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

Direct and indirect cholinergic septo-hippocampal pathways cooperate to structure spiking activity in the hippocampus

The medial septum/vertical diagonal band of Broca complex (MSvDB) is a key structure that modulates hippocampal rhythmogenesis. Cholinergic neurons of the MSvDB play a central role in generating and pacing theta-band oscillations in the hippocampal formation during exploration, novelty detection, and memory encoding. However, how precisely cholinergic neurons affect hippocampal oscillatory activity and spiking rates of hippocampal neurons in vivo, has remained elusive. I therefore used silicon probe recordings of local field potentials and unit activity in the dorsal hippocampus in combination with cell type specific optogenetic activation of cholinergic MSvDB neurons to study the effects of synaptically released acetylcholine on hippocampal network activity in urethane-anesthetized mice.In vivo optogenetic activation of cholinergic MSvDB neurons induced hippocampal rhythmogenesis at the theta (3-6 Hz) and slow gamma (26-48 Hz) frequency range with a suppression of peri-theta frequencies. Interestingly, this effect was independent from the stimulation frequency. In addition, stimulation of cholinergic MSvDB neurons resulted in a net increase of interneuron firing with a concomitant net decrease of principal cell firing in the hippocampal CA3 subfield. I used focal injections of cholinergic blockers either into the MSvDB or the hippocampus to demonstrate that cholinergic MSvDB neurons modulate hippocampal network activity via two distinct pathways. Focal injection of a cholinergic blocker cocktail into the hippocampus strongly diminished the cholinergic stimulation-induced spiking rate modulation of hippocampal interneurons and principal cells. This demonstrates that modulation of neuronal activity in hippocampal subfield CA3 by cholinergic MSvDB neurons is mediated via direct septo-hippocampal projections. In contrast, focal injection of atropine, a blocker of the muscarinic type of acetylcholine receptors, into the MSvDB had no effect on spiking rate modulation in CA3, but abolished hippocampal theta synchronization. This strongly suggests that activity of an indirect septo-hippocampal pathway induces hippocampal theta oscillations via an intraseptal relay. Furthermore, cholinergic neurons depolarized parvalbumin-positive (PV+) GABAergic neurons within the MSvDB in vitro, and optogenetic activation of these fast spiking neurons in vivo induced hippocampal rhythmic activity precisely at the stimulation frequency. Taken together, these data suggest an intraseptal relay with a strong contribution of PV+ GABAergic MSvDB neurons in pacing hippocampal theta oscillations. Activation of both the direct and indirect pathways causes a reduction in CA3 pyramidal neuron firing and a more precise coupling to theta oscillatory phase with CA3 interneurons preferentially firing at the descending phase and CA3 principal neurons preferentially firing near the trough of the ongoing theta oscillation recorded at the pyramidal cell layer. The two identified anatomically and functionally distinct pathways are likely relevant for cholinergic control of encoding vs. retrieval modes in the hippocampus

Two photon interrogation of hippocampal subregions CA1 and CA3 during spatial behaviour

The hippocampus is crucial for spatial navigation and episodic memory formation. Hippocampal place cells exhibit spatially selective activity within an environment and form the neural basis of a cognitive map of space which supports these mnemonic functions. Hebb’s (1949) postulate regarding the creation of cell assemblies is seen as the pre-eminent model of learning in neural systems. Investigating changes to the hippocampal representation of space during an animal’s exploration of its environment provides an opportunity to observe Hebbian learning at the population and single cell level. When exploring new environments animals form spatial memories that are updated with experience and retrieved upon re-exposure to the same environment, but how this is achieved by different subnetworks in hippocampal CA1 and CA3, and how these circuits encode distinct memories of similar objects and events remains unclear. To test these ideas, we developed an experimental strategy and detailed protocols for simultaneously recording from CA1 and CA3 populations with 2P imaging. We also developed a novel all-optical protocol to simultaneously activate and record from ensembles of CA3 neurons. We used these approaches to show that targeted activation of CA3 neurons results in an increasing excitatory amplification seen only in CA3 cells when stimulating other CA3 cells, and not in CA1, perhaps reflecting the greater number of recurrent connections in CA3. To probe hippocampal spatial representations, we titrated input to the network by morphing VR environments during spatial navigation to assess the local CA3 as well as downstream CA1 responses. To this end, we found CA1 and CA3 neural population responses behave nonlinearly, consistent with attractor dynamics associated with the two stored representations. We interpret our findings as supporting classic theories of Hebbian learning and as the beginning of uncovering the relationship between hippocampal neural circuit activity and the computations implemented by their dynamics. Establishing this relationship is paramount to demystifying the neural underpinnings of cognition

Exploring Language Mechanisms: The Mass-Count Distinction and The Potts Neural Network

The aim of this thesis is to explore language mechanisms in two aspects. First, the statistical properties of syntax and semantics, and second, the neural mechanisms which could be of possible use in trying to understand how the brain learns those particular statistical properties. In the first part of the thesis (part A) we focus our attention on a detailed statistical study of the syntax and semantics of the mass-count distinction in nouns. We collected a database of how 1,434 nouns are used with respect to the mass-count distinction in six languages; additional informants characterised the semantics of the underlying concepts. Results indicate only weak correlations between semantics and syntactic usage. The classification rather than being bimodal, is a graded distribution and it is similar across languages, but syntactic classes do not map onto each other, nor do they reflect, beyond weak correlations, semantic attributes of the concepts. These findings are in line with the hypothesis that much of the mass/count syntax emerges from language- and even speaker-specific grammaticalisation. Further, in chapter 3 we test the ability of a simple neural network to learn the syntactic and semantic relations of nouns, in the hope that it may throw some light on the challenges in modelling the acquisition of the mass-count syntax. It is shown that even though a simple self-organising neural network is insufficient to learn a mapping implementing a syntactic- semantic link, it does however show that the network was able to extract the concept of 'count', and to some extent that of \u2018mass\u2019 as well, without any explicit definition, from both the syntactic and from the semantic data. The second part of the thesis (part B) is dedicated to studying the properties of the Potts neural network. The Potts neural network with its adaptive dynamics represents a simplified model of cortical mechanisms. Among other cognitive phenomena, it intends to model language production by utilising the latching behaviour seen in the network. We expect that a model of language processing should robustly handle various syntactic- semantic correlations amongst the words of a language. With this aim, we test the effect on storage capacity of the Potts network when the memories stored in it share non trivial correlations. Increase in interference between stored memories due to correlations is studied along with modifications in learning rules to reduce the interference. We find that when strongly correlated memories are incorporated in the storage capacity definition, the network is able to regain its storage capacity for low sparsity. Strong correlations also affect the latching behaviour of the Potts network with the network unable to latch from one memory to another. However latching is shown to be restored by modifying the learning rule. Lastly, we look at another feature of the Potts neural network, the indication that it may exhibit spin-glass characteristics. The network is consistently shown to exhibit multiple stable degenerate energy states other than that of pure memories. This is tested for different degrees of correlations in patterns, low and high connectivity, and different levels of global and local noise. We state some of the implications that the spin-glass nature of the Potts neural network may have on language processing

Loughborough University Spontaneous Expression Database and baseline results for automatic emotion recognition

The study of facial expressions in humans dates back to the 19th century and the study of the emotions that these facial expressions portray dates back even further. It is a natural part of non-verbal communication for humans to pass across messages using facial expressions either consciously or subconsciously, it is also routine for other humans to recognize these facial expressions and understand or deduce the underlying emotions which they represent. Over two decades ago and following technological advances, particularly in the area of image processing, research began into the use of machines for the recognition of facial expressions from images with the aim of inferring the corresponding emotion. Given a previously unknown test sample, the supervised learning problem is to accurately determine the facial expression class to which the test sample belongs using the knowledge of the known class memberships of each image from a set of training images. The solution to this problem building an effective classifier to recognize the facial expression is hinged on the availability of representative training data. To date, much of the research in the area of Facial Expression Recognition (FER) is still based on posed (acted) facial expression databases, which are often exaggerated and therefore not representative of real life affective displays, as such there is a need for more publically accessible spontaneous databases that are well labelled. This thesis therefore reports on the development of the newly collected Loughborough University Spontaneous Expression Database (LUSED); designed to bolster the development of new recognition systems and to provide a benchmark for researchers to compare results with more natural expression classes than most existing databases. To collect the database, an experiment was set up where volunteers were discretely videotaped while they watched a selection of emotion inducing video clips. The utility of the new LUSED dataset is validated using both traditional and more recent pattern recognition techniques; (1) baseline results are presented using the combination of Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLDA) and their kernel variants Kernel Principal Component Analysis (KPCA), Kernel Fisher Discriminant Analysis (KFDA) with a Nearest Neighbour-based classifier. These results are compared to the performance of an existing natural expression database Natural Visible and Infrared Expression (NVIE) database. A scheme for the recognition of encrypted facial expression images is also presented. (2) Benchmark results are presented by combining PCA, FLDA, KPCA and KFDA with a Sparse Representation-based Classifier (SRC). A maximum accuracy of 68% was obtained recognizing five expression classes, which is comparatively better than the known maximum for a natural database; around 70% (from recognizing only three classes) obtained from NVIE

Handshape recognition using principal component analysis and convolutional neural networks applied to sign language

Handshape recognition is an important problem in computer vision with significant societal impact. However, it is not an easy task, since hands are naturally deformable objects. Handshape recognition contains open problems, such as low accuracy or low speed, and despite a large number of proposed approaches, no solution has been found to solve these open problems. In this thesis, a new image dataset for Irish Sign Language (ISL) recognition is introduced. A deeper study using only 2D images is presented on Principal Component Analysis (PCA) in two stages. A comparison between approaches that do not need features (known as end-to-end) and feature-based approaches is carried out. The dataset was collected by filming six human subjects performing ISL handshapes and movements. Frames from the videos were extracted. Afterwards the redundant images were filtered with an iterative image selection process that selects the images which keep the dataset diverse. The accuracy of PCA can be improved using blurred images and interpolation. Interpolation is only feasible with a small number of points. For this reason two-stage PCA is proposed. In other words, PCA is applied to another PCA space. This makes the interpolation possible and improves the accuracy in recognising a shape at a translation and rotation unknown in the training stage. Finally classification is done with two different approaches: (1) End-to-end approaches and (2) feature-based approaches. For (1) Convolutional Neural Networks (CNNs) and other classifiers are tested directly over raw pixels, whereas for (2) PCA is mostly used to extract features and again different algorithms are tested for classification. Finally, results are presented showing accuracy and speed for (1) and (2) and how blurring affects the accuracy

The role of A2A receptors in cognitive decline : decoding the molecular shift towards neurodegeneration

Aging is associated with cognitive decline both in humans and animals. Importantly, aging is the main risk factor for nerurodegenerative diseases, namely Alzheimer’s disease (AD), which primarily affects synapses in the temporal lobe and hippocampal formation. In fact, synaptic dysfunction plays a central role in AD, since it drives cognitive decline. Indeed, in age-related neurodegeneration, cognitive decline has a stronger correlation to early synapse loss than neuronal loss in patients. Despite the many clinical trials conducted to identify drug targets that could reduce protein toxicity in AD, such targets and strategies have proven unsuccessful. Therefore, efforts focused on identifying the early mechanisms of disease pathogenesis, driven or exacerbated by the aging process, may prove more relevant to slow the progression rather than the current disease-based models. A recent genetic study discovered a significant association of the adenosine A2A receptor encoding gene (ADORA2A) with hippocampal volume in mild cognitive impairment and Alzheimer’s disease. There is compelling evidence from animal models of a cortical and hippocampal upsurge of adenosine A2A receptors (A2AR) in glutamatergic synapses upon aging and AD. Importantly, the blockade of A2AR prevents hippocampus-dependent memory deficits and synaptic impairments in aged animals and in several AD models. Accordingly, in humans, several epidemiological studies have shown that regular caffeine consumption attenuates memory disruption during aging and decreases the risk of developing memory impairments in AD patients. Together, these data suggest that A2AR might be a good candidate as trigger to synaptic dysfunction in aging and AD. The main goal of this dissertation was then to explore the synaptic function of A2AR in age-related conditions. We have assessed the A2AR expression in human hippocampal slices and found a significant upsurge of A2AR in hippocampal neurons of aged humans, a phenotype aggravated in AD patients. Increased selective expression of A2AR driven by the CaMKII promoter in rat forebrain neurons was sufficient to mimic aging-like memory impairments, assessed by the Morris water maze task, and to uncover an LTD-to-LTP shift in the Schaffer collaterals-CA1 synapse of hippocampus. This shift was due to an increased NMDA receptor gating and associated to increased Ca2+ influx. The mGluR5-NMDAR interplay was identified as a key event in A2AR-induced synaptic dysfunction. Moreover, chronic treatment with an A2AR selective antagonist, orally delivered for one month, rescued the aberrant NMDAR overactivation and the plasticity shift. Importantly, the same LTD-to-LTP shift was observed in memory-impaired aged rats and APP/PS1 mice modeling AD, a phenotype rescued upon A2AR blockade. These data support a key role for over-active hippocampal A2AR in aging and AD-dependent synaptic and cognitive dysfunction and may underlie the significant genetic association of ADORA2A with AD. Importantly, this newly found interaction might prove a suitable alternative for regulating aberrant mGluR5/NMDAR signaling in AD without disrupting their constitutive activity

Virtual Morphology and Evolutionary Morphometrics in the new millenium.

EDITED VOLUME; abstract N/

Investigating the role of fast-spiking interneurons in neocortical dynamics

PhD ThesisFast-spiking interneurons are the largest interneuronal population in neocortex. It is well documented that this population is crucial in many functions of the neocortex by subserving all aspects of neural computation, like gain control, and by enabling dynamic phenomena, like the generation of high frequency oscillations. Fast-spiking interneurons, which represent mainly the parvalbumin-expressing, soma-targeting basket cells, are also implicated in pathological dynamics, like the propagation of seizures or the impaired coordination of activity in schizophrenia. In the present thesis, I investigate the role of fast-spiking interneurons in such dynamic phenomena by using computational and experimental techniques. First, I introduce a neural mass model of the neocortical microcircuit featuring divisive inhibition, a gain control mechanism, which is thought to be delivered mainly by the soma-targeting interneurons. Its dynamics were analysed at the onset of chaos and during the phenomena of entrainment and long-range synchronization. It is demonstrated that the mechanism of divisive inhibition reduces the sensitivity of the network to parameter changes and enhances the stability and exibility of oscillations. Next, in vitro electrophysiology was used to investigate the propagation of activity in the network of electrically coupled fast-spiking interneurons. Experimental evidence suggests that these interneurons and their gap junctions are involved in the propagation of seizures. Using multi-electrode array recordings and optogenetics, I investigated the possibility of such propagating activity under the conditions of raised extracellular K+ concentration which applies during seizures. Propagated activity was recorded and the involvement of gap junctions was con rmed by pharmacological manipulations. Finally, the interaction between two oscillations was investigated. Two oscillations with di erent frequencies were induced in cortical slices by directly activating the pyramidal cells using optogenetics. Their interaction suggested the possibility of a coincidence detection mechanism at the circuit level. Pharmacological manipulations were used to explore the role of the inhibitory interneurons during this phenomenon. The results, however, showed that the observed phenomenon was not a result of synaptic activity. Nevertheless, the experiments provided some insights about the excitability of the tissue through scattered light while using optogenetics. This investigation provides new insights into the role of fast-spiking interneurons in the neocortex. In particular, it is suggested that the gain control mechanism is important for the physiological oscillatory dynamics of the network and that the gap junctions between these interneurons can potentially contribute to the inhibitory restraint during a seizure.Wellcome Trust