Andrew Sornborger

• PhD Theoretical Physics
• Researcher at Los Alamos National Laboratory

Spiking Neural Network (SNN) control systems have demonstrated advantages over conventional Artificial Neural Networks (ANNs) in energy efficiency and data paucity. In this study, we introduce a SNN-based controller designed within the Neural Engineering Framework (NEF) for the stabilization and trajectory tracking of a quad rotorcraft Unmanned Air...

Self-learning control techniques mimicking the functionality of the limbic system in the mammalian brain have shown advantages in terms of superior learning ability and low computational cost. However, accompanying stability analyses and mathematical proofs rely on unrealistic assumptions which limit not only the performance, but also the implement...

We introduce a performance-guaranteed Limbic System-Inspired Control (LISIC) which is appropriate for implementation in neuromorphic hardware. The control strategy aims to stabilize the tracking error of a class of nonlinear systems with uncertain dynamics and external perturbations. The objective of the LISIC structure is to identify and compensat...

Currently available quantum hardware allows for small-scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a two-qubit unitary by a sequence of three parameteri...

There is great interest in using near-term quantum computers to simulate and study foundational problems in quantum mechanics and quantum information science, such as the scrambling measured by an out-of-time-ordered correlator (OTOC). Here we use an IBM Q processor, quantum error mitigation, and weaved Trotter simulation to study high-resolution o...

The capabilities of natural neural systems have inspired new generations of machine learning algorithms as well as neuromorphic very large-scale integrated (VLSI) circuits capable of fast, low-power information processing. However, most modern machine learning algorithms are not neurophysiologically plausible and thus are not directly implementable...

Scrambling processes, which rapidly spread entanglement through many-body quantum systems, are difficult to investigate using standard techniques, but are relevant to quantum chaos and thermalization. In this Letter, we ask if quantum machine learning (QML) could be used to investigate such processes. We prove a no-go theorem for learning an unknow...

Recent progress in quantum machine learning has shown that the number of input-output state pairs needed to learn an unknown unitary can be exponentially smaller when entanglement is used as a resource. Here, we study quantum machine learning in the context of continuous variable (CV) systems and demonstrate that similar entanglement-enhanced resou...

Currently available quantum hardware allows for small scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a two-qubit unitary by a sequence of three parameteri...

Non-von Neumann computational hardware, based on neuron-inspired, non-linear elements connected via linear, weighted synapses -- so-called neuromorphic systems -- is a viable computational substrate. Since neuromorphic systems have been shown to use less power than CPUs for many applications, they are of potential use in autonomous systems such as...

Moderate-size quantum computers are now publicly accessible over the cloud, opening the exciting possibility of performing dynamical simulations of quantum systems. However, while rapidly improving, these devices have short coherence times, limiting the depth of algorithms that may be successfully implemented. Here we demonstrate that, despite thes...

Quantum neural networks (QNNs) have generated excitement around the possibility of efficiently analyzing quantum data. But this excitement has been tempered by the existence of exponentially vanishing gradients, known as barren plateau landscapes, for many QNN architectures. Recently, Quantum Convolutional Neural Networks (QCNNs) have been proposed...

Scrambling, the rapid spread of entanglement and information through many-body quantum systems, is of fundamental importance to thermalization and quantum chaos but can be challenging to investigate using standard techniques. Recently, quantum machine learning (QML) has emerged as a promising platform to study complex quantum processes. This prompt...

Dynamical quantum simulation may be one of the first applications to see quantum advantage. However, the circuit depth of standard Trotterization methods can rapidly exceed the coherence time of noisy quantum computers. This has led to recent proposals for variational approaches to dynamical simulation. In this work, we aim to make variational dyna...

Trotterization-based, iterative approaches to quantum simulation (QS) are restricted to simulation times less than the coherence time of the quantum computer (QC), which limits their utility in the near term. Here, we present a hybrid quantum-classical algorithm, called variational fast forwarding (VFF), for decreasing the quantum circuit depth of...

The No-Free-Lunch (NFL) theorem is a celebrated result in learning theory that limits one's ability to learn a function with a training data set. With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer's ability to learn a unitary process (t...

Trotterization-based, iterative approaches to quantum simulation are restricted to simulation times less than the coherence time of the quantum computer, which limits their utility in the near term. Here, we present a hybrid quantum-classical algorithm, called Variational Fast Forwarding (VFF), for decreasing the quantum circuit depth of quantum si...

Cortical oscillations are central to information transfer in neural systems. Significant evidence supports the idea that coincident spike input can allow the neural threshold to be overcome and spikes to be propagated downstream in a circuit. Thus, an observation of oscillations in neural circuits would be an indication that repeated synchronous sp...

Although quantum computers are predicted to have many commercial applications, less attention has been given to their potential for resolving foundational issues in quantum mechanics. Here we focus on quantum computers' utility for the Consistent Histories formalism, which has previously been employed to study quantum cosmology, quantum paradoxes,...

A new class of neuromorphic processors promises to provide fast and power-efficient execution of spiking neural networks with on-chip synaptic plasticity. This efficiency derives in part from the fine-grained parallelism as well as event-driven communication mediated by spatially and temporally sparse spike messages. Another source of efficiency ar...

A rich array of spatially complex surface seiche modes exists in lakes. While the amplitude of these oscillations is often small, knowledge of their spatio-temporal characteristics is valuable for understanding when they might be of localized hydrodynamic importance. The expression and impact of these basin-scale barotropic oscillations in Lake Tah...

Compiling quantum algorithms for near-term quantum computers (accounting for connectivity and native gate alphabets) is a major challenge that has received significant attention both by industry and academia. Avoiding the exponential overhead of classical simulation of quantum dynamics will allow compilation of larger algorithms, and a strategy for...

For some time, it has been thought that backpropagation of errors could not be implemented in biophysiologically realistic neural circuits. This belief was largely due to either 1) the need for symmetric replication of feedback and feedforward weights, 2) the need for differing forms of activation between forward and backward propagating sweeps, an...

Light sheet fluorescence microscopy (LSFM) is a powerful tool for investigating model organisms including zebrafish. However, due to scattering and refractive index variations within the sample, the resulting image often suffers from low contrast. Structured illumination (SI) has been combined with scanned LSFM to remove out-of-focus and scattered...

While quantum computers are predicted to have many commercial applications, less attention has been given to their potential for resolving foundational issues in quantum mechanics. Here we focus on quantum computers' utility for the Consistent Histories formalism, which has previously been employed to study quantum cosmology, quantum paradoxes, and...

Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap ${\rm Tr}(\rho\sigma)$ between two qu...

Compiling quantum algorithms for near-term quantum computers (accounting for connectivity and native gate alphabets) is a major challenge that has received significant attention both by industry and academia. Avoiding the exponential overhead of classical simulation of quantum dynamics will allow compilation of larger algorithms, and a strategy for...

The brain has a central, short-term learning module, the hippocampus, which transfers what it has learned to long-term memory in cortex during non-REM sleep. The putative mechanism responsible for this type of memory consolidation invokes hierarchically nested hippocampal ripples (100-250 Hz), thalamo-cortical spindles (7-15 Hz), and cortical slow...

One of the most promising applications of an error-corrected universal quantum computer is the efficient simulation of complex quantum systems such as large molecular systems. In this application, one is interested in both the electronic structure such as the ground state energy and dynamical properties such as the scattering cross section and chem...

Light-sheet microscopy is an ideal imaging modality for long-term live imaging in model organisms. However, significant optical aberrations can be present when imaging into an organism that is hundreds of microns or greater in size. To measure and correct optical aberrations, an adaptive optics system must be incorporated into the microscope. Many...

Zebrafish are a promising vertebrate model for elucidating how neural circuits generate behavior under normal and pathological conditions. The Baraban group first demonstrated that zebrafish larvae are valuable for investigating seizure events and can be used as a model for epilepsy in humans. Because of their small size and transparency, zebrafish...

Coherent neuronal activity is believed to underlie the transfer and processing of information in the brain. Coherent activity in the form of synchronous firing and oscillations has been measured in many brain regions and has been correlated with enhanced feature processing and other sensory and cognitive functions. In the theoretical context, synfi...

Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. Because of their neutral stability along a linear manifold, line attractors are associated with a time-translational invariance that allows graded informatio...

While many tools exist for identifying and quantifying individual cell types, few methods are available to assess the relationships between cell types in organs and tissues and how these relationships change during aging or disease states. We present a quantitative method for evaluating cellular organization, using the mouse thymus as a test organ....

Software for the MiCASA program. The MiCASA program is coded in MATLAB R2014b and can be accessed through a graphical user interface (GUI). Users should note that they also need the open access Chronux package (http://Chronux.org). The GUI takes either RGB or paired single channel images for each condition. In the case of the single channel the GUI...

Supplementary Figures, Supplementary Note and Supplementary References

The thymus is essential for proper development and maintenance of a T-cell repertoire that can respond to newly encountered antigens, but its function can be adversely affected by internal factors such as pregnancy and normal aging or by external stimuli such as stress, infection, chemotherapy and ionizing radiation. We have utilized a unique archi...

Recent evidence suggests that neural information is encoded in packets and may be flexibly routed from region to region. We have hypothesized that neural circuits are split into sub-circuits where one sub-circuit controls information propagation via pulse gating and a second sub-circuit processes graded information under the control of the first su...

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, $C(...

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C(τ...

Recent evidence suggests that neural information is encoded in packets and may be flexibly routed from region to region. We have hypothesized that neural circuits are split into sub-circuits where one sub-circuit controls information propagation via pulse gating and a second sub-circuit processes graded information under the control of the first su...

Supporting information. (PDF)

The single excitation subspace (SES) method for universal quantum simulation is investigated for a number of diatomic molecular collision complexes. Assuming a system of $n$ tunably-coupled, and fully-connected superconducting qubits, computations are performed in the $n$-dimensional SES which maps directly to an $n$-channel collision problem withi...

Quantum particle simulations have largely been based on time-independent, split-operator schemes in which kinetic and potential operators are interwoven to provide accurate approximations to system dynamics. These simulations can be very expensive in terms of the number of gates required, although individual cases, such as tunneling, have been foun...

Information transmission is a key element for information processing in the brain. A number of mechanisms have been proposed for transferring volleys of spikes between layers of a feedforward neural circuit. Many of these mechanisms use synchronous activity to provide windows in time when spikes may be transferred more easily from layer to layer. R...

Coherent neural spiking and local field potentials are believed to be signatures of the binding and transfer of information in the brain. Coherent activity has now been measured experimentally in many regions of mammalian cortex. Synfire chains are one of the main theoretical constructs that have been appealed to to describe coherent spiking phenom...

Neural oscillations can enhance feature recognition (Azouz and Gray Proceedings of the National Academy of Sciences of the United States of America, 97, 8110-8115 2000), modulate interactions between neurons (Womelsdorf et al. Science, 316, 1609-01612 2007), and improve learning and memory (Markowska et al. The Journal of Neuroscience, 15, 2063-207...

Methods and systems for data analysis using covarying data. Eigenvalues and eigenvectors of one or more lagged covariance matrices of data obtained over time may be generated and used to enhance the data.

Methods and systems for data analysis using covarying data. Eigenvalues and eigenvectors of one or more lagged covariance matrices of data obtained over time may be generated and used to enhance the data.

To evaluate examiner variability in a superficial skin marker model of canine stifle kinematics. Experimental. Six clinically normal dogs. Dogs had 11 retroreflective markers fixed to the skin on the right hindlimb. Dogs were trotted 5 times through the calibrated testing space and this was repeated on 4 different testing days. Examiner A applied a...

Neural oscillations can enhance feature recognition, modulate interactions between neurons, and improve learning and memory. Simulational studies have shown that coherent oscillations give rise to windows in time during which information transfer can be enhanced in neuronal networks. Unanswered questions are: 1) What is the transfer mechanism? And...

Synchronous neural activity can improve neural processing and is believed to mediate neuronal interaction by providing temporal windows during which information is more easily transferred. We demonstrate a pulse gating mechanism in a feedforward network that can exactly propagate graded information through a multilayer circuit. Based on this mechan...

Previously, electrophysiological studies in adult zebrafish have been limited to slice preparations or to eye cup preparations and electrorentinogram recordings. This paper describes how an adult zebrafish can be immobilized, intubated, and used for in vivo electrophysiological experiments, allowing recording of neural activity. Immobilization of t...

We propose a method for general-purpose quantum computation and simulation that is well suited for today's pre-threshold-fidelity superconducting qubits. This approach makes use of the $n$-dimensional single-excitation subspace (SES) of a system of $n$ tunably coupled qubits. It can be viewed as a nonscalable special case of the standard gate-based...

Systems and methods for analyzing ratiometric data, e.g., ratiometric image data such as fluorescent image data, may generate a correlation matrix for the ratiometric data, generate a plurality of eigenvalues and a plurality of eigenvectors based on the correlation matrix, select a set of eigenvectors from the plurality of eigenvectors, and reconst...

A number of quantum algorithms have been performed on small quantum computers; these include Shor's prime factorization algorithm, error correction, Grover's search algorithm and a number of analog and digital quantum simulations. Because of the number of gates and qubits necessary, however, digital quantum particle simulations remain untested. A c...

The efficiency and accuracy of cortical maps from optical imaging experiments have been improved using periodic stimulation protocols. The resulting data analysis requires the detection and estimation of periodic information in a multivariate dataset. To date, these analyses have relied on discrete Fourier transform (DFT) sinusoid estimates. Multit...

In this paper, we extend our framework for constructing low-dimensional dynamical system models of large-scale neuronal networks of mammalian primary visual cortex. Our dimensional reduction procedure consists of performing a suitable linear change of variables and then systematically truncating the new set of equations. The extended framework incl...

Optical imaging in vivo is an important tool for allowing researchers to understand neural ensemble interactions during awake behavior, sleep, anesthesia and during seizure activity. A major bottleneck in the overall efficiency of neural imaging experiments is the need for post-hoc analysis of imaging data. Computational capabilities are now at the...

Evaluate the effect of marker placement on kinematics of the canine stifle in 3 distinct hindlimb models. In vivo biomechanical study. Normal adult mixed-breed dogs (n=5). Ten retroreflective markers were affixed to the skin on the right rear leg of each dog to establish normal stifle kinematics. Four additional markers were placed around the great...

We introduce a protocol for the fast simulation of n-dimensional quantum systems on n-qubit quantum computers with tunable couplings. A mapping is given between the control parameters of the quantum computer and the matrix elements of an n- dimensional real (but otherwise arbitrary) Hamiltonian that is simulated in the n- dimensional single-excitat...

Single-qubit gate design using oscillatory controls is related to the Rabi problem of rotating a spin. In the classical solution one drives the spin with an oscillatory electromagnetic field orthogonal to a background field. Here, we introduce a new, general method for constructing continuous, oscillatory quantum controls based on Floquet's theorem...

The ability to map functional connectivity is necessary for the study of the flow of activity in neuronal circuits. Optical imaging of calcium indicators, including FRET-based genetically encoded indicators and extrinsic dyes, is an important adjunct to electrophysiology and is widely used to visualize neuronal activity. However, techniques for map...

In this paper, we conduct an investigation of the null hypothesis distribution for functional magnetic resonance imaging (fMRI) time series using multiscale analysis tools, SiZer (significance of zero crossings of the derivative) and wavelets. Most current approaches to the analysis of fMRI data assume simple models for temporal (short term or long...

The use of nondestructive NMR spectroscopy for enzymatic studies offers unique opportunities to identify nearly all enzymatic byproducts and detect unstable short-lived products or intermediates at the molecular level; however, numerous challenges must be overcome before it can become a widely used tool. The biosynthesis of acetyl-coenzyme A (acety...

We introduce a protocol for the fast simulation of $n$-dimensional quantum systems on $n$-qubit quantum computers with tunable couplings. A mapping is given between the control parameters of the quantum computer and the matrix elements of $H_{\rm s}(t)$, an arbitrary, real, time-dependent $n\times n$ dimensional Hamiltonian that is simulated in the...

While advances are continually being made in the computational treatment of atomic and molecular scattering on classical computers, the computational costs grow exponentially with system size. As a consequence, collision complexes involving 5 particles are at the fore-front of modern research with exact treatment of larger systems currently intract...

To model the kinematics of the canine stifle in 3 dimensions using the Joint Coordinate System (JCS) and compare the JCS method with linear and segmental models. In vivo biomechanical study. Normal adult mixed breed dogs (n=6). Dogs had 10 retroreflective markers affixed to the skin on the right pelvic limb. Dogs were walked and trotted 5 times thr...

Systems-level neurophysiological data reveal coherent activity that is distributed across large regions of cortex. This activity is often thought of as an emergent property of recurrently connected networks. The fact that this activity is coherent means that populations of neurons may be thought of as the carriers of information, not individual neu...

Ratiometric fluorescent indicators are used for making quantitative measurements of a variety of physiological variables. Their utility is often limited by noise. This is the second in a series of papers describing statistical methods for denoising ratiometric data with the aim of obtaining improved quantitative estimates of variables of interest....

We investigate the effect that noise has on the evolution of measurement strategies and competition in populations of organisms with sensory systems of differing fidelities. We address two questions motivated by experimental and theoretical work on sensory systems in noisy environments: (1) How complex must a sensory system be in order to face the...

Drosophila melanogaster postfeeding larvae show food-averse migration toward food-free habitats before metamorphosis. This developmental switching from food attraction to aversion is regulated by a neuropeptide Y (NPY)-related brain signaling peptide. We used the fly larva model to delineate the neurobiological basis of age-restricted response to e...

Ratiometric fluorescent indicators are becoming increasingly prevalent in many areas of biology. They are used for making quantitative measurements of intracellular free calcium both in vitro and in vivo, as well as measuring membrane potentials, pH, and other important physiological variables of interest to researchers in many subfields. Often, fu...

The Sheng-Suzuki theorem states that all exponential operator splitting methods of order greater than 2 must contain negative time integration. There have been claims in the literature that higher-order splitting methods for deterministic parabolic equations are unstable due to this fact. We show stability for a class of higher-order splitting meth...

Josephson junctions have demonstrated enormous potential as qubits for scalable quantum computing architectures. Here we discuss the current approaches for making multi-qubit circuits and performing quantum information processing with them.

Cameleons are genetically encoded fluorescence resonance energy transfer (FRET)-based Ca(2+) indicators. Attempts to use cameleons to detect neural activity in vertebrate systems have been largely frustrated by the small FRET signal, in contradistinction to the higher signals seen in Drosophila and Caenorhabditis elegans. We have developed a statis...

Motivated by a problem in the evolution of sensory systems where gains obtained by improvements in detection are offset by increased costs, we prove several results about the dynamics of replicator equations with an n × n game matrix of the form: A ij = a i b j − c i . First, we show that, generically, for this class of game matrix, all equilibria...

Josephson junctions have demonstrated enormous potential as qubits for scalable quantum computing architectures. Here we discuss the current approaches for making multi-qubit circuits and performing quantum information processing with them.

We used slot blot hybridization, quantitative polymerase chain reaction (qPCR), and flow cytometry microarrays to quantify specific 16S rDNAs in weekly fecal specimens from four monkeys housed in a research vivarium for periods ranging from five to 8 months. Even in these uniformly housed and fed animals the gut microbiota is idiosyncratic, very dy...

Josephson junctions have demonstrated enormous potential as qubits for scalable quantum computing architectures. Here we study the speed and fidelity of four controlled-NOT gate implementations designed for capacitively coupled phase qubits. One gate applies to qubits fixed permanently in resonance, two require varying the dc current bias, and the...

In optical imaging experiments of primary visual cortex, visual stimuli evoke a complicated dynamics. Typically, any stimulus with sufficient contrast evokes a response. Much of the response is the same regardless of which stimulus is presented. For instance, when oriented drifting gratings are presented to the visual system, over 90% of the respon...

We consider a simple model of a Josephson junction phase qubit coupled to a solid-state nanoelectromechanical resonator. This and many related qubit-resonator models are analogous to an atom in an electromagnetic cavity. When the systems are weakly coupled and nearly resonant, the dynamics is accurately described by the rotating-wave approximation...

In many experimental circumstances, heart dynamics are, to a good approximation, periodic. For this reason, it makes sense to use high-resolution methods in the frequency domain to visualize the spectrum of imaging data of the heart and to estimate the deterministic signal content and extract the periodic signal from background noise in experimenta...

Previous methods for analyzing optical imaging data have relied heavily on temporal averaging. However, response dynamics are rich sources of information. Here, we develop and present a method that combines principal component analysis and multitaper harmonic analysis to extract the statistically significant spatial and temporal response from optic...

Any realistic model of the neuronal pathway from the retina to the visual cortex (V1) must account for the burstingbehavior of neurons in the lateral geniculate nucleus (LGN). A robust but minimal model, the integrate- and-fire-or-burst (IFB) model, has recently been proposed for individual LGN neurons. Based on this, we derive a dynamic population...