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The notion that learning and memory processes are highly dependent on central cholinergic neurotransmission has been widely accepted. However, studies documenting the importance of Acetylcholine (ACh) in attention have led some to suggest that attention impairments may underlie the deficits in learning and memory resulting from cholinergic disruptions. Using a visual discrimination task, I attempted to discern whether performance impairments by Scopolamine were predominantly due to the importance of muscarinic receptor integrity in attention, or memory consolidation in learning. Rats were trained in a visual discrimination task using a Y-shaped water maze apparatus. To successfully navigate to a hidden platform located in one of the two goal arms, rats learned to discriminate between 2 distinct visual cues, indicating the platform’s presence (CS+) or absence (CS-), respectively. Following task acquisition, testing continued using a combination of Regular trials (RT; both CS+ and CS- present) and Probe trials (PT; only one of the cues present). Results indicated that performance on PT was impaired due to greater task difficulty under conditions of reduced information, while Scopolamine (1 mg/kg) further impacted PT performance without affecting RTs. In a second experiment, PTs were administered with the platform present to provide reinforcement and a learning opportunity. Animals still exhibited poorer PT performance, but rapidly learned to rely on a single cue for accurate platform localization. Interestingly, this learning was not apparent under conditions of Scopolamine treatment (1 mg/kg), even though RT performance was completely unaffected. To examine experience-dependent changes in neuronal responding after visual discrimination learning, a subset of animals were anesthetised and visual evoked potentials (VEPs) in V1 and area CA1 of the hippocampus were recorded in response to CS+, CS-, and novel stimuli. In both the V1 and CA1, the VEP amplitudes elicited to familiar and novel stimuli were not significantly different. First, these experiments demonstrate the importance of the cholinergic system in sustaining visual attention and acquiring a new single-cue strategy. Furthermore, the null electrophysiology findings do not rule out the plastic response properties of the mature V1 and CA1, but remind us of the complex nature of memory encoding in the brain. / Thesis (Master, Psychology) -- Queen's University, 2011-09-16 13:50:24.045

A neurociência computacional é uma vasta área que tem como objeto de estudo o entendimento ou a emulação da dinâmica cerebral em diversos níveis. Neste trabalho atenta-se ao estudo da dinâmica de neurônios, os quais, no consenso atual, acredita-se serem as unidades fundamentais do processamento cerebral. A importância do estudo sobre o comportamento de neurônios se encontra na diversidade de propriedades que eles podem apresentar. O estudo se torna mais rico quando há interações de sistemas internos ao neurônio em diferentes escalas de tempo, criando propriedades como adaptação, latência e comportamento em rajada, o que pode acarretar em diferentes papéis que os neurônios podem ter na rede. Nesta dissertação é feita uma análise sob o ponto de vista de sistemas dinâmicos e de análise de sensibilidade de seis modelos ao estilo de Hodgkin-Huxley e compartimentais de neurônios encontrados no córtex visual primário de mamíferos. Esses modelos correspondem a seis classes eletrofisiológicas de neurônios corticais e o estudo feito nesta dissertação oferece uma contribuição ao entendimento dos princípios de sistemas dinâmicos subjacentes a essa classificação. / Computational neuroscience is a vast scientific area which has as subject of study the unsderstanding or emulation of brain dynamics at different levels. This work studies the dynamics of neurons, which are believed, according to present consensus, to be the fundamental processing units of the brain. The importance of studying neuronal behavior comes from the diversity of properties they may have. This study becomes richer when there are interactions between distintic neuronal internal systems, in different time scales, creating properties like adaptation, latency and bursting, resulting in different roles that neurons may have in the network. This dissertation contains a study of six reduced compartmental conductance-based models of neurons found in the primary visual cortex of mammals under the dynamical systems and sensitivity analysis viewpoints. These models correspond to six eletrophysiological classes of cortical neurons and this dissertation offers a contribution to the understanding of the dynamical-systems principles underlying such classification.

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<p>Intricate neural circuits underlie all brain functions. However, these neural circuits are highly dynamic. The ability to change, or the plasticity, of the brain has long been demonstrated at the level of isolated single synapses under artificial conditions. Circuit organization and brain function has been extensively studied by correlating neuronal activity with information input. The primary visual cortex has become an important model brain region for the study of sensory processing, in large part due to the ease of manipulating visual stimuli. Much has been learned from studies of visual cortex focused on understanding the signal-processing of visual inputs within neural circuits. Many of these findings are generalizable to other sensory systems and other regions of cortex. However, few studies have directly demonstrated the orchestrated neural-circuit plasticity occurring during behavioral experience. </p> <p>It is vital to measure the precise circuit connectivity and to quantitatively characterize experience-dependent circuit plasticity to understand the processes of learning and memory formation. Moreover, it is important to study how circuit connectivity and plasticity in neurological and psychiatric disease states deviates from that in healthy brains. By understanding the impact of disease on circuit plasticity, it may be possible to develop therapeutic interventions to alleviate significant neurological and psychiatric morbidity. In the case of neural trauma or ischemic injury, where neurons and their connections are lost, functional recovery relies on neural-circuit repair. Evaluating whether neurons are reconnected into the local circuitry to re-establish the lost connectivity is crucial for guiding therapeutic development.</p> <p>There are several major technical hurdles for studies aiming to quantify circuit connectivity. First, the lack of high-specificity circuit stimulation methods and second, the low throughput of the gold-standard patch-clamp technique for measuring synaptic events have limited progress in this area. To address these problems, we first engineered the patch-clamp experimental system to automate the patching process, increasing the throughput and consistency of patch-clamp electrophysiology while retaining compatibility of the system for experiments in <i>ex vivo </i>brain slices. We also took advantage of optogenetics, the technology that enables control of neural activity with light through ectopic expression of genetically encoded photo-sensitive channels in targeted neuronal populations. Combining optogenetic stimulation of pre-synaptic axonal terminals and whole-cell patch-clamp recording of post-synaptic currents, we mapped the distribution and strength of synaptic connections from a specific group of neurons onto a single cell. With the improved patch-clamp efficiency using our automated system, we efficiently mapped a significant number of neurons in different experimental conditions/treatments. This approach yielded large datasets, with sufficient power to make meaningful comparisons between groups.</p> <p>Using this method, we first studied visual experience-dependent circuit plasticity in the primary visual cortex. We measured the connectivity of local feedback and recurrent neural projections in a Fragile X syndrome mouse model and their healthy counterparts, with or without a specific visual experience. We found that repeated visual experience led to increased excitatory drive onto inhibitory interneurons and intrinsically bursting neurons in healthy animals. Potentiation at these synapses was absent or abnormal in Fragile X animals. Furthermore, recurrent excitatory input onto regular spiking neurons within the same layer remained stable in healthy animals but was depressed in Fragile X animals following repeated visual experience. These results support the hypothesis that visual experience leads to selective circuit plasticity which may underlie the mechanism of visual learning. This circuit plasticity process is impaired in a mouse model of Fragile X syndrome. </p> <p>In a separate study, in collaboration with the laboratory of Dr. Gong Chen, we applied the circuit-mapping method to measure the effect of a novel brain-repair therapy on functional circuit recovery following ischemic injury, which locally kills neurons and creates a glial scar. By directly reprogramming astrocytes into neurons within the region of the glial scar, this gene-therapy technology aims to restore the local circuit and thereby dramatically improve behavioral function after devastating neurological injury. We found that direct reprogramming converted astrocytes into neurons, and importantly, we found that these newly reprogrammed neurons integrated appropriately into the local circuit. The reprogramming also improved connections between surviving endogenous neurons at the injury site toward normal healthy levels of connectivity. Connections formed onto the newly reprogrammed neurons spontaneously remodeled, the process of which resembled neural development. By directly demonstrating functional connectivity of newly reprogrammed neurons, our results suggest that this direct reprogramming gene-therapy technology holds significant promise for future clinical application to restore circuit connectivity and neurological function following brain injury.</p>

Dans cette thèse, nous développons une approche supervisée de reconnaissance d’objets basée sur l’utilisation de nouveaux descripteurs d’images globaux inspirés du modèle du cortex visuel humain primaire V1 en tant que groupe de roto-translations semi-discrètes SE (2,N)=R² x ZN produit semi-direct entre R² et ZN. La méthode proposée est basée sur des descripteurs de Fourier généralisés et rotationnels définis sur le groupe SE (2,N), qui sont invariants aux transformations géométriques (translations, et rotations). De plus, nous montrons que ces descripteur de Fourier sont faiblement complets, dans le sens qu’ils permettent de discriminer sur un ensemble ouvert et dense L² (SE(2,N)) de fonctions à support compact, donc distinguer entre des images réelles. Ces descripteurs sont ensuite utilisés pour alimenter un classifieur de type SVM dans le cadre de la reconnaissance d’objets. Nous avons mené une séries d’expérimentations dans le but d’évaluer notre méthode sur les bases de visages RL, CVL et ORL et sur la base d’images d’objets variés COIL-100, et de comparer ses performances à celles des méthodes basées sur des descripteurs globaux et locaux. Les résultats obtenus ont montré que notre approche est en mesure de concurrencer de nombreuses techniques de reconnaissance d’objets existantes et de surpasser de nombreuse autres. Ces résultats ont également montré que notre méthode est robuste aux bruits. Enfin, nous avons employé la technique proposée pour reconnaître des navires dans un contexte de surveillance maritime. / In this thesis, we develop a supervised object recognition method using new global image descriptors inspired by the model of the human primary visual cortex V1. Mathematically speaking, the latter is modeled as the semi-discrete roto-translation group SE (2,N)=R² x ZN semi-direct product between R² and ZN. Therefore, our technique is based on generalized and rotational Fourier descriptors defined in SE (2,N) , and which are invariant to natural geometric transformations (translations, and rotations). Furthermore, we show that such Fourier descriptors are weakly complete, in the sense that they allow to distinguish over an open and dense set of compactly supported functions in L² (SE(2,N)) , hence between real-world images. These descriptors are later used in order to feed a Support Vector Machine (SVM) classifier for object recognition purposes. We have conducted a series of experiments aiming both at evaluating and comparing the performances of our method against existing both local - and global - descriptor based state of the art techniques, using the RL, the CVL, and the ORL face databases, and the COIL-100 image database (containing various types of objects). The obtained results have demonstrated that our approach was able to compete with many existing state of the art object recognition techniques, and to outperform many others. These results have also shown that our method is robust to noise. Finally, we have applied the proposed method on vessels recognition in the framework of maritime surveillance.

Stimuli outside classical receptive fields have been shown to exert significant influence over the activities of neurons in primary visual cortexWe propose that contextual influences are used for pre-attentive visual segmentation, in a new framework called segmentation without classification. This means that segmentation of an image into regions occurs without classification of features within a region or comparison of features between regions. This segmentation framework is simpler than previous computational approaches, making it implementable by V1 mechanisms, though higher leve l visual mechanisms are needed to refine its output. However, it easily handles a class of segmentation problems that are tricky in conventional methods. The cortex computes global region boundaries by detecting the breakdown of homogeneity or translation invariance in the input, using local intra-cortical interactions mediated by the horizontal connections. The difference between contextual influences near and far from region boundaries makes neural activities near region boundaries higher than elsewhere, making boundaries more salient for perceptual pop-out. This proposal is implemented in a biologically based model of V1, and demonstrated using examples of texture segmentation and figure-ground segregation. The model performs segmentation in exactly the same neural circuit that solves the dual problem of the enhancement of contours, as is suggested by experimental observations. Its behavior is compared with psychophysical and physiological data on segmentation, contour enhancement, and contextual influences. We discuss the implications of segmentation without classification and the predictions of our V1 model, and relate it to other phenomena such as asymmetry in visual search.

L'intégration sensorielle nécessite un alignement correct des cartes nerveuses dans le cerveau. Dans les couches superficielles du colliculus supérieur, situé dans le mésencéphale, des projections en provenance de la rétine ainsi que du cortex visuel primaire doivent être alignés, mais les mécanismes sous-jacents de ce processus demeurent à ce jour méconnus. Afin d'élucider ces mécanismes, éphrine-A3 a été sur-exprimée dans un modèle murin, dans une sous-population des cellules ganglionnaires de la rétine, induisant un disruption de l'alignement de la carte rétino-colliculaire sur la carte cortico-colliculaire. L'inactivation in vivo d'éphrine-A3 dans la rétine restaure un phénotype sauvage. Une analyse théorique utilisant un modèle informatique a permis la modélisation des donnés obtenues. Ces résultats ont permis l'identification d'un principe de base dans l'alignement des cartes et des mécanismes associés, validés par un modèle théorique. / Efficient sensory processing requires correct alignment of neural maps throughout the brain. In the superficial layers of the superior colliculus in the midbrain, projections from retinal ganglion cells and V1 cortex must be aligned to form a visuotopic map, but the basic principle and underlying mechanism are elusive and still incomplete. In a new mouse model, over-expression of ephrin-A3 in a subset of retinal ganglion cells disrupts the cortico-collicular map alignment onto the retino-collicular map, creating a visuotopic mismatch. In vivo inactivation of retinal ephrin-A3 over-expression restores a wild-type corticocollicular map. Theoretical analyses using an original algorithm models the stochastic nature of maps formation and alignment, and recapitulates our observations. Our results identify a basic principle for the alignment of converging maps and the associated mechanism, validated by a theoretical model.

Face recognition that is invariant to pose and illumination is a problem solved effortlessly by the human brain, but the computational details that underlie such efficient recognition are still far from clear. This thesis draws on research from psychology and neuroscience about face and object recognition and the visual system in order to develop a novel computational method for face detection, feature selection and representation, and memory structure for recall. A biologically plausible framework for developing a face recognition system will be presented. This framework can be divided into four parts: 1) A face detection system. This is an improved version of a biologically inspired feedforward neural network that has modifiable connections and reflects the hierarchical and elastic structure of the visual system. The face detection system can detect if a face is present in an input image, and determine the region which contains that face. The system is also capable of detecting the pose of the face. 2) A face region selection mechanism. This mechanism is used to determine the Gabor-style features corresponding to the detected face, i.e., the features from the region of interest. This region of interest is selected using a feedback mechanism that connects the higher level layer of the feedforward neural network where ultimately the face is detected to an intermediate level where the Gabor style features are detected. 3) A face recognition system which is based on the binary encoding of the Gabor style features selected to represent a face. Two alternative coding schemes are presented, using 2 and 4 bits to represent a winning orientation at each location. The effectiveness of the Gabor-style features and the different coding schemes in discriminating faces from different classes is evaluated using the Yale B Face Database. The results from this evaluation show that this representation is close to other results on the same database. 4) A theoretical approach for a memory system capable of memorising sequences of poses. A basic network for memorisation and recall of sequences of labels have been implemented, and from this it is extrapolated a memory model that could use the ability of this model to memorise and recall sequences, to assist in the recognition of faces by memorising sequences of poses. Finally, the capabilities of the detection and recognition parts of the system are demonstrated using a demo application that can learn and recognise faces from a webcam.