Latent variable models for neural data analysis

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis.

It is divided into two parts. The first begins with an exposition of the general techniques of latent variable modeling. A new, extremely general, optimization algorithm is proposed - called Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter values of arbitrary latent variable models. This algorithm appears to alleviate the common problem of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for model size selection; in combination with standard model selection techniques the quality of fits may be further improved, while the appropriate model size is automatically and efficiently determined. Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and approximate inference and learning algorithms are derived for it. This model is applied in the second part of the thesis.

The second part brings the technology of part I to bear on two important problems in experimental neuroscience. The first is known as spike sorting; this is the problem of separating the spikes from different neurons embedded within an extracellular recording. The dissertation offers the first thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. The second problem addressed is that of characterizing the distribution of spike trains recorded from the same neuron under identical experimental conditions. A latent variable model is proposed. Inference and learning in this model leads to new principled algorithms for smoothing and clustering of spike data.

Chain-forming zintl antimonides as novel thermoelectric materials

Zintl phases, a subset of intermetallic compounds characterized by covalently-bonded "sub-structures," surrounded by highly electropositive cations, exhibit precisely the characteristics desired for thermoelectric applications. The requirement that Zintl compounds satisfy the valence of anions through the formation of covalent substructures leads to many unique, complex crystal structures. Such complexity often leads to exceptionally low lattice thermal conductivity due to the containment of heat in low velocity optical modes in the phonon dispersion. To date, excellent thermoelectric properties have been demonstrated in several Zintl compounds. However, compared with the large number of known Zintl phases, very few have been investigated as thermoelectric materials.

From this pool of uninvestigated compounds, we selected a class of Zintl antimonides that share a common structural motif: anionic moieties resembling infinite chains of linked MSb₄ tetrahedra, where $M$ is a triel element. The compounds discussed in this thesis (A₅M₂Sb₆ and A₃MSb₃, where A = Ca or Sr and M = Al, Ga and In) crystallize as four distinct, but closely related "chain-forming" structure types. This thesis describes the thermoelectric characterization and optimization of these phases, and explores the influence of their chemistry and structure on the thermal and electronic transport properties. Due to their large unit cells, each compound exhibits exceptionally low lattice thermal conductivity (0.4 - 0.6 W/mK at 1000 K), approaching the predicted glassy minimum at high temperatures. A combination of Density Functional calculations and classical transport models were used to explain the experimentally observed electronic transport properties of each compound. Consistent with the Zintl electron counting formalism, A₅M₂Sb₆ and A₃MSb₃ phases were found to have filled valence bands and exhibit intrinsic electronic properties. Doping with divalent transition metals (Zn²⁺ and Mn²⁺) on the M³⁺ site, or Na¹⁺ on the A³⁺ site allowed for rational control of the carrier concentration and a transition towards degenerate semiconducting behavior. In optimally-doped samples, promising peak zT values between 0.4 and 0.9 were obtained, highlighting the value of continued investigations of complex Zintl phases.

The variations and climatic significance of D/H ratios in the carbon-bound hydrogen of cellulose in trees

The σD values of nitrated cellulose from a variety of trees covering a wide geographic range have been measured. These measurements have been used to ascertain which factors are likely to cause σD variations in cellulose C-H hydrogen.

It is found that a primary source of tree σD variation is the σD variation of the environmental precipitation. Superimposed on this are isotopic variations caused by the transpiration of the leaf water incorporated by the tree. The magnitude of this transpiration effect appears to be related to relative humidity.

Within a single tree, it is found that the hydrogen isotope variations which occur for a ring sequence in one radial direction may not be exactly the same as those which occur in a different direction. Such heterogeneities appear most likely to occur in trees with asymmetric ring patterns that contain reaction wood. In the absence of reaction wood such heterogeneities do not seem to occur. Thus, hydrogen isotope analyses of tree ring sequences should be performed on trees which do not contain reaction wood.

Comparisons of tree σD variations with variations in local climate are performed on two levels: spatial and temporal. It is found that the σD values of 20 North American trees from a wide geographic range are reasonably well-correlated with the corresponding average annual temperature. The correlation is similar to that observed for a comparison of the σD values of annual precipitation of 11 North American sites with annual temperature. However, it appears that this correlation is significantly disrupted by trees which grew on poorly drained sites such as those in stagnant marshes. Therefore, site selection may be important in choosing trees for climatic interpretation of σD values, although proper sites do not seem to be uncommon.

The measurement of σD values in 5-year samples from the tree ring sequences of 13 trees from 11 North American sites reveals a variety of relationships with local climate. As it was for the spatial σD vs climate comparison, site selection is also apparently important for temporal tree σD vs climate comparisons. Again, it seems that poorly-drained sites are to be avoided. For nine trees from different "well-behaved" sites, it was found that the local climatic variable best related to the σD variations was not the same for all sites.

Two of these trees showed a strong negative correlation with the amount of local summer precipitation. Consideration of factors likely to influence the isotopic composition of summer rain suggests that rainfall intensity may be important. The higher the intensity, the lower the σD value. Such an effect might explain the negative correlation of σD vs summer precipitation amount for these two trees. A third tree also exhibited a strong correlation with summer climate, but in this instance it was a positive correlation of σD with summer temperature.

The remaining six trees exhibited the best correlation between σD values and local annual climate. However, in none of these six cases was it annual temperature that was the most important variable. In fact annual temperature commonly showed no relationship at all with tree σD values. Instead, it was found that a simple mass balance model incorporating two basic assumptions yielded parameters which produced the best relationships with tree σD values. First, it was assumed that the σD values of these six trees reflected the σD values of annual precipitation incorporated by these trees. Second, it was assumed that the σD value of the annual precipitation was a weighted average of two seasonal isotopic components: summer and winter. Mass balance equations derived from these assumptions yielded combinations of variables that commonly showed a relationship with tree σD values where none had previously been discerned.

It was found for these "well-behaved" trees that not all sample intervals in a σD vs local climate plot fell along a well-defined trend. These departures from the local σD VS climate norm were defined as "anomalous". Some of these anomalous intervals were common to trees from different locales. When such widespread commonalty of an anomalous interval occurred, it was observed that the interval corresponded to an interval in which drought had existed in the North American Great Plains.

Consequently, there appears to be a combination of both local and large scale climatic information in the σD variations of tree cellulose C-H hydrogen.

Nonlinear Effects in Granular Crystals with Broken Periodicity

When studying physical systems, it is common to make approximations: the contact interaction is linear, the crystal is periodic, the variations occurs slowly, the mass of a particle is constant with velocity, or the position of a particle is exactly known are just a few examples. These approximations help us simplify complex systems to make them more comprehensible while still demonstrating interesting physics. But what happens when these assumptions break down? This question becomes particularly interesting in the materials science community in designing new materials structures with exotic properties In this thesis, we study the mechanical response and dynamics in granular crystals, in which the approximation of linearity and infinite size break down. The system is inherently finite, and contact interaction can be tuned to access different nonlinear regimes. When the assumptions of linearity and perfect periodicity are no longer valid, a host of interesting physical phenomena presents itself. The advantage of using a granular crystal is in its experimental feasibility and its similarity to many other materials systems. This allows us to both leverage past experience in the condensed matter physics and materials science communities while also presenting results with implications beyond the narrower granular physics community. In addition, we bring tools from the nonlinear systems community to study the dynamics in finite lattices, where there are inherently more degrees of freedom. This approach leads to the major contributions of this thesis in broken periodic systems. We demonstrate the first defect mode whose spatial profile can be tuned from highly localized to completely delocalized by simply tuning an external parameter. Using the sensitive dynamics near bifurcation points, we present a completely new approach to modifying the incremental stiffness of a lattice to arbitrary values. We show how using nonlinear defect modes, the incremental stiffness can be tuned to anywhere in the force-displacement relation. Other contributions include demonstrating nonlinear breakdown of mechanical filters as a result of finite size, and the presents of frequency attenuation bands in essentially nonlinear materials. We finish by presenting two new energy harvesting systems based on our experience with instabilities in weakly nonlinear systems.

I. Proton magnetic resonance of polynucleotides and transfer RNA. II. Electron spin relaxation studies of manganese (II) complexes in acetonitrile

Part I. Proton Magnetic Resonance of Polynucleotides and Transfer RNA.

Proton magnetic resonance was used to follow the temperature dependent intramolecular stacking of the bases in the polynucleotides of adenine and cytosine. Analysis of the results on the basis of a two state stacked-unstacked model yielded values of -4.5 kcal/mole and -9.5 kcal/mole for the enthalpies of stacking in polyadenylic and polycytidylic acid, respectively.

The interaction of purine with these molecules was also studied by pmr. Analysis of these results and the comparison of the thermal unstacking of polynucleotides and short chain nucleotides indicates that the bases contained in stacks within the long chain poly nucleotides are, on the average, closer together than the bases contained in stacks in the short chain nucleotides.

Temperature and purine studies were also carried out with an aqueous solution of formylmethionine transfer ribonucleic acid. Comparison of these results with the results of similar experiments with the homopolynucleotides of adenine, cytosine and uracil indicate that the purine is probably intercalating into loop regions of the molecule.

The solvent denaturation of phenylalanine transfer ribonucleic acid was followed by pmr. In a solvent mixture containing 83 volume per cent dimethylsulf oxide and 17 per cent deuterium oxide, the tRNA molecule is rendered quite flexible. It is possible to resolve resonances of protons on the common bases and on certain modified bases.

Part II. Electron Spin Relaxation Studies of Manganese (II) Complexes in Acetonitrile.

The electron paramagnetic resonance spectra of three Mn⁺² complexes, [Mn(CH₃CN)₆]⁺², [MnCl₄]^-2, and [MnBr₄]^-2, in acetonitrile were studied in detail. The objective of this study was to relate changes in the effective spin Hamiltonian parameters and the resonance line widths to the structure of these molecular complexes as well as to dynamical processes in solution.

Of the three systems studied, the results obtained from the [Mn(CH₃CN)₆]⁺² system were the most straight-forward to interpret. Resonance broadening attributable to manganese spin-spin dipolar interactions was observed as the manganese concentration was increased.

In the [MnCl₄]^-2 system, solvent fluctuations and dynamical ion-pairing appear to be significant in determining electron spin relaxation.

In the [MnBr₄]^-2 system, solvent fluctuations, ion-pairing, and Br- ligand exchange provide the principal means of electron spin relaxation. It was also found that the spin relaxation in this system is dependent upon the field strength and is directly related to the manganese concentration. A relaxation theory based on a two state collisional model was developed to account for the observed behavior.