29 research outputs found
Parameter estimation from an Ornstein-Uhlenbeck process with measurement noise
This article aims to investigate the impact of noise on parameter fitting for
an Ornstein-Uhlenbeck process, focusing on the effects of multiplicative and
thermal noise on the accuracy of signal separation. To address these issues, we
propose algorithms and methods that can effectively distinguish between thermal
and multiplicative noise and improve the precision of parameter estimation for
optimal data analysis. Specifically, we explore the impact of both
multiplicative and thermal noise on the obfuscation of the actual signal and
propose methods to resolve them. Firstly, we present an algorithm that can
effectively separate thermal noise with comparable performance to Hamilton
Monte Carlo (HMC) but with significantly improved speed. Subsequently, we
analyze multiplicative noise and demonstrate that HMC is insufficient for
isolating thermal and multiplicative noise. However, we show that, with
additional knowledge of the ratio between thermal and multiplicative noise, we
can accurately distinguish between the two types of noise when provided with a
sufficiently large sampling rate or an amplitude of multiplicative noise
smaller than thermal noise. This finding results in a situation that initially
seems counterintuitive. When multiplicative noise dominates the noise spectrum,
we can successfully estimate the parameters for such systems after adding
additional white noise to shift the noise balance.Comment: 16 pages, 4 figure
Second-Hand Stress: Neurobiological Evidence for a Human Alarm Pheromone
Alarm pheromones are airborne chemical signals, released by an individual into the environment, which transmit warning of danger to conspecifics via olfaction. Using fMRI, we provide the first neurobiological evidence for a human alarm pheromone. Individuals showed activation of the amygdala in response to sweat produced by others during emotional stress, with exercise sweat as a control; behavioral data suggest facilitated evaluation of ambiguous threat
FixFit: using parameter-compression to solve the inverse problem in overdetermined models
All fields of science depend on mathematical models. One of the fundamental
problems with using complex nonlinear models is that data-driven parameter
estimation often fails because interactions between model parameters lead to
multiple parameter sets fitting the data equally well. Here, we develop a new
method to address this problem, FixFit, which compresses a given mathematical
model's parameters into a latent representation unique to model outputs. We
acquire this representation by training a neural network with a bottleneck
layer on data pairs of model parameters and model outputs. The bottleneck layer
nodes correspond to the unique latent parameters, and their dimensionality
indicates the information content of the model. The trained neural network can
be split at the bottleneck layer into an encoder to characterize the
redundancies and a decoder to uniquely infer latent parameters from
measurements. We demonstrate FixFit in two use cases drawn from classical
physics and neuroscience
Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks
Task-free connectivity analyses have emerged as a powerful tool in functional neuroimaging. Because the cross-correlations that underlie connectivity measures are sensitive to distortion of time-series, here we used a novel dynamic phantom to provide a ground truth for dynamic fidelity between blood oxygen level dependent (BOLD)-like inputs and fMRI outputs. We found that the de facto quality-metric for task-free fMRI, temporal signal to noise ratio (tSNR), correlated inversely with dynamic fidelity; thus, studies optimized for tSNR actually produced time-series that showed the greatest distortion of signal dynamics. Instead, the phantom showed that dynamic fidelity is reasonably approximated by a measure that, unlike tSNR, dissociates signal dynamics from scanner artifact. We then tested this measure, signal fluctuation sensitivity (SFS), against human resting-state data. As predicted by the phantom, SFS—and not tSNR—is associated with enhanced sensitivity to both local and long-range connectivity within the brain's default mode network
Chemosensory Cues to Conspecific Emotional Stress Activate Amygdala in Humans
Alarm substances are airborne chemical signals, released by an individual into the environment, which communicate emotional stress between conspecifics. Here we tested whether humans, like other mammals, are able to detect emotional stress in others by chemosensory cues. Sweat samples collected from individuals undergoing an acute emotional stressor, with exercise as a control, were pooled and presented to a separate group of participants (blind to condition) during four experiments. In an fMRI experiment and its replication, we showed that scanned participants showed amygdala activation in response to samples obtained from donors undergoing an emotional, but not physical, stressor. An odor-discrimination experiment suggested the effect was primarily due to emotional, and not odor, differences between the two stimuli. A fourth experiment investigated behavioral effects, demonstrating that stress samples sharpened emotion-perception of ambiguous facial stimuli. Together, our findings suggest human chemosensory signaling of emotional stress, with neurobiological and behavioral effects
Electrostatically Self-assembled Amphiplexes
This research will focus on characterizing the phase behavior of polyelectrolyte-surfactant microemulsions (PSM) that were recently discovered in our lab and indentifing possible uses of their long-range ordered nanostructures towards bioseparation, oil-recovery and drug delivery systems. In addition, we are proposing strategies for synthesizing solid and long-range ordered materials with unit cells on the nanometer scale using polymerization and/or cross-linking to solidify the soft template
Recommended from our members
Electrostatically Self-assembled Amphiplexes
This research will focus on characterizing the phase behavior of polyelectrolyte-surfactant microemulsions (PSM) that were recently discovered in our lab and indentifing possible uses of their long-range ordered nanostructures towards bioseparation, oil-recovery and drug delivery systems. In addition, we are proposing strategies for synthesizing solid and long-range ordered materials with unit cells on the nanometer scale using polymerization and/or cross-linking to solidify the soft template