24,403 research outputs found
Neural Connectivity with Hidden Gaussian Graphical State-Model
The noninvasive procedures for neural connectivity are under questioning.
Theoretical models sustain that the electromagnetic field registered at
external sensors is elicited by currents at neural space. Nevertheless, what we
observe at the sensor space is a superposition of projected fields, from the
whole gray-matter. This is the reason for a major pitfall of noninvasive
Electrophysiology methods: distorted reconstruction of neural activity and its
connectivity or leakage. It has been proven that current methods produce
incorrect connectomes. Somewhat related to the incorrect connectivity
modelling, they disregard either Systems Theory and Bayesian Information
Theory. We introduce a new formalism that attains for it, Hidden Gaussian
Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden
by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS
is equivalent to a frequency domain Linear State Space Model (LSSM) but with
sparse connectivity prior. The mathematical contribution here is the theory for
high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS
can attenuate the leakage effect in the most critical case: the distortion EEG
signal due to head volume conduction heterogeneities. Its application in EEG is
illustrated with retrieved connectivity patterns from human Steady State Visual
Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence
for noninvasive procedures of neural connectivity: concurrent EEG and
Electrocorticography (ECoG) recordings on monkey. Open source packages are
freely available online, to reproduce the results presented in this paper and
to analyze external MEEG databases
Statistical Mechanics and Visual Signal Processing
The nervous system solves a wide variety of problems in signal processing. In
many cases the performance of the nervous system is so good that it apporaches
fundamental physical limits, such as the limits imposed by diffraction and
photon shot noise in vision. In this paper we show how to use the language of
statistical field theory to address and solve problems in signal processing,
that is problems in which one must estimate some aspect of the environment from
the data in an array of sensors. In the field theory formulation the optimal
estimator can be written as an expectation value in an ensemble where the input
data act as external field. Problems at low signal-to-noise ratio can be solved
in perturbation theory, while high signal-to-noise ratios are treated with a
saddle-point approximation. These ideas are illustrated in detail by an example
of visual motion estimation which is chosen to model a problem solved by the
fly's brain. In this problem the optimal estimator has a rich structure,
adapting to various parameters of the environment such as the mean-square
contrast and the correlation time of contrast fluctuations. This structure is
in qualitative accord with existing measurements on motion sensitive neurons in
the fly's brain, and we argue that the adaptive properties of the optimal
estimator may help resolve conlficts among different interpretations of these
data. Finally we propose some crucial direct tests of the adaptive behavior.Comment: 34pp, LaTeX, PUPT-143
Integrated 2-D Optical Flow Sensor
I present a new focal-plane analog VLSI sensor that estimates optical flow in two visual dimensions. The chip significantly improves previous approaches both with respect to the applied model of optical flow estimation as well as the actual hardware implementation. Its distributed computational architecture consists of an array of locally connected motion units that collectively solve for the unique optimal optical flow estimate. The novel gradient-based motion model assumes visual motion to be translational, smooth and biased. The model guarantees that the estimation problem is computationally well-posed regardless of the visual input. Model parameters can be globally adjusted, leading to a rich output behavior. Varying the smoothness strength, for example, can provide a continuous spectrum of motion estimates, ranging from normal to global optical flow. Unlike approaches that rely on the explicit matching of brightness edges in space or time, the applied gradient-based model assures spatiotemporal continuity on visual information. The non-linear coupling of the individual motion units improves the resulting optical flow estimate because it reduces spatial smoothing across large velocity differences. Extended measurements of a 30x30 array prototype sensor under real-world conditions demonstrate the validity of the model and the robustness and functionality of the implementation
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