History and Philosophy of Neural Networks

This chapter conceives the history of neural networks emerging from two millennia of attempts to rationalise and formalise the operation of mind. It begins with a brief review of early classical conceptions of the soul, seating the mind in the heart; then discusses the subsequent Cartesian split of mind and body, before moving to analyse in more depth the twentieth century hegemony identifying mind with brain; the identity that gave birth to the formal abstractions of brain and intelligence we know as ‘neural networks’. The chapter concludes by analysing this identity - of intelligence and mind with mere abstractions of neural behaviour - by reviewing various philosophical critiques of formal connectionist explanations of ‘human understanding’, ‘mathematical insight’ and ‘consciousness’; critiques which, if correct, in an echo of Aristotelian insight, sug- gest that cognition may be more profitably understood not just as a result of [mere abstractions of] neural firings, but as a consequence of real, embodied neural behaviour, emerging in a brain, seated in a body, embedded in a culture and rooted in our world; the so called 4Es approach to cognitive science: the Embodied, Embedded, Enactive, and Ecological conceptions of mind

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Toward a further understanding of object feature binding: a cognitive neuroscience perspective.

The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory. The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem

Toward a further understanding of object feature binding : a cognitive neuroscience perspective

The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory. The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Plasticité de la réponse aux orientations dans le cortex visuel primaire du chat par la méthode d'imagerie optique intrinsèque

Dans le cortex visuel primaire du chat (aires 17 et 18), les neurones répondant aux orientations présentes dans l’environnement (comme le contour des objets) sont organisés en colonnes perpendiculaires à la surface du cortex. Il a précédemment été montré qu'un changement drastique des orientations présentes dans l’environnement change la réponse des neurones. Par exemple, un neurone répondant à des orientations horizontales pourra répondre, après apprentissage d'un nouvel environnement, à des orientations obliques. Nous avons voulu, dans cette thèse, suivre les changements de propriétés de populations entières de neurones suite à ce type d'apprentissage. A cet effet, nous avons utilisé la technique d'imagerie optique des signaux intrinsèques, qui permet de mesurer l'activité d'une surface de cortex en utilisant le signal BOLD (blood-oxygen-level dependent). Cette thèse s'articule sur trois axes : l'effet de l'apprentissage au niveau local, l'effet de l’apprentissage à l'échelle de l'aire cérébrale, et la modélisation de l’apprentissage. Dans la première partie, nous avons comparé les changements d’orientations des neurones en fonction du gradient d’orientation local. Ce gradient est fort quand deux neurones voisins ont des orientations très différentes, et faible quand leurs orientations sont semblables. Les résultats montrent que plus les neurones sont entourés de neurones aux orientations différentes, plus l'apprentissage change leur réponse à l’orientation. Ceci suggère que les connexions locales ont une influence déterminante sur l'ampleur de l’apprentissage. Dans la deuxième partie, nous avons comparé le changement d’orientation des neurones des aires 17 et 18 avant et après apprentissage. Les résultats ne sont pas notablement différents entre les aires 17 et 18. On peut toutefois noter que les changements d’orientations dans l’aire 18 ont des amplitudes plus variables que dans l’aire 17. Ceci peut provenir du fait que l’aire 18 reçoit des afférences plus variées que l’aire 17, notamment une afférence directe des cellules Y du CGLd (Corps Genouillé Latéral dorsal). Dans la troisième partie, nous avons modélisé l'apprentissage expérimentalement observé à l'aide de réseaux de neurones utilisant un apprentissage Hebbien (cartes auto-organisatrices). Nous avons montré que le « feedback » des aires supérieures vers le cortex visuel primaire était souhaitable pour la conservation de la sélectivité à l'orientation des neurones. De manière générale, cette thèse montre l'importance des connexions locales dans la plasticité neuronale. Notamment, elles garantissent un apprentissage homéostatique, c'est-à- dire conservant la représentativité des orientations au niveau du cortex. De manière complémentaire, elle montre également l’importance des aires supérieures dans le maintient à long terme des orientations apprises par les neurones lors de l'apprentissage.In the cat primary visual cortex (areas 17 and 18), neurons responding to orientations in the environment (such as the outline of objects) are organized in columns perpendicular to the cortical surface. It was previously shown that a drastic change in orientations in the environment changes the response of neurons. For example, a neuron responding to a horizontal orientation will respond, after learning a new environment, to an oblique orientation. In this thesis, we seek to follow the changes of properties of large populations of neurons due to this type of learning. To this end, we used the intrinsic signals optical imaging technique, which measures the activity of a cortical surface using the BOLD (blood-oxygen-level dependent) signal. This thesis follows three axes: the effect of learning at the local level, the effect of learning at the visual area scale, and the modeling of learning. In the first part, we compared the changes in orientation of neurons according to the local gradient of orientation. This gradient is strong when two neighboring neurons have very different orientations, and weak when their orientations are similar. The obtained relation between the gradient and the magnitude of change in orientation shows that when neurons are increasingly surrounded by neurons with different orientations, they change their response to orientation to a greater extent. This suggests that local connections have a decisive influence on the extent of learning. In the second part, we followed the change in the orientation of neurons in the areas 17 and 18, before and after learning. The results are not significantly different between area 17 and area 18. However, it is noteworthy that orientation changes in area 18 are more variable in amplitude than in area 17. This may be because area 18 receives more diverse inputs than area 17, including a direct input from dLGN (dorsal Lateral Geniculate Nucleus) Y cells. In the third part, we modeled the experimentally observed learning with neural networks using a Hebbian learning rule (networks are self-organizing maps). We have shown that feedback from higher areas to the primary visual cortex was desirable for the neurons orientation selectivity conservation. Overall, this thesis shows the importance of local connections in neuronal plasticity. In particular, they guarantee a homeostatic learning, i.e. maintaining the representativeness of orientations in the cortex. In a complementary manner, it also shows the importance of the superior areas in the conservation of learned orientations

Analysis and Control of Mobile Robots in Various Environmental Conditions

The world sees new inventions each day, made to make the lifestyle of humans more easy and luxurious. In such global scenario, the robots have proved themselves to be an invention of great importance. The robots are being used in almost each and every field of the human world. Continuous studies are being done on them to make them simpler and easier to work with. All fields are being unraveled to make them work better in the human world without human interference. We focus on the navigation field of these mobile robots. The aim of this thesis is to find the controller that produces the most optimal path for the robot to reach its destination without colliding or damaging itself or the environment. The techniques like Fuzzy logic, Type 2 fuzzy logic, Neural networks and Artificial bee colony have been discussed and experimented to find the best controller that could find the most optimal path for the robot to reach its goal position. Simulation and Experiments have been done alike to find out the optimal path for the robot

A right hemisphere advantage for processing blurred faces

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