Jim Wielaard

In vivo snapshot hyperspectral image analysis of age-related macular degeneration

Drusen, the hallmark lesions of age related macular degeneration (AMD), are biochemically heterogeneous and the identification of their biochemical distribution is key to the understanding of AMD. Yet the challenges are to develop imaging technology and analytics, which respect the physical generation of the hyperspectral signal in the presence of noise, artifacts, and multiple mixed sources while maximally exploiting the full data dimensionality to uncover clinically relevant spectral signatures. This paper reports on the statistical analysis of hyperspectral signatures of drusen and anatomical regions of interest using snapshot hyperspectral imaging and non-negative matrix factorization (NMF). We propose physical meaningful priors as initialization schemes to NMF for finding low-rank decompositions that capture the underlying physiology of drusen and the macular pigment. Preliminary results show that snapshot hyperspectral imaging in combination with NMF is able to detect biochemically meaningful components of drusen and the macular pigment. To our knowledge, this is the first reported demonstration in vivo of the separate absorbance peaks for lutein and zeaxanthin in macular pigment.

Perceptual decision making investigated via sparse decoding of a spiking neuron model of V1

Recent empirical evidence supports the hypothesis that invariant visual object recognition might result from non-linear encoding of the visual input followed by linear decoding [1]. This hypothesis has received theoretical support through the development of neural network architectures which are based on a non-linear encoding of the input via recurrent network dynamics followed by a linear decoder [2], [3]. In this paper we consider such an architecture in which the visual input is non-linearly encoded by a biologically realistic spiking model of V1, and mapped to a perceptual decision via a sparse linear decoder. Novel is that we 1) utilize a large-scale conductance based spiking neuron model of V1 which has been well-characterized in terms of classical and extra-classical response properties, and 2) use the model to investigate decoding over a large population of neurons. We compare decoding performance of the model system to human performance by comparing neurometric and psychometric curves.

Perceptual Decision Making “Through the Eyes” of a Large-scale Neural Model of V1

Sparse coding has been posited as an efficient information processing strategy employed by sensory systems, particularly visual cortex. Substantial theoretical and experimental work has focused on the issue of sparse encoding, namely how the early visual system maps the scene into a sparse representation. In this paper we investigate the complementary issue of sparse decoding, for example given activity generated by a realistic mapping of the visual scene to neuronal spike trains, how do downstream neurons best utilize this representation to generate a “decision.” Specifically we consider both sparse (L1-regularized) and non-sparse (L2 regularized) linear decoding for mapping the neural dynamics of a large-scale spiking neuron model of primary visual cortex (V1) to a two alternative forced choice (2-AFC) perceptual decision. We show that while both sparse and non-sparse linear decoding yield discrimination results quantitatively consistent with human psychophysics, sparse linear decoding is more efficient in terms of the number of selected informative dimension.

Decoding simulated neurodynamics predicts the perceptual consequences of age-related macular degeneration.

Age-related macular degeneration (AMD) is the major cause of blindness in the developed world. Though substantial work has been done to characterize the disease, it is difficult to predict how the state of an individuals retina will ultimately affect their high-level perceptual function. In this paper, we describe an approach that couples retinal imaging with computational neural modeling of early visual processing to generate quantitative predictions of an individuals visual perception. Using a patient population with mild to moderate AMD, we show that we are able to accurately predict subject-specific psychometric performance by decoding simulated neurodynamics that are a function of scotomas derived from an individuals fundus image. On the population level, we find that our approach maps the disease on the retina to a representation that is a substantially better predictor of high-level perceptual performance than traditional clinical metrics such as drusen density and coverage. In summary, our work identifies possible new metrics for evaluating the efficacy of treatments for AMD at the level of the expected changes in high-level visual perception and, in general, typifies how computational neural models can be used as a framework to characterize the perceptual consequences of early visual pathologies.

Simulated optical imaging of orientation preference in a model of V1

Optical imaging studies have played an important role in mapping the orientation selectivity and ocular dominance of neurons across an extended area of primary visual cortex (V1). Such studies have produced images with a more or less smooth and regular spatial distribution of relevant neuronal response properties. This is in spite of the fact that results from electrophysiological recordings, though limited in their number and spatial distribution, show significant scatter/variability in the relevant response properties of nearby neurons. We present a simulation of the optical imaging experiments of ocular dominance and orientation selectivity using a computational model of the primary visual cortex. The simulations assume that the optical imaging signal is proportional to the averaged response of neighboring neurons. The model faithfully reproduces ocular dominance columns and orientation pinwheels in the presence of realistic scatter of single cell preferred responses. In addition,we find the simulated optical imaging of orientation pinwheels to be remarkably robust, with the pinwheel structure maintained up to an addition of /spl plusmn/60 degrees of random scatter in the orientation preference of single cells. Our results suggest that an optical imaging result does not necessarily, by itself, provide any obvious upper bound for the scatter of the underlying neuronal response properties on local scales.