Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies

Quantum dynamics study of the hydrogen molecule confined in singlewalled carbon nanotubes

In the present work a full-dimensional study of the dynamics of a hydrogen molecule confined in a narrow Single-walled Carbon Nanotube (SWCNT) is performed by using the Multi-configurational Time-dependent Hartree approach. New insights on the coupling between the different degrees of freedom of the molecule during the diffusion along the nanotube are found and discussed

Effect of mould inoculation on formation of chunky graphite in heavy section spheroidal graphite cast iron parts

The manufacturing process of heavy section ductile iron castings is strongly influenced by the risk of graphite degeneration under slow cooling rates. Appearance of this kind of defect is commonly linked to significant reductions in the mechanical properties of large castings. Studies on the effect of inoculation on chunky graphite formation in heavy sections have led to contradictory results in the literature and this triggered the present work. New experimental data are presented on the effect of mould inoculation on chunky graphite appearance during solidification of nodular irons which clearly demonstrate that mould inoculation increases the risk of chunky graphite formation in heavy sections. This is in agreement with some previous works which are reviewed, and it is suggested that the contradiction with other results could relate to the fact that these latter works dealt with chill casting

Determination of the molecular diffusion coefficients in ternary mixtures by the sliding symmetric tubes technique

A new analytical methodology has been developed to determine the diagonal and cross-diagonal molecular diffusion coefficients in ternary mixtures by the Sliding Symmetric Tubes technique. The analytical solution is tested in binary mixtures obtaining good agreement with the results of the literature. Results are presented for the ternary mixture formed by tetralin, isobutylbenzene, and dodecane with an equal mass fraction for all the components (1–1–1) which is held at 25 °C. Diagonal and cross-diagonal coefficients are determined for the three possible orders of components, in order to compare the results with those available in the literature. A comparison with published results shows a good agreement for the eigenvalues of the diffusion matrix, and a reasonable agreement for the diagonal molecular diffusion coefficients

Quantum dynamics study of the hydrogen molecule confined in singlewalled carbon nanotubes

In the present work a full-dimensional study of the dynamics of a hydrogen molecule confined in a narrow Single-walled Carbon Nanotube (SWCNT) is performed by using the Multi-configurational Time-dependent Hartree approach. New insights on the coupling between the different degrees of freedom of the molecule during the diffusion along the nanotube are found and discussed

Denoising Autoencoders for fast Combinatorial Black Box Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.Comment: corrected typos and small inconsistencie

5D quantum dynamics of the H2@SWNT system: Quantitative study of the rotational-translational coupling

The dynamics of the dihydrogen molecule when confined in carbon nanotubes with different chiral- ities and diameters are studied by using a 5 dimensional model considering the most relevant degrees of freedom of the system. The nuclear eigenstates are calculated for an (8,0) and a (5,0) carbon nanotubes by the State-Average Multiconfigurational Time-dependent Hartree, and then studied using qualitative tools (mapping of the total wave functions onto given subspaces) and more rigorous analysis (different kinds of overlaps with reference functions). The qualitative analysis is seen to fail due to a strong coupling between the internal and translational degrees of freedom. Using more accurate tools allows us to gain a deeper insight into the behaviour of confined species

An island based hybrid evolutionary algorithm for optimization

This is a post-print version of the article - Copyright @ 2008 Springer-VerlagEvolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation of Distribution Algorithm (EDA). Within IHEA, an island model is designed to cooperatively search for the global optima in search space. By combining the strengths of the three component algorithms, IHEA greatly improves the optimization performance of the three basic algorithms. Experimental results demonstrate that IHEA outperforms all the three component algorithms on the test problems.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1