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

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.

<p><a></a><a>The visual system is capable of interpreting immense sensory complexity, allowing us to quickly identify behaviorally relevant stimuli in the environment. It performs this task with a hierarchical organization that works to detect, relay, and integrate visual stimulus features into an interpretable form. To understand the complexities of this system, visual neuroscientists have benefited from the many advantages of using mice as visual models. Despite their poor visual acuity, these animals possess surprisingly complex visual systems, and have been instrumental in understanding how visual features are processed in the primary visual cortex (V1). However, a growing body of literature has shown that primary sensory areas like V1 are capable of more than basic feature detection, but can express neural activity patterns related to learning, memory, categorization, and prediction. </a></p> <p>Visual experience fundamentally changes the encoding and perception of visual stimuli at many scales, and allows us to become familiar with environmental cues. However, the neural processes that govern visual familiarity are poorly understood. By exposing awake mice to repetitively presented visual stimuli over several days, we observed the emergence of low frequency oscillations in the primary visual cortex (V1). The oscillations emerged in population level responses known as visually evoked potentials (VEPs), as well as single-unit responses, and were not observed before the perceptual experience had occurred. They were also not evoked by novel visual stimuli, suggesting that they represent a new form of visual familiarity in the form of low frequency oscillations. The oscillations also required the muscarinic acetylcholine receptors (mAChRs) for their induction and expression, highlighting the importance of the cholinergic system in this learning and memory-based phenomenon. Ongoing visually evoked oscillations were also shown to increase the VEP amplitude of incoming visual stimuli if the stimuli were presented at the high excitability phase of the oscillations, demonstrating how neural activity with unique temporal dynamics can be used to influence visual processing.</p> <p>Given the necessity of perceptual experience for the strong expression of these oscillations and their dependence on the cholinergic system, it was clear we had discovered a phenomenon grounded in visual learning or memory. To further validate this, we characterized this response in a mouse model of Fragile X syndrome (FX), the most common inherited form of autism and a condition with known visual perceptual learning deficits. Using a multifaceted experimental approach, a number of neurophysiological differences were found in the oscillations displayed in FX mice. Extracellular recordings revealed shorter durations and lower power oscillatory activity in FX mice. Furthermore, we found that the frequency of peak oscillatory activity was significantly decreased in FX mice, demonstrating a unique temporal neural impairment not previously reported in FX. In collaboration with Dr. Christopher J. Quinn at Purdue, we performed functional connectivity analysis on the extracellularly recorded spikes from WT and FX mice. This analysis revealed significant impairments in functional connections from multiple layers in FX mice after the perceptual experience; some of which were validated by another graduate student (Qiuyu Wu) using Channelrhodopsin-2 assisted circuit mapping (CRACM). Together, these results shed new light on how visual stimulus familiarity is differentially encoded in FX via persistent oscillations, and allowed us to identify impairments in cross layer connectivity that may underlie these differences. </p> <p>Finally, we asked whether these oscillations are observable in other brain areas or are intrinsic to V1. Furthermore, we sought to determine if the oscillating unit populations in V1 possess uniform firing dynamics, or contribute differentially to the population level response. By performing paired recordings, we did not find prominent oscillatory activity in two visual thalamic nuclei (dLGN and LP) or a nonvisual area (RSC) connected to V1, suggesting the oscillations may not propagate with similar dynamics via cortico-thalamic connections or retrosplenial connections, <a>but may either be uniquely distributed across the visual hierarchy or predominantly</a> restricted to V1. Using K-means clustering on a large population of oscillating units in V1, we found unique temporal profiles of visually evoked responses, demonstrating distinct contributions of different unit sub-populations to the oscillation response dynamics.</p>