)R2 receptor subunits in the rat\u27s central auditory system

The GABAB receptor is important for the function of neurons in the central auditory system. A functional GABAB receptor is a heterodimer of the GABABR1 and GABABR2 subunits. In this thesis, I used immunohistochemical methods to examine the level and localisation of both subunits in the rat\u27s central auditory system. Results revealed that GABABR1 and GABABR2 subunits were expressed throughout the auditory system. High levels of immunoreactivity to both subunits were found in the superficial layers of the auditory cortex, medial geniculate nucleus, dorsal region of the inferior colliculus, and dorsal cochlear nucleus. While the expression of these subunits was generally parallel with each other, some differences were observed between the two subunits. Overall, distributions of the GABABR1 and GABABR2 subunits in auditory structures are consistent with inputs to these structures. The localisation of the subunits supports the contribution of functional GABAB receptors that are likely mediating auditory connections

Microstructure Imaging in the Human Brain with Advanced Diffusion MRI and Machine Learning

Today, a plethora of model-based diffusion MRI (dMRI) techniques exist that aim to provide quantitative metrics of cellular-scale tissue properties. In the brain, many of these techniques focus on cylindrical projections such as axons and dendrites. Capturing additional tissue features is challenging, as conventional dMRI measurements have limited sensitivity to different cellular components, and modelling cellular architecture is not trivial in heterogeneous tissues such as grey matter. Additionally, fitting complex non-linear models with traditional techniques can be time-consuming and prone to local minima, which hampers their widespread use. In this thesis, we harness recent advances in measurement technology and modelling efforts to tackle these challenges. We probe the utility of B-tensor encoding, a technique that offers additional sensitivity to tissue microstructure compared to conventional measurements, and observe that B-tensor encoding provides unique contrast in grey matter. Motivated by this and recent work showing that the diffusion signature of soma in grey matter may be captured with spherical compartments, we use B-tensor encoding measurements and a biophysical model to disentangle spherical and cylindrical cellular structures. We map apparent markers of these geometries in healthy human subjects and evaluate the extent to which they may be interpreted as correlates of soma and projections. To ensure fast and robust model fitting, we use supervised machine learning (ML) to estimate parameters. We explore limitations in ML fitting in several microstructure models, including the model developed here, and demonstrate that the choice of training data significantly impacts estimation performance. We highlight that high precision obtained using ML may mask strong biases and that visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates. We believe that the methods developed in this work provide new insight into the reliability and potential utility of advanced dMRI and ML in microstructure imaging

Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%

ASD: çok amaçlı ayarlanabilir sınıflandırıcı devreler

Göknar, İzzet Cem (Dogus Author) -- Minaei, Shahram (Dogus Author) -- Yıldız, Merih (Dogus Author)Çalışmada, ayarlanabilir sınıflandırıcı devreleri ve uygulama alanları incelenmiştir. AMS 0.35 μm CMOS prosesi ile, tasarlanan sınıflandırıcı bir tümdevrenin üretimi de yapılmıştır. Bu sınıflandırıcı devresinin kontrol parametrelerinin bulunmasını sağlayan öğrenme algoritmaları çeşitli uygulamalar için geliştirilmiştir. Sınıflandırma işlemleri geliştirilen algoritmalar ve üretilen devre ile İris ve Haberman veri kümelerine uygulanarak sonuçların uyum içinde olduğu gösterilmiştir.TÜBİTA