Brain Differently Changes Its Algorithms in Parallel Processing of Visual Information

Feedback from the visual cortex (Vl) to the Lateral Geniculate Nucleus (LGN) in macaque monkey increase contrast gain of LGN neurons for black and white (B&W) and for color (C) stimuli. LGN parvocellular cells responses to B&W gratings are enhanced by feedback multiplicatively and in contrast independent manner. However, in magnocellular neurons corticofugal pathways enhance cells responses in a contrast~dependent non-linear manner. For C stimuli cortical feedback enhances parvocellular neurons responses in a very strong contrast-dependent manner. Based on these results [13] we propose a model which includes excitatory and inhibitory effects on cells activity (shunting equations) in retina and LGN while taking into account the anatomy of cortical feedback connections. The main mechanisms related to different algorithms of the data processing in the visual brain are differences in feedback properties from Vl to parvocellular (PC) and to magnocellular (MC) neurons. Descending pathways from Vl change differently receptive field (RF) structure of PC and MC cells. For B&W stimuli, in PC cells feedback changes gain similarly in the RF center and in the RF surround, leaving PC RF structure invariant. However, feedback influence MC cells in two ways: directly and through LGN interneurons, which together changes gain and sizes of their RF center differently than gain and size of the RF surround. For C stimuli PC cells operate like MC cells for B&W. The first mechanism extracts from the stimulus an important features in a independent way from other stimulus parameters, whereas the second channel changes its tuning properties as a function of other stimulus attributes like contrast and/or spatial extension. The model suggests novel idea about the possible functional role of PC and MC pathways

The iso-response method

Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments

A deep neural network model of the primate superior colliculus for emotion recognition

Although sensory processing is pivotal to nearly every theory of emotion, the evaluation of the visual input as ‘emotional’ (e.g. a smile as signalling happiness) has been traditionally assumed to take place in supramodal ‘limbic’ brain regions. Accordingly, subcortical structures of ancient evolutionary origin that receive direct input from the retina, such as the superior colliculus (SC), are traditionally conceptualized as passive relay centres. However, mounting evidence suggests that the SC is endowed with the necessary infrastructure and computational capabilities for the innate recognition and initial categorization of emotionally salient features from retinal information. Here, we built a neurobiologically inspired convolutional deep neural network (DNN) model that approximates physiological, anatomical and connectional properties of the retino-collicular circuit. This enabled us to characterize and isolate the initial computations and discriminations that the DNN model of the SC can perform on facial expressions, based uniquely on the information it directly receives from the virtual retina. Trained to discriminate facial expressions of basic emotions, our model matches human error patterns and above chance, yet suboptimal, classification accuracy analogous to that reported in patients with V1 damage, who rely on retino-collicular pathways for non-conscious vision of emotional attributes. When presented with gratings of different spatial frequencies and orientations never ‘seen’ before, the SC model exhibits spontaneous tuning to low spatial frequencies and reduced orientation discrimination, as can be expected from the prevalence of the magnocellular (M) over parvocellular (P) projections. Likewise, face manipulation that biases processing towards the M or P pathway affects expression recognition in the SC model accordingly, an effect that dovetails with variations of activity in the human SC purposely measured with ultra-high field functional magnetic resonance imaging. Lastly, the DNN generates saliency maps and extracts visual features, demonstrating that certain face parts, like the mouth or the eyes, provide higher discriminative information than other parts as a function of emotional expressions like happiness and sadness. The present findings support the contention that the SC possesses the necessary infrastructure to analyse the visual features that define facial emotional stimuli also without additional processing stages in the visual cortex or in ‘limbic’ areas

Algorithms for colour image processing based on neurological models

373 p. : il., gráf., fot., tablasColour image processing is nowadays mostly achieved through the extrapolation of algorithms developed for images in grey levels into three colour planes, either RGB or some transformed planes, such as HSI, CIELAB... These techniques provide reliable solutions only in simple situations. As colour is a perception and not a characteristic inherent to objects, this thesis has developed new bioinspired algorithms for colour image processing. The work of this thesis has joined elements in colour theory and processing undertaken in the human visual system. A new functional model of the retina has been developed where each cell type has been characterised according to its connections, distribution and size. A retina architecture has been created which provides detailed information about its cell elements and organisation. This has allowed the creation of a retina model that generates a set of parallel output channels as happens in the human retina. The level of detail provided in the model has allowed the characterisation of each of the pathways with a precision that is not present in existing models described in scientific publications. The development of a colour processing model requires the combination of a functional retina model with colour appearance models. This union has achieved a new algorithm for colour image processing that provides colour attributes, such as: hue, lightness, brightness, saturation, chroma, colourfulness as well as edge detection components both in chromatic as well as achromatic components. The results provided by this model have been compared with CIECAM02 model's ones and have obtained noticeably better results in the "ab" plane and in the attributes calculated on Munsell colour samples. The colour processing model is backed by its results and has allowed identifying output channels of the retina that make up the usual "a", "b" and "A" channels in colour appearance models. This model entails a step forward on colour processing techniques that shall be of great use for image segmentation, characterisation and object identification. Key Words Colour image processing, neuroinspired models, computational modelling, colour appearance models. Colour image processing is nowadays mostly achieved through the extrapolation of algorithms developed for images in grey levels into three colour planes, either RGB or some transformed planes, such as HSI, CIELAB... These techniques provide reliable solutions only in simple situations. As colour is a perception and not a characteristic inherent to objects, this thesis has developed new bioinspired algorithms for colour image processing. The work of this thesis has joined elements in colour theory and processing undertaken in the human visual system. A new functional model of the retina has been developed where each cell type has been characterised according to its connections, distribution and size. A retina architecture has been created which provides detailed information about its cell elements and organisation. This has allowed the creation of a retina model that generates a set of parallel output channels as happens in the human retina. The level of detail provided in the model has allowed the characterisation of each of the pathways with a precision that is not present in existing models described in scientific publications. The development of a colour processing model requires the combination of a functional retina model with colour appearance models. This union has achieved a new algorithm for colour image processing that provides colour attributes, such as: hue, lightness, brightness, saturation, chroma, colourfulness as well as edge detection components both in chromatic as well as achromatic components. The results provided by this model have been compared with CIECAM02 model's ones and have obtained noticeably better results in the "ab" plane and in the attributes calculated on Munsell colour samples. The colour processing model is backed by its results and has allowed identifying output channels of the retina that make up the usual "a", "b" and "A" channels in colour appearance models. This model entails a step forward on colour processing techniques that shall be of great use for image segmentation, characterisation and object identification. Key Words - Colour image processing, neuroinspired models, computational modelling, colour appearance models.El Gobierno Vasco ha proporcionado apoyo financiero a través del programa ETORTEK, para las estancias en el Instituto Técnico de Massachusetts (MIT) y en la Universidad de Cambridge

High accuracy decoding of dynamical motion from a large retinal population

Motion tracking is a challenge the visual system has to solve by reading out the retinal population. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar's position. Instead, we found that ganglion cells fired sparsely over an area much larger than predicted by their receptive fields, so that the neural image did not track the bar. This highly redundant organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.Comment: 23 pages, 7 figure

Low-level visual processing and its relation to neurological disease

Retinal neurons extract changes in image intensity across space, time, and wavelength. Retinal signal is transmitted to the early visual cortex, where the processing of low-level visual information occurs. The fundamental nature of these early visual pathways means that they are often compromised by neurological disease. This thesis had two aims. First, it aimed to investigate changes in visual processing in response to Parkinson’s disease (PD) by using electrophysiological recordings from animal models. Second, it aimed to use functional magnetic resonance imaging (fMRI) to investigate how low-level visual processes are represented in healthy human visual cortex, focusing on two pathways often compromised in disease; the magnocellular pathway and chromatic S-cone pathway. First, we identified a pathological mechanism of excitotoxicity in the visual system of Drosophila PD models. Next, we found that we could apply machine learning classifiers to multivariate visual response profiles recorded from the eye and brain of Drosophila and rodent PD models to accurately classify these animals into their correct class. Using fMRI and psychophysics, found that measurements of temporal contrast sensitivity differ as a function of visual space, with peripherally tuned voxels in early visual areas showing increased contrast sensitivity at a high temporal frequency. Finally, we used 7T fMRI to investigate systematic differences in achromatic and S-cone population receptive field (pRF) size estimates in the visual cortex of healthy humans. Unfortunately, we could not replicate the fundamental effect of pRF size increasing with eccentricity, indicating complications with our data and stimulus