Operant Discriminative Learning and Evidence of Subtelencephalic Plastic Changes After Long-Term Detelencephalation in Pigeons

We analyzed operant discrimination in detelencephalated pigeons and neuroanatomical substrates after long-term detelencephalation. In Experiment I, experimental pigeons with massive telencephalic ablation and control pigeons were conditioned to key peck for food. Successive discrimination was made under alternating red (variable-ratio reinforcement) and yellow (extinction) lights in one key of the chamber. These relations were interchanged during reversal discrimination. The sessions were run until steady-state rates were achieved. Experiment II analyzed the morphology of the nucleus rotundus and optic tectum in long-term detelencephalated and control birds, using a Klüver-Barrera staining and image analyzer system. Detelencephalated birds had more training sessions for response shaping and steady-state behavior (p<0.001), higher red key peck rates during discrimination (p<0.01), and reversal discrimination indexes around 0.50. Morphometric analysis revealed a decreased number of neurons and increased vascularity, associated with increases in the perimeter (p<0.001) in the nucleus rotundus. In the optic tectum, increases in the perimeter (p<0.05) associated with disorganization in the layers arrangement were seen. The data indicate that telencephalic systems might have an essential function in reversal operant discrimination learning. The structural characteristics of subtelencephalic systems after long-term detelencephalation evidence plastic changes that might be related to functional mechanisms of learning and neural plasticity in pigeons

Guia para publicação de trabalhos apresentados em cursos da Embrapa Gado de Corte.

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An Iterative Wavelet Threshold for Signal Denoising

This paper introduces an adaptive filtering process based on shrinking wavelet coefficients from the corresponding signal wavelet representation. The filtering procedure considers a threshold method determined by an iterative algorithm inspired by the control charts application, which is a tool of the statistical process control (SPC). The proposed method, called SpcShrink, is able to discriminate wavelet coefficients that significantly represent the signal of interest. The SpcShrink is algorithmically presented and numerically evaluated according to Monte Carlo simulations. Two empirical applications to real biomedical data filtering are also included and discussed. The SpcShrink shows superior performance when compared with competing algorithms.Comment: 19 pages, 10 figures, 2 table