3 resultados para probabilistic radial basis neural networks
em Brock University, Canada
Resumo:
The freshwater mollusc Lymnaea stagnalis was utilized in this study to further the understanding of how network properties change as a result of associative learning, and to determine whether or not this plasticity is dependent on previous experience during development. The respiratory and neural correlates of operant conditioning were first determined in normally reared Lymnaea. The same procedure was then applied to differentially reared Lymnaea, that is, animals that had never experienced aerial respiration during their development. The aim was to determine whether these animals would demonstrate the same responses to the training paradigm. In normally reared animals, a behavioural reduction in aerial respiration was accompanied by numerous changes within the neural network. Specifically, I provide evidence of changes at the level of the respiratory central pattern generator and the motor output. In the differentially reared animals, there was little behavioural data to suggest learning and memory. There were, however, significant differences in the network parameters, similar to those observed in normally reared animals. This demonstrated an effect of operant conditioning on differentially reared animals. In this thesis, I have identified additional correlates of operant conditioning in normally reared animals and provide evidence of associative learning in differentially reared animals. I conclude plasticity is not dependent on previous experience, but is rather ontogenetically programmed within the neural network.
Resumo:
Objectlve:--This study examined the intraclass reliability· of different measures of the
excitability of the Hoffmann reflex, derived from stimulus-response curves. The slope of the
regression line of the H-reflex stimulus-response curve advocated by Funase et al. (1994) was
also compared to the peak of the first derivative of the H-reflex stimulus-response curve
(dHIdVmax), a new measure introduced in this investigation. A secondary purpose was to explore
the possibility of mood as a covariate when measuring excitability of the H-reflex arc.
Methods: The H-reflex amplitude at a stimulus intensity corresponding to 5% of the
maximum M-wave (Mmax) is an established measure that was used as an additional basis of
comparison. The H-reflex was elicited in the soleus for 24 subjects (12 males and 12 females)
on five separate days. Vibration was applied to the Achilles tendon prior to stimulation to test
the sensitivity of the measures on test day four. The means of five evoked potentials at each
gradually increasing intensity, from below H-reflex threshold to above Mmax, were used to create
both the H-reflex and M-wave stimulus response curves for each subject across test days. The
mood of the subjects was assessed using the Subjective Exercise Experience Scale (SEES) prior
to the stimulation protocol each day.
Results: There was a modest decrease in all H-reflex measures from the first to third test day,
but it was non-significant (P's>0.05). All measures of the H-reflex exhibited a profound
reduction following vibration on test day four, and then returned to baseline levels on test day
five (P's<0.05). The intraclass correlation coefficient (ICC) for H-reflex amplitude at 5% of
Mmax was 0.85. The ICC for the slope of the regression line was 0.79 while it was 0.89 for
dH/dVmax. Maximum M-wave amplitude had an ICC of 0.96 attesting to careful methodological
controls. The SEES subscales of fatigue and psychological well-being remained unchanged
IV
across the five days. The psychological distress subscale (P
Resumo:
The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.