Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding

According to the theory of efficient coding, sensory systems are adapted to represent natural scenes with high fidelity and at minimal metabolic cost. Testing this hypothesis for sensory structures performing non-linear computations on high dimensional stimuli is still an open challenge. Here we develop a method to characterize the sensitivity of the retinal network to perturbations of a stimulus. Using closed-loop experiments, we explore selectively the space of possible perturbations around a given stimulus. We then show that the response of the retinal population to these small perturbations can be described by a local linear model. Using this model, we computed the sensitivity of the neural response to arbitrary temporal perturbations of the stimulus, and found a peak in the sensitivity as a function of the frequency of the perturbations. Based on a minimal theory of sensory processing, we argue that this peak is set to maximize information transmission. Our approach is relevant to testing the efficient coding hypothesis locally in any context where no reliable encoding model is known

Acquisition, Storage, and Retrieval in Digital and Biological Brains

My work examines how the brain acquires, stores, and retrieves information. I first present a theoretical model of the retina, and use the model to explore how the design of sensory systems affects our ability to make inferences about the physical world. I next present three analyses of electrocorticographic recordings taken as human neurosurgical patients participated in experimental cognitive tasks. In the first analysis, I measure the relation between single-neuron spiking and local field potentials, which reflect the aggregate activity of large populations of neurons. In the second analysis, I ask how the brain represents the meanings of individual words as they are studied and remembered. In the third electrocorticographic analysis, I address the question of how our brains retrieve memories of past experiences

Tissue- and Species-Specific Patterns of RNA metabolism in Post-Mortem Mammalian Retina and Retinal Pigment Epithelium.

Accurate analysis of gene expression in human tissues using RNA sequencing is dependent on the quality of source material. One major source of variation in mRNA quality is post-mortem time. While it is known that individual transcripts show differential post-mortem stability, few studies have directly and comprehensively analyzed mRNA stability following death, and in particular the extent to which tissue- and species-specific factors influence post-mortem mRNA stability are poorly understood. This knowledge is particularly important for ocular tissues studies, where tissues obtained post-mortem are frequently used for research or therapeutic applications. To directly investigate this question, we profiled mRNA levels in both neuroretina and retinal pigment epithelium (RPE) from mouse and baboon over a series of post-mortem intervals. We found substantial changes in gene expression as early as 15 minutes in the mouse and as early as three hours in the baboon eye tissues. Importantly, our findings demonstrate both tissue- and species- specific patterns of RNA metabolism, by identifying a set of genes that are either rapidly degraded or very stable in both species and/or tissues. Taken together, the data from this study lay the foundation for understanding RNA regulation post-mortem and provide novel insights into RNA metabolism in the tissues of the mammalian eye

Simulating and optimizing electrical current flow and neuronal activation in retinal implants

© 2018 Dr Timothy Bede Esler[Abstract Withheld

Optimal Sensory Coding By Populations Of ON And OFF Neurons

In many sensory systems the neural signal is coded by multiple parallel pathways, suggesting an evolutionary fitness benefit of general nature. A common pathway splitting is that into ON and OFF cells, responding to stimulus increments and decrements, respectively. According to efficient coding theory, sensory neurons have evolved to an optimal configuration for maximizing information transfer given the structure of natural stimuli and circuit constraints. Using the efficient coding framework, we describe two aspects of neural coding: how to optimally split a population into ON and OFF pathways, and how to allocate the firing thresholds of individual neurons given realistic noise levels, stimulus distributions and optimality measures. We find that populations of ON and OFF neurons convey equal information about the stimulus regardless of the ON/OFF mixture, once the thresholds are chosen optimally, independent of stimulus statistics and noise. However, an equal ON/OFF mixture is the most efficient as it uses the fewest spikes to convey this information. The optimal thresholds and coding efficiency, however, depend on noise and stimulus statistics if information is decoded by an optimal linear readout. With non-negligible noise, mixed ON/OFF populations reap significant advantages compared to a homogeneous population. The best coding performance is achieved by a unique mixture of ON/OFF neurons tuned to stimulus asymmetries and noise. We provide a theory for how different cell types work together to encode the full stimulus range using a diversity of response thresholds. The optimal ON/OFF mixtures derived from the theory accord with certain biases observed experimentally

Review of feature selection techniques in Parkinson's disease using OCT-imaging data

Several spectral-domain optical coherence tomography studies (OCT) reported a decrease on the macular region of the retina in Parkinson’s disease. Yet, the implication of retinal thinning with visual disability is still unclear. Macular scans acquired from patients with Parkinson’s disease (n = 100) and a control group (n = 248) were used to train several supervised classification models. The goal was to determine the most relevant retinal layers and regions for diagnosis, for which univari- ate and multivariate filter and wrapper feature selection methods were used. In addition, we evaluated the classification ability of the patient group to assess the applicability of OCT measurements as a biomarker of the disease

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