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| Sá,A.L. de; Bahia,C.P.; Correa,V.C.; Dias,I.A.; Batista,C.; Gomes-Leal,W.; Pinho,A.L.S.; Houzel,J.C.; Picanço-Diniz,C.W.; Pereira,A.. |
| We used biotinylated dextran amine (BDA) to anterogradely label individual axons projecting from primary somatosensory cortex (S1) to four different cortical areas in rats. A major goal was to determine whether axon terminals in these target areas shared morphometric similarities based on the shape of individual terminal arbors and the density of two bouton types: en passant (Bp) and terminaux (Bt). Evidence from tridimensional reconstructions of isolated axon terminal fragments (n=111) did support a degree of morphological heterogeneity establishing two broad groups of axon terminals. Morphological parameters associated with the complexity of terminal arbors and the proportion of beaded Bp vs stalked Bt were found to differ significantly in these two... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Somatosensory cortex; Barrel field; Feedforward networks; Axon terminal; Axon morphometry. |
| Ano: 2016 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2016000600601 |
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| Joerding, Wayne H.; Li, Ying; Young, Douglas L.. |
| Feedforward networks have powerful approximation capabilities without the "explosion of parameters" problem faced by Fourier and polynomial expansions. This paper first introduces feedforward networks and describes their approximation capabilities, then we address several practical issues faced by applications of feedforward networks. First, we demonstrate networks can provide a reasonable estimate of a Bermudagrass hay fertilizer response function with the relatively sparse data often available from experiments. Second, we demonstrate that the estimated network with a practical number of hidden units provides reasonable flexibility. Third, we show how one can constrain feedforward networks to satisfy a priori information without losing their flexible... |
| Tipo: Journal Article |
Palavras-chave: Biological process models; Feedforward networks; Production function; Neural networks; Research Methods/ Statistical Methods. |
| Ano: 1994 |
URL: http://purl.umn.edu/15430 |
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