The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).

This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream -- from V1, V2, V4 and to IT -- is to discount image transformations, after learning them during development

Finding AGN remnant candidates based on radio morphology with machine learning

Remnant radio galaxies represent the dying phase of radio-loud active galactic nuclei (AGN). Large samples of remnant radio galaxies are important for quantifying the radio galaxy life cycle. The remnants of radio-loud AGN can be identified in radio sky surveys based on their spectral index, or, complementary, through visual inspection based on their radio morphology. However, this is extremely time-consuming when applied to the new large and sensitive radio surveys. Here we aim to reduce the amount of visual inspection required to find AGN remnants based on their morphology, through supervised machine learning trained on an existing sample of remnant candidates. For a dataset of 4107 radio sources, with angular sizes larger than 60 arcsec, from the LOw Frequency ARray (LOFAR) Two-Metre Sky Survey second data release (LoTSS-DR2), we started with 151 radio sources that were visually classified as 'AGN remnant candidate'. We derived a wide range of morphological features for all radio sources from their corresponding Stokes-I images: from simple source catalogue-derived properties, to clustered Haralick-features, and self-organising map (SOM) derived morphological features. We trained a random forest classifier to separate the 'AGN remnant candidates' from the not yet inspected sources. The SOM-derived features and the total to peak flux ratio of a source are shown to be most salient to the classifier. We estimate that $31\pm5\%$ of sources with positive predictions from our classifier will be labelled 'AGN remnant candidates' upon visual inspection, while we estimate the upper bound of the $95\%$ confidence interval for 'AGN remnant candidates' in the negative predictions at $8\%$. Visual inspection of just the positive predictions reduces the number of radio sources requiring visual inspection by $73\%$.Comment: 23 pages; accepted for publication in A&

Complete lattice projection autoassociative memories

Orientador: Marcos Eduardo Ribeiro do Valle MesquitaTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: A capacidade do cérebro humano de armazenar e recordar informações por associação tem inspirado o desenvolvimento de modelos matemáticos referidos na literatura como memórias associativas. Em primeiro lugar, esta tese apresenta um conjunto de memórias autoassociativas (AMs) que pertecem à ampla classe das memórias morfológicas autoassociativas (AMMs). Especificamente, as memórias morfológicas autoassociativas de projeção max-plus e min-plus (max-plus e min-plus PAMMs), bem como suas composições, são introduzidas nesta tese. Tais modelos podem ser vistos como versões não distribuídas das AMMs propostas por Ritter e Sussner. Em suma, a max-plus PAMM produz a maior combinação max-plus das memórias fundamentais que é menor ou igual ao padrão de entrada. Dualmente, a min-plus PAMM projeta o padrão de entrada no conjunto de todas combinações min-plus. Em segundo, no contexto da teoria dos conjuntos fuzzy, esta tese propõe novas memórias autoassociativas fuzzy, referidas como classe das max-C e min-D FPAMMs. Uma FPAMM representa uma rede neural morfológica fuzzy com uma camada oculta de neurônios que é concebida para o armazenamento e recordação de conjuntos fuzzy ou vetores num hipercubo. Experimentos computacionais relacionados à classificação de padrões e reconhecimento de faces indicam possíveis aplicações dos novos modelos acima mencionadosAbstract: The human brain¿s ability to store and recall information by association has inspired the development various mathematical models referred to in the literature as associative memories. Firstly, this thesis presents a set of autoassociative memories (AMs) that belong to the broad class of autoassociative morphological memories (AMMs). Specifically, the max-plus and min-plus projection autoassociative morphological memories (max-plus and min-plus PAMMs), as well as their compositions, are introduced in this thesis. These models are non-distributed versions of the AMM models developed by Ritter and Sussner. Briefly, the max-plus PAMM yields the largest max-plus combination of the stored vectors which is less than or equal to the input pattern. Dually, the min-plus PAMM projects the input pattern into the set of all min-plus combinations. In second, in the context of fuzzy set theory, this thesis proposes new fuzzy autoassociative memories mentioned as class of the max-C and min-D FPAMMs. A FPAMM represents a fuzzy morphological neural network with a hidden layer of neurons that is designed for the storage and retrieval of fuzzy sets or vectors on a hypercube. Computational experiments concerning pattern classification and face recognition indicate possible applications of the aforementioned new AM modelsDoutoradoMatematica AplicadaDoutor em Matemática AplicadaCAPE

Network analysis of the cellular circuits of memory

Intuitively, memory is conceived as a collection of static images that we accumulate as we experience the world. But actually, memories are constantly changing through our life, shaped by our ongoing experiences. Assimilating new knowledge without corrupting pre-existing memories is then a critical brain function. However, learning and memory interact: prior knowledge can proactively influence learning, and new information can retroactively modify memories of past events. The hippocampus is a brain region essential for learning and memory, but the network-level operations that underlie the continuous integration of new experiences into memory, segregating them as discrete traces while enabling their interaction, are unknown. Here I show a network mechanism by which two distinct memories interact. Hippocampal CA1 neuron ensembles were monitored in mice as they explored a familiar environment before and after forming a new place-reward memory in a different environment. By employing a network science representation of the co-firing relationships among principal cells, I first found that new associative learning modifies the topology of the cells’ co-firing patterns representing the unrelated familiar environment. I fur- ther observed that these neuronal co-firing graphs evolved along three functional axes: the first segregated novelty; the second distinguished individual novel be- havioural experiences; while the third revealed cross-memory interaction. Finally, I found that during this process, high activity principal cells rapidly formed the core representation of each memory; whereas low activity principal cells gradually joined co-activation motifs throughout individual experiences, enabling cross-memory in- teractions. These findings reveal an organizational principle of brain networks where high and low activity cells are differentially recruited into coactivity motifs as build- ing blocks for the flexible integration and interaction of memories. Finally, I employ a set of manifold learning and related approaches to explore and characterise the complex neural population dynamics within CA1 that underlie sim- ple exploration.Open Acces