Nanodiamonds-induced effects on neuronal firing of mouse hippocampal microcircuits

Fluorescent nanodiamonds (FND) are carbon-based nanomaterials that can efficiently incorporate optically active photoluminescent centers such as the nitrogen-vacancy complex, thus making them promising candidates as optical biolabels and drug-delivery agents. FNDs exhibit bright fluorescence without photobleaching combined with high uptake rate and low cytotoxicity. Focusing on FNDs interference with neuronal function, here we examined their effect on cultured hippocampal neurons, monitoring the whole network development as well as the electrophysiological properties of single neurons. We observed that FNDs drastically decreased the frequency of inhibitory (from 1.81 Hz to 0.86 Hz) and excitatory (from 1.61 Hz to 0.68 Hz) miniature postsynaptic currents, and consistently reduced action potential (AP) firing frequency (by 36%), as measured by microelectrode arrays. On the contrary, bursts synchronization was preserved, as well as the amplitude of spontaneous inhibitory and excitatory events. Current-clamp recordings revealed that the ratio of neurons responding with AP trains of high-frequency (fast-spiking) versus neurons responding with trains of low-frequency (slow-spiking) was unaltered, suggesting that FNDs exerted a comparable action on neuronal subpopulations. At the single cell level, rapid onset of the somatic AP ("kink") was drastically reduced in FND-treated neurons, suggesting a reduced contribution of axonal and dendritic components while preserving neuronal excitability.Comment: 34 pages, 9 figure

Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses

Dendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. Through carefully chosen parameter fits, solvable in the least-squares sense, we obtain accurate reduced compartmental models at any level of complexity. We show that (back-propagating) action potentials, Ca2+ spikes, and N-methyl-D-aspartate spikes can all be reproduced with few compartments. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input resistance between the ablated branches and the next proximal dendrite. Furthermore, our methodology fits reduced models directly from experimental data, without requiring morphological reconstructions. We provide software that automatizes the simplification, eliminating a common hurdle toward including dendritic computations in network models

The Neural Computations of Spatial Memory from Single Cells to Networks

Studies of spatial memory provide valuable insight into more general mnemonic functions, for by observing the activity of cells such as place cells, one can follow a subject’s dynamic representation of a changing environment. I investigate how place cells resolve conflicting neuronal input signals by developing computational models that integrate synaptic inputs on two scales. First, I construct reduced models of morphologically accurate neurons that preserve neuronal structure and the spatial specificity of inputs. Second, I use a parallel implementation to examine the dynamics among a network of interconnected place cells. Both models elucidate possible roles for the inputs and mechanisms involved in spatial memory

Automated longitudinal monitoring of in vivo protein aggregation in neurodegenerative disease C. elegans models

Background: While many biological studies can be performed on cell-based systems, the investigation of molecular pathways related to complex human dysfunctions - e.g. neurodegenerative diseases - often requires long-term studies in animal models. The nematode Caenorhabditis elegans represents one of the best model organisms for many of these tests and, therefore, versatile and automated systems for accurate time-resolved analyses on C. elegans are becoming highly desirable tools in the field. Results: We describe a new multi-functional platform for C. elegans analytical research, enabling automated worm isolation and culture, reversible worm immobilization and long-term high-resolution imaging, and this under active control of the main culture parameters, including temperature. We employ our platform for in vivo observation of biomolecules and automated analysis of protein aggregation in a C. elegans model for amyotrophic lateral sclerosis (ALS). Our device allows monitoring the growth rate and development of each worm, at single animal resolution, within a matrix of microfluidic chambers. We demonstrate the progression of individual protein aggregates, i.e. mutated human superoxide dismutase 1 - Yellow Fluorescent Protein (SOD1-YFP) fusion proteins in the body wall muscles, for each worm and over several days. Moreover, by combining reversible worm immobilization and on-chip high-resolution imaging, our method allows precisely localizing the expression of biomolecules within the worms' tissues, as well as monitoring the evolution of single aggregates over consecutive days at the sub-cellular level. We also show the suitability of our system for protein aggregation monitoring in a C. elegans Huntington disease (HD) model, and demonstrate the system's ability to study long-term doxycycline treatment-linked modification of protein aggregation profiles in the ALS model. Conclusion: Our microfluidic-based method allows analyzing in vivo the long-term dynamics of protein aggregation phenomena in C. elegans at unprecedented resolution. Pharmacological screenings on neurodegenerative disease C. elegans models may strongly benefit from this method in the near future, because of its full automation and high-throughput potential

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page

Model reduction of large spiking neurons

This thesis introduces and applies model reduction techniques to problems associated with simulation of realistic single neurons. Neurons have complicated dendritic structures and spatially-distributed ionic kinetics that give rise to highly nonlinear dynamics. However, existing model reduction methods compromise the geometry, and thus sacrifice the original input-output relationship. I demonstrate that linear and nonlinear model reduction techniques yield systems that capture the salient dynamics of morphologically accurate neuronal models and preserve the input-output maps while using significantly fewer variables than the full systems. Two main dynamic regimes characterize the voltage response of a neuron, and I demonstrate that different model reduction techniques are well-suited to each regime. Small perturbations from the neuron's rest state fall into the subthreshold regime, which can be accurately described by a linear system. By applying Balanced Truncation (BT), a model reduction technique for general linear systems, I recover subthreshold voltage dynamics, and I provide an efficient Iterative Rational Krylov Algorithm (IRKA), which makes large problems of interest tractable. However, these approximations are not valid once the input to the neuron is sufficient to drive the voltage into the spiking regime, which is characterized by highly nonlinear behavior. To reproduce spiking dynamics, I use a proper orthogonal decomposition (POD) to reduce the number of state variables and a discrete empirical interpolation method (DEIM) to reduce the complexity of the nonlinear terms. The techniques described above are successful, but they inherently assume that the whole neuron is either passive (linear) or active (nonlinear). However, in realistic cells the voltage response at distal locations is nearly linear, while at proximal locations it is very nonlinear. With this observation, I fuse the aforementioned models together to create a reduced coupled model in which each reduction technique is used where it is most advantageous, thereby making it possible to more accurately simulate a larger class of cortical neurons