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PubMed2006

Cyborgs and moral identity.

Neuroscience and technological medicine in general increasingly faces us with the imminent reality of cyborgs-integrated part human and part machine complexes. If my brain functions in a way that is supported by and exploits intelligent technology both external and implantable, then how should I be treated and what is my moral status-am I a machine or am I a person? I explore a number of scenarios where the balance between human and humanoid machine shifts, and ask questions about the moral status of the individuals concerned. The position taken is very much in accordance with the Aristotelian idea that our moral behaviour is of a piece with our social and personal skills and forms a reactive and reflective component of those skills.

Anencephaly↗
PubMed2003

Neural network training with global optimization techniques.

This paper presents an approach of using Simulated Annealing and Tabu Search for the simultaneous optimization of neural network architectures and weights. The problem considered is the odor recognition in an artificial nose. Both methods have produced networks with high classification performance and low complexity. Generalization has been improved by using the backpropagation algorithm for fine tuning. The combination of simple and traditional search methods has shown to be very suitable for generating compact and efficient networks.

Algorithms↗
PubMed1996

Analytical interpretation of feed-forward nets outputs after training.

The minimization quadratic error criterion which gives rise to the back-propagation algorithm is studied using functional analysis techniques. With them, we recover easily the well-known statistical result which states that the searched global minimum is a function which assigns, to each input pattern, the expected value of its corresponding output patterns. Its application to classification tasks shows that only certain output class representations can be used to obtain the optimal Bayesian decision rule. Finally, our method permits the study of other error criterions, finding out, for instance, that absolute value errors lead to medians instead of mean values.

Algorithms↗
PubMed1996

Hardware prototypes of a Boolean neural network and the simulated annealing optimization method.

Boolean Neural Network is a neural network that operates with binary weight values of "1" and "0". Otherwise it is formally analogous to the Multilayer Perceptron (MLP). Simulated Annealing is a stochastic optimization methods that is suitable for performing nonlinear multivariable optimization tasks. Training a Boolean Neural Network is a well-suited problem to this algorithm. However, the Simulated Annealing method is computationally heavy, which makes the training procedure slow. The training speed can be improved by using custom designed hardware for the whole system including the optimization method and the neural network. Hardware prototypes of a Boolean Neural Network and the Simulated Annealing optimization method have been designed using discrete components. The Boolean Neural Network implementation is basically a dynamically configurable feedforward network of Boolean logic gates of two inputs. The Simulated Annealing implementation is a general purpose hardware tool for multivariable optimization tasks. Here it is applied to do supervised training of the Boolean Neural Network hardware.

Algorithms↗
PubMed1996

Constructive training methods for feedforward neural networks with binary weights.

Quantization of the parameters of a Perceptron is a central problem in hardware implementation of neural networks using a numerical technology. A neural model with each weight limited to a small integer range will require little surface of silicon. Moreover, according to Occam's razor principle, better generalization abilities can be expected from a simpler computational model. The price to pay for these benefits lies in the difficulty to train these kind of networks. This paper proposes essentially two new ideas for constructive training algorithms, and demonstrates their efficiency for the generation of feedforward networks composed of Boolean threshold gates with discrete weights. A proof of the convergence of these algorithms is given. Some numerical experiments have been carried out and the results are presented in terms of the size of the generated networks and of their generalization abilities.

Algorithms↗
PubMed1996

Improvement of projection-type associative memories by using a two-phase recalling procedure.

In this letter, we propose a Two-Phase Recalling Procedure (TPRP) to improve the recall capability of the projection-type associative memory. It is known that the conventional projection dynamic sometimes produces spurious states that do not belong to the space spanned by the prototype vectors (memory space). The proposed TPRP provides a trapped spurious state another chance to project onto the memory space such that the recall capability of the projection-type associative memories can be greatly improved. Finally a simulation result demonstrates the effectiveness of the TPRP.

Computer Simulation↗
PubMed1996

Nonlinear time series analysis by neural networks: a case study.

This paper presents a neural network approach to time-series analysis of a univariate nonlinear system. Feedforward networks are studied, and an appropriate network size is determined by different criteria computed on the basis of the performance of the models on the training and test sets. The analysis and conclusions drawn are supported by studies of the phase portraits of the models. By a proper choice of network size, the problems of over-parameterization are demonstrated to be avoided. The overfitting observed for larger networks is analyzed and the underlying reasons for their worse generalization capabilities are explained. Finally, some observations are made on the approximation provided by an oversized network with weights determined by an incomplete (interrupted) training and that of the optimal-sized network.

Cybernetics↗
PubMed1996

Robust feedforward processing in synfire chains.

The rapidity of time-constrained visual identification suggests a feedforward process in which neural activity is propagated through a number of cortical stages. The process is modeled by using a synfire chain, leading to a neural-network model which involves propagating activation waves through a sequence of layers. Theory and analysis of the model's behavior, especially in the presence of noise, predict enhancement of wave propagation for a range of noise intensities. Simulation studies confirm this prediction. The results are discussed in terms of (spatio-temporal) stochastic resonance. It is concluded that feedforward processes such as time-constrained visual identification may benefit from moderate levels of noise.

Algorithms↗
PubMed1974

[Pupillography].

Explore the source record for details and available documents.

Accommodation, Ocular↗
PubMed1995

The target switch algorithm: a constructive learning procedure for feed-forward neural networks.

We propose an efficient procedure for constructing and training a feed-forward neural network. The network can perform binary classification for binary or analogue input data. We show that the procedure can also be used to construct feedforward neural networks with binary-valued weights. Neural networks with binary-valued weights are potentially straightforward to implement using microelectronic or optical devices and they can also exhibit good generalization.

Algorithms↗
PubMed1989

Oscillations of attachment in borderline personality disorder.

The authors propose that oscillations of attachment in borderline personality disorder stem from a central problem with regulation of interpersonal distance. This problem derives from borderline patients' conflicts between fears of abandonment and domination. When they move closer to others, they fear that they will be dominated; when they move away, they fear that they will be abandoned. Whichever direction they move, they experience negative feedback. This gives rise to recurrent oscillations between attachment to and detachment from others. Because the oscillations are reinforced by the ambivalent reactions of significant others and the involvement of third parties, family therapy is often indicated.

Borderline Personality Disorder↗