Spontaneous travelling waves of neuronal activity are a prominent feature throughout the developing brain and have been shown to be essential for achieving normal function, but the mechanism of their action on post-synaptic connections remains unknown. A well-known and widespread mechanism for altering synaptic strengths is spike-timing dependent plasticity (STDP), whereby the temporal relationship between the pre- and post-synaptic spikes determines whether a synapse is strengthened or weakened. Here, I answer the theoretical question of how these two phenomenon interact: what types of connectivity patterns can emerge when travelling waves drive a downstream area that implements STDP, and what are the critical features of the waves and the plasticity rules that shape these patterns? I then demonstrate how the theory can be applied to the development of the visual system, where retinal waves are hypothesised to play a role in the refinement of downstream connections. My major findings are as follows. (1) Mathematically, STDP translates the correlated activity of travelling waves into coherent patterns of synaptic connectivity; it maps the spatiotemporal structure in waves into a spatial pattern of synaptic strengths, building periodic structures into feedforward circuits. This is analogous to pattern formation in reaction diffusion systems. The theory reveals a role for the wave speed and time scale of the STDP rule in determining the spatial frequency of the connectivity pattern. (2) Simulations verify the theory and extend it from one-dimensional to two-dimensional cases, and from simplified linear wavefronts to more complex realistic and noisy wave patterns. (3) With appropriate constraints, these pattern formation abilities can be harnessed to explain a wide range of developmental phenomena, including how receptive fields (RFs) in the visual system are refined in size and topography and how simple-cell and direction selective RFs can develop. The theory is applied to the visual system here but generalises across different brain areas and STDP rules. The theory makes several predictions that are testable using existing experimental paradigms.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-04-24T15:04:03Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mapas-auto-organizaveis2.pdf: 2835656 bytes, checksum: 8836a86bd2cced9353cb25b53383b305 (MD5) / Made available in DSpace on 2017-04-24T15:04:03Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) mapas-auto-organizaveis2.pdf: 2835656 bytes, checksum: 8836a86bd2cced9353cb25b53383b305 (MD5) Previous issue date: 2016-08-16 / Métodos e algoritmos em aprendizado de máquina não supervisionado têm sido empregados em diversos problemas significativos. Uma explosão na disponibilidade de dados de várias fontes e modalidades está correlacionada com os avanços na obtenção, compressão, armazenamento, transferência e processamento de grandes quantidades de dados complexos com alta dimensionalidade, como imagens digitais, vídeos de vigilância e microarranjos de DNA. O agrupamento se torna difícil devido à crescente dispersão desses dados, bem como a dificuldade crescente em discriminar distâncias entre os pontos de dados. Este trabalho apresenta um algoritmo de agrupamento suave em subespaços baseado em um mapa auto-organizável (SOM) com estrutura variante no tempo, o que significa que o agrupamento dos dados pode ser alcançado sem qualquer conhecimento prévio, tais como o número de categorias ou a topologia dos padrões de entrada, nos quais ambos são determinados durante o processo de treinamento. O modelo também atribui diferentes pesos a diferentes dimensões, o que implica que cada dimensão contribui para o descobrimento dos aglomerados de dados. Para validar o modelo, diversos conjuntos de dados reais foram utilizados, considerando uma diversificada gama de contextos, tais como mineração de dados, expressão genética, agrupamento multivista e problemas de visão computacional. Os resultados são promissores e conseguem lidar com dados reais caracterizados pela alta dimensionalidade. / Unsupervised learning methods have been employed on many significant problems. A blast in the availability of data from multiple sources and modalities is correlated with advancements in how to obtain, compress, store, transfer, and process large amounts of complex high-dimensional data, such as digital images, surveillance videos, and DNA microarrays. Clustering becomes challenging due to the increasing sparsity of such data, as well as the increasing difficulty in discriminating distances between data points. This work presents a soft subspace clustering algorithm based on a self-organizing map (SOM) with time-variant structure, meaning that clustering data can be achieved without any prior knowledge such as the number of categories or input data topology, in which both are determined during the training process. The model also assigns different weights to different dimensions, this implies that every dimension contributes to uncover clusters. To validate the model, we used a number of real-world data sets, considering a diverse range of contexts such as data mining, gene expression, multi-view and computer vision problems. The promising results can handle real-world data characterized by high dimensionality.

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Les neurones du cortex visuel primaire (aire 17) du chat adulte répondent de manière sélective à différentes propriétés d’une image comme l’orientation, le contraste ou la fréquence spatiale. Cette sélectivité se manifeste par une réponse sous forme de potentiels d’action dans les neurones visuels lors de la présentation d’une barre lumineuse de forme allongée dans les champs récepteurs de ces neurones. La fréquence spatiale (FS) se mesure en cycles par degré (cyc./deg.) et se définit par la quantité de barres lumineuses claires et sombres présentées à une distance précise des yeux. Par ailleurs, jusqu’à récemment, l’organisation corticale chez l’adulte était considérée immuable suite à la période critique post-natale. Or, lors de l'imposition d'un stimulus non préféré, nous avons observé un phénomène d'entrainement sous forme d'un déplacement de la courbe de sélectivité à la suite de l'imposition d'une FS non-préférée différente de la fréquence spatiale optimale du neurone. Une deuxième adaptation à la même FS non-préférée induit une réponse neuronale différente par rapport à la première imposition. Ce phénomène de "gain cortical" avait déjà été observé dans le cortex visuel primaire pour ce qui est de la sélectivité à l'orientation des barres lumineuses, mais non pour la fréquence spatiale. Une telle plasticité à court terme pourrait être le corrélat neuronal d'une modulation de la pondération relative du poids des afférences synaptiques. / Primary visual cortex neurons in adult cat are selective to different image properties as orientation, contrast and spatial frequency. This selectivity is characterized by action potentials as electrical activity from the visual neurons. This response occurs during the presentation of a luminous bar in the receptive fields of the neurons. Spatial frequency is the amount of luminous bars in a grating presented from a precise distance from the eyes and is measured in cycles per degree. Furthermore, it was establish until recently that cortical organisation in the adult remains inflexible following the critical period after birth. However, our results have revealed that spatial frequency selectivity is able to change after an imposition of a non-preferred spatial frequency, also called adapter. Following cortical activity recordings, there is a shift of the spatial frequency tuning curves in the direction of the adapter. A second adaptation at the same non-preferred spatial frequency produced a different neural response from the first adaptation. This “short-term plasticity” was already observed in the primary visual cortex for orientation selective neurons but not yet for spatial frequency. The results presented in this study suggest that such plasticity is possible and that visual neurons regulate their electrical responses through modulation of the weights of their synaptic afferences.

Les neurones du cortex visuel primaire (aire 17) du chat adulte répondent de manière sélective à différentes propriétés d’une image comme l’orientation, le contraste ou la fréquence spatiale. Cette sélectivité se manifeste par une réponse sous forme de potentiels d’action dans les neurones visuels lors de la présentation d’une barre lumineuse de forme allongée dans les champs récepteurs de ces neurones. La fréquence spatiale (FS) se mesure en cycles par degré (cyc./deg.) et se définit par la quantité de barres lumineuses claires et sombres présentées à une distance précise des yeux. Par ailleurs, jusqu’à récemment, l’organisation corticale chez l’adulte était considérée immuable suite à la période critique post-natale. Or, lors de l'imposition d'un stimulus non préféré, nous avons observé un phénomène d'entrainement sous forme d'un déplacement de la courbe de sélectivité à la suite de l'imposition d'une FS non-préférée différente de la fréquence spatiale optimale du neurone. Une deuxième adaptation à la même FS non-préférée induit une réponse neuronale différente par rapport à la première imposition. Ce phénomène de "gain cortical" avait déjà été observé dans le cortex visuel primaire pour ce qui est de la sélectivité à l'orientation des barres lumineuses, mais non pour la fréquence spatiale. Une telle plasticité à court terme pourrait être le corrélat neuronal d'une modulation de la pondération relative du poids des afférences synaptiques. / Primary visual cortex neurons in adult cat are selective to different image properties as orientation, contrast and spatial frequency. This selectivity is characterized by action potentials as electrical activity from the visual neurons. This response occurs during the presentation of a luminous bar in the receptive fields of the neurons. Spatial frequency is the amount of luminous bars in a grating presented from a precise distance from the eyes and is measured in cycles per degree. Furthermore, it was establish until recently that cortical organisation in the adult remains inflexible following the critical period after birth. However, our results have revealed that spatial frequency selectivity is able to change after an imposition of a non-preferred spatial frequency, also called adapter. Following cortical activity recordings, there is a shift of the spatial frequency tuning curves in the direction of the adapter. A second adaptation at the same non-preferred spatial frequency produced a different neural response from the first adaptation. This “short-term plasticity” was already observed in the primary visual cortex for orientation selective neurons but not yet for spatial frequency. The results presented in this study suggest that such plasticity is possible and that visual neurons regulate their electrical responses through modulation of the weights of their synaptic afferences.