Stability in N-Layer recurrent neural networks

Starting with the theory developed by Hopfield, Cohen-Grossberg and Kosko, the study of associative memories is extended to N - layer re-current neural networks. The stability of different multilayer networks is demonstrated under specified bounding hypotheses. The analysis involves theorems for the additive as well as the multiplicative models for continuous and discrete N - layer networks. These demonstrations are based on contin-uous and discrete Liapunov theory. The thesis develops autoassociative and heteroassociative memories. It points out the link between all recurrent net-works of this type. The discrete case is analyzed using the threshold signal function as the activation function. A general approach for studying the sta-bility and convergence of the multilayer recurrent networks is developed

The Performance of Associative Memory Models with Biologically Inspired Connectivity

This thesis is concerned with one important question in artificial neural networks, that is, how biologically inspired connectivity of a network affects its associative memory performance. In recent years, research on the mammalian cerebral cortex, which has the main responsibility for the associative memory function in the brains, suggests that the connectivity of this cortical network is far from fully connected, which is commonly assumed in traditional associative memory models. It is found to be a sparse network with interesting connectivity characteristics such as the “small world network” characteristics, represented by short Mean Path Length, high Clustering Coefficient, and high Global and Local Efficiency. Most of the networks in this thesis are therefore sparsely connected. There is, however, no conclusive evidence of how these different connectivity characteristics affect the associative memory performance of a network. This thesis addresses this question using networks with different types of connectivity, which are inspired from biological evidences. The findings of this programme are unexpected and important. Results show that the performance of a non-spiking associative memory model is found to be predicted by its linear correlation with the Clustering Coefficient of the network, regardless of the detailed connectivity patterns. This is particularly important because the Clustering Coefficient is a static measure of one aspect of connectivity, whilst the associative memory performance reflects the result of a complex dynamic process. On the other hand, this research reveals that improvements in the performance of a network do not necessarily directly rely on an increase in the network’s wiring cost. Therefore it is possible to construct networks with high associative memory performance but relatively low wiring cost. Particularly, Gaussian distributed connectivity in a network is found to achieve the best performance with the lowest wiring cost, in all examined connectivity models. Our results from this programme also suggest that a modular network with an appropriate configuration of Gaussian distributed connectivity, both internal to each module and across modules, can perform nearly as well as the Gaussian distributed non-modular network. Finally, a comparison between non-spiking and spiking associative memory models suggests that in terms of associative memory performance, the implication of connectivity seems to transcend the details of the actual neural models, that is, whether they are spiking or non-spiking neurons

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

Neurocomputational Methods for Autonomous Cognitive Control

Artificial Intelligence can be divided between symbolic and sub-symbolic methods, with neural networks making up a majority of the latter. Symbolic systems have the advantage when capabilities such as deduction and planning are required, while sub-symbolic ones are preferable for tasks requiring skills such as perception and generalization. One of the domains in which neural approaches tend to fare poorly is cognitive control: maintaining short-term memory, inhibiting distractions, and shifting attention. Our own biological neural networks are more than capable of these sorts of executive functions, but artificial neural networks struggle with them. This work explores the gap between the cognitive control that is possible with both symbolic AI systems and biological neural networks, but not with artificial neural networks. To do so, I identify a set of general-purpose, regional-level functions and interactions that are useful for cognitive control in large-scale neural architectures. My approach has three main pillars: a region-and-pathway architecture inspired by the human cerebral cortex and biologically-plausible Hebbian learning, neural regions that each serve as an attractor network able to learn sequences, and neural regions that not only learn to exchange information but also to modulate the functions of other regions. The resultant networks have behaviors based on their own memory contents rather than exclusively on their structure. Because they learn not just memories of the environment but also procedures for tasks, it is possible to "program" these neural networks with the desired behaviors. This research makes four primary contributions. First, the extension of Hopfield-like attractor networks from processing only fixed-point attractors to processing sequential ones. This is accomplished via the introduction of temporally asymmetric weights to Hopfield-like networks, a novel technique that I developed. Second, the combination of several such networks to create models capable of autonomously directing their own performance of cognitive control tasks. By learning procedural memories for a task they can perform in ways that match those of human subjects in key respects. Third, the extension of this approach to spatial domains, binding together visuospatial data to perform a complex memory task at the same level observed in humans and a comparable symbolic model. Finally, these new memories and learning procedures are integrated so that models can respond to feedback from the environment. This enables them to improve as they gain experience by refining their own internal representations of their instructions. These results establish that the use of regional networks, sequential attractor dynamics, and gated connections provide an effective way to accomplish the difficult task of neurally-based cognitive control

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

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

Short-Term Plasticity at the Schaffer Collateral: A New Model with Implications for Hippocampal Processing

A new mathematical model of short-term synaptic plasticity (STP) at the Schaffer collateral is introduced. Like other models of STP, the new model relates short-term synaptic plasticity to an interaction between facilitative and depressive dynamic influences. Unlike previous models, the new model successfully simulates facilitative and depressive dynamics within the framework of the synaptic vesicle cycle. The novelty of the model lies in the description of a competitive interaction between calcium-sensitive proteins for binding sites on the vesicle release machinery. By attributing specific molecular causes to observable presynaptic effects, the new model of STP can predict the effects of specific alterations to the presynaptic neurotransmitter release mechanism. This understanding will guide further experiments into presynaptic functionality, and may contribute insights into the development of pharmaceuticals that target illnesses manifesting aberrant synaptic dynamics, such as Fragile-X syndrome and schizophrenia. The new model of STP will also add realism to brain circuit models that simulate cognitive processes such as attention and memory. The hippocampal processing loop is an example of a brain circuit involved in memory formation. The hippocampus filters and organizes large amounts of spatio-temporal data in real time according to contextual significance. The role of synaptic dynamics in the hippocampal system is speculated to help keep the system close to a region of instability that increases encoding capacity and discriminating capability. In particular, synaptic dynamics at the Schaffer collateral are proposed to coordinate the output of the highly dynamic CA3 region of the hippocampus with the phase-code in the CA1 that modulates communication between the hippocampus and the neocortex