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 individual's 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 individual's 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 individual's 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.

Dependence of response properties on sparse connectivity in a spiking neuron model of the lateral geniculate nucleus.

We present a large-scale anatomically constrained spiking neuron model of the lateral geniculate nucleus (LGN), which operates solely with retinal input, relay cells, and interneurons. We show that interneuron inhibition and sparse connectivity between LGN cells could be key factors for explaining a number of observed classical and extraclassical response properties in LGN of monkey and cat. Among them are 1) weak orientation tuning, 2) contrast invariance of spatial frequency tuning in the absence of cortical feedback, 3) extraclassical surround suppression, and 4) orientation tuning of extraclassical surround suppression. The model also makes two surprising predictions: 1) a possible pinwheel-like spatial organization of orientation preference in the parvo layers of monkey LGN, much like what is seen in V1, and 2) a stimulus-induced trend (bias) in the orientation and phase preference of surround suppression, originating from the stimulus discontinuity between center and surround gratings rather than from specific circuitry.

Circuitry and the classification of simple and complex cells in V1.

Based on a large-scale neural network model of striate cortex (V1), we present a simulation study of extra- and intracellular response modulations for drifting and contrast reversal grating stimuli. Specifically, we study the dependency of these modulations on the neural circuitry. We find that the frequently used ratio of the first harmonic to the mean response to classify simple and complex cells is highly insensitive to circuitry. Limited experimental sample size for the distribution of this measure makes it unsuitable for distinguishing whether the dichotomy of simple and complex cells originates from distinct LGN axon connectivity and/or local circuitry in V1. We show that a possible useful measure in this respect is the ratio of the intracellular second- to first-harmonic response for contrast reversal gratings. This measure is highly sensitive to neural circuitry and its distribution can be sampled with sufficient accuracy from a limited amount of experimental data. Further, the distribution of this measure is qualitatively similar to that of the subfield correlation coefficient, although it is more robust and easier to obtain experimentally.

Extraclassical receptive field phenomena and short-range connectivity in V1.

Extraclassical receptive field phenomena in V1 are commonly attributed to long-range lateral connections and/or extrastriate feedback. We address 2 such phenomena: surround suppression and receptive field expansion at low contrast. We present rigorous computational support for the hypothesis that the phenomena largely result from local short-range (< 0.5 mm) cortical connections and lateral geniculate nucleus input. The neural mechanisms of surround suppression in our simulations operate via (A) enhancement of inhibition, (B) reduction of excitation, or (C) action of both simultaneously. Mechanisms (B) and (C) are substantially more prevalent than (A). We observe, on average, a growth in the spatial summation extent of excitatory and inhibitory synaptic inputs for low-contrast stimuli. However, we find this is neither sufficient nor necessary to explain receptive field expansion at low contrast, which usually involves additional changes in the relative gain of these inputs.

Analysis of a gain control model of V1: is the goal redundancy reduction?

In this paper we analyze a popular divisive normalization model of V1 with respect to the relationship between its underlying coding strategy and the extraclassical physiological responses of its constituent modeled neurons. Specifically we are interested in whether the optimization goal of redundancy reduction naturally leads to reasonable neural responses, including reasonable distributions of responses. The model is trained on an ensemble of natural images and tested using sinusoidal drifting gratings, with metrics such as suppression index and contrast dependent receptive field growth compared to the objective function values for a sample of neurons. We find that even though the divisive normalization model can produce "typical" neurons that agree with some neurophysiology data, distributions across samples do not agree with experimental data. Our results suggest that redundancy reduction itself is not necessarily causal of the observed extraclassical receptive field phenomena, and that additional optimization dimensions and/or biological constraints must be considered.