Texture segmentation is an effortless process in scene analysis, yet its neural mechanisms are not sufficiently understood. A common assumption in most current approaches is that texture segmentation is a vision problem. However, considering that texture is basically a surface property, this assumption can at times be misleading. One interesting possibility is that texture may be more intimately related with touch than with vision. Recent neurophysiological findings showed that receptive fields (RFs) for touch resemble that of vision, albeit with some subtle differences. To leverage on this, here I propose three ways to investigate the tactile receptive fields in the context of texture processing: (1) performance, (2) development, and (3) analysis. For performance, I tested how such distinct properties in tactile receptive fields can affect texture segmentation performance, as compared to that of visual receptive fields. Preliminary results suggest that touch has an advantage over vision in texture segmentation. These results support the idea that texture is fundamentally a tactile (surface) property. The next question is what drives the two types of RFs, visual and tactile, to become different during cortical development? I investigated the possibility that tactile RF and visual RF emerge based on the same cortical learning process, where the only difference is in the input type, natural-scene-like vs. texture-like. The main result is that RFs trained on natural scenes develop RFs resembling visual RFs, while those trained on texture resemble tactile RFs. These results again suggest a tight link between texture and the tactile modality, from a developmental context. To investigate further the functional properties of these RFs in texture processing, the response of tactile RFs and visual RFs were analyzed with manifold learning and with statistical approaches. The results showed that touch-based manifold seems more suitable for texture processing and desirable properties found in visual RF response can carry over to those in the tactile domain. These results are expected to shed new light on the role of tactile perception of texture; help develop more powerful, biologically inspired texture segmentation algorithms; and further clarify the differences and similarities between touch and vision.

Rapid receptive field (RF) reorganization of somatosensory neurons in the cat dorsal column nuclei (DCN) was studied using electrophysiological and histological methods. Soon after denervation of the peripheral RF by lidocaine injection, every DCN neuron tested exhibited a reorganized RF.

A fundamental question on visual system in neuroscience is how the visual stimuli are functionally related to neural responses. This relationship is often explained by the notion of receptive fields, an approximated linear or quasi-linear filter that encodes the high dimensional visual stimuli into neural spikes. Traditional methods for estimating the filter do not efficiently exploit prior information about the structure of neural receptive fields. Here, we propose several approaches to design the prior distribution over the filter, considering the neurophysiological fact that receptive fields tend to be localized both in space-time and spatio-temporal frequency domain. To automatically regularize the estimation of neural receptive fields, we use the evidence optimization technique, a MAP (maximum a posteriori) estimation under a prior distribution whose parameters are set by maximizing the marginal likelihood. Simulation results show that the proposed methods can estimate the receptive field using datasets that are tens to hundreds of times smaller than those required by traditional methods. / text

V minul©m stolet­ byl vynaloen znaÄn vzkum na pochopen­, jak jsou vizuln­ informace kdovny neurln­mi populacemi a jejich obvody. Celkov obraz, kter vyplynul z tohoto sil­, naznaÄuje, e vizuln­ informace jsou nejprve zpracovny sloitmi obvody v s­tnici a nslednÄ peneseny do vy­ch mozkovch struktur. Ukazuje se, e s­tnice i mozek si vyvinuly pozoruhodnÄ sofistikovan© vpoÄty pro extrakci tÄchto informac­. FunkÄn­ studie tÄchto neuronln­ch transformac­ byly provdÄny pomoc­ elektrofyziologickch nebo zobrazovac­ch technik. Tyto techniky omezovaly analzu prostorovch specializac­ s­tnice, a to buÄ poÄtem dostupnch elektrod (v elektrofyziologii) nebo velikost­ zorn©ho pole (FOV) (v zobrazovac­ch experimentech). Pro ukzku â zznamy aktivit gangliovch bunÄk s­tnice (RGC) byly omezeny na relativnÄ malou oblast (~ 200 x 200 um2) za pouit­ nejmodernÄj­ch zobrazovac­ch technik. Ve sv© diplomov© prci jsem prozkoumal novÄ vyvinutou metodu vyu­vaj­c­ FOV, kter je 40krt vÄt­ ve srovnn­ s FOV konvenÄn­ch optickch metod, co mi umoilo pekonat toto technick© omezen­. Prce vyu­v tuto novou zobrazovac­ metodu k prozkoumn­ populaÄn­ch charakteristik smÄrovÄ selektivn­ch gangliovch bunÄk (DSGC) v s­tnic­ch my­. Replikac­ ji znmch populaÄn­ch vzorc jsme verifikovali, e nae nov zobrazovc­ metoda funguje. Prce dle zkoum Äinky pomocnch ltek pro zven­ m­ry infekce RGCs. Tyto pomocn© ltky tak mohou potencilnÄ usnadnit nezaujat© zaznamenvn­ aktivit RGCs. Prce nav­c pedstavuje nov stimul pro inspekci receptivn­ch pol­ (RF) RGCs. Tento nov stimul pekonv konvenÄn­ stimuly pou­van© v souÄasnch studi­ch jak v rozlien­ vyprodukovan©ho RF, tak v nezbytn©m Äase prezentace stimulu a otev­r tak dvee pro nsleduj­c­ studie, kter© mohou poprv© popsat distribuÄn­ vzorce receptivn­ch poli nap­Ä s­tnic­ a zlepit tak klasifikaci bunÄÄnch t­d.

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Este trabalho mostra que a suavização Gaussiana pode exercer outra função além da filtração. Considerando-se imagens binárias, este processo pode funcionar como uma espécie de marcador, que modifica as feições das fronteiras entre duas regiões homogêneas contrastantes. Tais feições são pontos de concavidades, de convexidades ou de bordas em linha reta. Ou seja, toda a informação necessária para se caracterizar a forma bidimensional de uma região. A quantidade de suavização realizada em cada ponto depende da configuração preto/branco que compõe a vizinhança onde este se situa. Isto significa que cada ponto sofre uma quantidade particular de modificação, a qual reflete a interface local entre o objeto e o fundo. Então, para detectar tais feições, basta quantificar a suavização em cada ponto. No entanto, a discriminação pixel a pixel exige que a distribuição Gaussiana apresente boa localização, o que só acontece em escalas muito baixas (σ≅0,5). Assim, propõe-se uma distribuição construída a partir da soma de duas Gaussianas. Uma é bem estreita para garantir a boa localização e a outra possui abertura irrestrita para representar a escala desejada. Para confirmar a propriedade de marcação dessa distribuição, são propostos três detectores de corners de contorno, os quais são aplicados à detecção de pontos dominantes. O primeiro utiliza a entropia de Shannon para quantificar a suavização em cada ponto. O segundo utiliza as probabilidades de objeto e de fundo contidos na vizinhança observada. O terceiro utiliza a diferença entre Gaussianas (DoG) para determinar a quantidade suavizada, porém com a restrição de que uma das versões da imagem tenha suavização desprezível, para garantir a boa localização. Este trabalho se fundamenta na física da luz e na visão biológica. Os ótimos resultados apresentados sugerem que a detecção de curvaturas do sistema visual pode ocorrer na retina. / This work shows that the Gaussian smoothing can have additional function to filtration. Considering the binary images, this process can operate as a kind of marker that changes the features of the boundaries between two contrasting homogeneous regions. These features are points of concavities, convexities or straight edges, which are all the necessary information to characterize the two-dimensional shape of a region. The amount of smoothing performed at each point depends on the black/white configuration that composes the neighborhood where the point is located. This means that each point suffers a particular modification, which reflects the local interface between object and background. Thus, to detect such features, one must quantify the smoothing at each point. However, pixel-wise discrimination requires that the Gaussian distribution does not suffer flattening, which occurs in very low scales (σ≅0.5), only. Thus, it is proposed a distribution built from the sum of two Gaussians. One must be very narrow to ensure good localization, and the other is free to represent the desired scale. To confirm the property of marking, three boundary based corner detectors are proposed, which are applied to the detection of dominant points. The first uses the Shannon\'s entropy to quantify the smoothing at each point. The second uses the probabilities of object and background contained in the local neighborhood. The third uses the difference of Gaussians (DoG) to determine the amount of smoothing. This Work relies on the physics of light and biological vision. The presented results are good enough to suggest that the curvature detection, in visual system, occurs in the retina.

Este trabalho mostra que a suavização Gaussiana pode exercer outra função além da filtração. Considerando-se imagens binárias, este processo pode funcionar como uma espécie de marcador, que modifica as feições das fronteiras entre duas regiões homogêneas contrastantes. Tais feições são pontos de concavidades, de convexidades ou de bordas em linha reta. Ou seja, toda a informação necessária para se caracterizar a forma bidimensional de uma região. A quantidade de suavização realizada em cada ponto depende da configuração preto/branco que compõe a vizinhança onde este se situa. Isto significa que cada ponto sofre uma quantidade particular de modificação, a qual reflete a interface local entre o objeto e o fundo. Então, para detectar tais feições, basta quantificar a suavização em cada ponto. No entanto, a discriminação pixel a pixel exige que a distribuição Gaussiana apresente boa localização, o que só acontece em escalas muito baixas (σ≅0,5). Assim, propõe-se uma distribuição construída a partir da soma de duas Gaussianas. Uma é bem estreita para garantir a boa localização e a outra possui abertura irrestrita para representar a escala desejada. Para confirmar a propriedade de marcação dessa distribuição, são propostos três detectores de corners de contorno, os quais são aplicados à detecção de pontos dominantes. O primeiro utiliza a entropia de Shannon para quantificar a suavização em cada ponto. O segundo utiliza as probabilidades de objeto e de fundo contidos na vizinhança observada. O terceiro utiliza a diferença entre Gaussianas (DoG) para determinar a quantidade suavizada, porém com a restrição de que uma das versões da imagem tenha suavização desprezível, para garantir a boa localização. Este trabalho se fundamenta na física da luz e na visão biológica. Os ótimos resultados apresentados sugerem que a detecção de curvaturas do sistema visual pode ocorrer na retina. / This work shows that the Gaussian smoothing can have additional function to filtration. Considering the binary images, this process can operate as a kind of marker that changes the features of the boundaries between two contrasting homogeneous regions. These features are points of concavities, convexities or straight edges, which are all the necessary information to characterize the two-dimensional shape of a region. The amount of smoothing performed at each point depends on the black/white configuration that composes the neighborhood where the point is located. This means that each point suffers a particular modification, which reflects the local interface between object and background. Thus, to detect such features, one must quantify the smoothing at each point. However, pixel-wise discrimination requires that the Gaussian distribution does not suffer flattening, which occurs in very low scales (σ≅0.5), only. Thus, it is proposed a distribution built from the sum of two Gaussians. One must be very narrow to ensure good localization, and the other is free to represent the desired scale. To confirm the property of marking, three boundary based corner detectors are proposed, which are applied to the detection of dominant points. The first uses the Shannon\'s entropy to quantify the smoothing at each point. The second uses the probabilities of object and background contained in the local neighborhood. The third uses the difference of Gaussians (DoG) to determine the amount of smoothing. This Work relies on the physics of light and biological vision. The presented results are good enough to suggest that the curvature detection, in visual system, occurs in the retina.

One of the major goals of this thesis is to investigate the extent to which correspondence noise, (i.e., the false pairing of dots in adjacent frames) limits motion detection performance in random dot kinematograms (RDKs). The performance measures of interest are Dmax and Dmin i.e., the largest and smallest inter-frame dot displacement, respectively, for which motion can be reliably detected. Dmax and threshold coherence (i.e., the smallest proportion of dots that must be moved between frames for motion to be reliably detected) in RDKs are known to be affected by false pairing or correspondence noise. Here the roles of correspondence noise and receptive field geometry in limiting performance are investigated. The range of Dmax observed in the literature is consistent with the current information-limit based interpretation. Dmin is interpreted in the light of correspondence noise and under-sampling. Based on the psychophysical experiments performed in the early parts of the dissertation, a model for correspondence noise based on the principle of receptive field scaling is developed for Dmax. Model simulations provide a good account of psychophysically estimated Dmax over a range of stimulus parameters, showing that correspondence noise and receptive field geometry have a major influence on displacement thresholds.

Patterned neuronal activity driven by the sensory environment plays a key role in the development of precise synaptic connectivity in the brain. It is well established that the action potentials (‘spikes’) generated by individual neurons are crucial to this developmental process. A neuron’s spiking activity is jointly determined by its synaptic inputs and its intrinsic excitability. It is therefore important to ask how a neuron develops these attributes, and whether the emergence of spiking might itself be governed by activity-dependent processes. In this thesis, I address these questions in the retinotectal system of Xenopus laevis. First, I investigate the extent to which visuospatial information is available to the developing retinotectal system. I show that the eyes of developing Xenopus larvae are hyperopic at the onset of vision, but rapidly grow towards correct vision. Despite its imperfect optics, the Xenopus eye is able to generate spatially restricted activity on the retina, which is evident in the spatial structure of the receptive fields (RFs) of tectal neurons. Using a novel method to map the visually driven spiking output and synaptic inputs of the same tectal neuron in vivo, I show that neuronal spiking activity closely follows the spatiotemporal profile of glutamatergic inputs. Next, I characterise a population of neurons in the developing optic tectum that does not fire action potentials, despite receiving visually evoked glutamatergic and γ-aminobutyric acid (GABA)ergic synaptic inputs. A comparison of visually spiking and visually non-spiking neurons reveals that the principal reason these neurons are ‘silent’ is that they lack sufficient glutamatergic synaptic excitation. In the final section of the thesis, I investigate whether visually driven activity can play a role in the ‘unsilencing’ of these silent neurons. I show that non-spiking tectal neurons can be rapidly converted into spiking neurons through a visual conditioning protocol. This conversion is associated with a selective increase in glutamatergic input and implicates a novel, spike-independent form of synaptic potentiation. I provide evidence that this novel plasticity process is mediated by GABAergic inputs that are depolarising during early development, and can act in synergy with N-methyl-D-aspartate receptors (NMDARs) to strengthen immature glutamatergic synapses. Consistent with this, preventing the depolarising effects of GABA or blocking NMDARs abolished the activity-dependent unsilencing of tectal neurons. These results therefore support a model in which GABAergic and glutamatergic transmitter systems function synergistically to enable a neuron to recruit the synaptic excitation it needs to develop sensory-driven spiking activity. This represents a transition with important consequences for both the functional output and the activity-dependent development of a neuron.

One of the major goals of this thesis is to investigate the extent to which correspondence noise, (i.e., the false pairing of dots in adjacent frames) limits motion detection performance in random dot kinematograms (RDKs). The performance measures of interest are Dmax and Dmin i.e., the largest and smallest inter-frame dot displacement, respectively, for which motion can be reliably detected. Dmax and threshold coherence (i.e., the smallest proportion of dots that must be moved between frames for motion to be reliably detected) in RDKs are known to be affected by false pairing or correspondence noise. Here the roles of correspondence noise and receptive field geometry in limiting performance are investigated. The range of Dmax observed in the literature is consistent with the current information-limit based interpretation. Dmin is interpreted in the light of correspondence noise and under-sampling. Based on the psychophysical experiments performed in the early parts of the dissertation, a model for correspondence noise based on the principle of receptive field scaling is developed for Dmax. Model simulations provide a good account of psychophysically estimated Dmax over a range of stimulus parameters, showing that correspondence noise and receptive field geometry have a major influence on displacement thresholds.