A Computational Investigation of Neural Dynamics and Network Structure

With the overall goal of illuminating the relationship between neural dynamics and neural network structure, this thesis presents a) a computer model of a network infrastructure capable of global broadcast and competition, and b) a study of various convergence properties of spike-timing dependent plasticity (STDP) in a recurrent neural network. The first part of the thesis explores the parameter space of a possible Global Neuronal Workspace (GNW) realised in a novel computational network model using stochastic connectivity. The structure of this model is analysed in light of the characteristic dynamics of a GNW: broadcast, reverberation, and competition. It is found even with careful consideration of the balance between excitation and inhibition, the structural choices do not allow agreement with the GNW dynamics, and the implications of this are addressed. An additional level of competition – access competition – is added, discussed, and found to be more conducive to winner-takes-all competition. The second part of the thesis investigates the formation of synaptic structure due to neural and synaptic dynamics. From previous theoretical and modelling work, it is predicted that homogeneous stimulation in a recurrent neural network with STDP will create a self-stabilising equilibrium amongst synaptic weights, while heterogeneous stimulation will induce structured synaptic changes. A new factor in modulating the synaptic weight equilibrium is suggested from the experimental evidence presented: anti-correlation due to inhibitory neurons. It is observed that the synaptic equilibrium creates competition amongst synapses, and those specifically stimulated during heterogeneous stimulation win out. Further investigation is carried out in order to assess the effect that more complex STDP rules would have on synaptic dynamics, varying parameters of a trace STDP model. There is little qualitative effect on synaptic dynamics under low frequency (< 25Hz) conditions, justifying the use of simple STDP until further experimental or theoretical evidence suggests otherwise

The U.S. and Irish Credit Crises: Their Distinctive Differences and Common Features

Abstract: Although the US credit crisis precipitated it, the Irish credit crisis is an identifiably separate one, which might have occurred in the absence of the U.S. crash. The distinctive differences between them are notable. Almost all the apparent causal factors of the U.S. crisis are missing in the Irish case; and the same applies vice-versa. At a deeper level, we identify four common features of the two credit crises: capital bonanzas, irrational exuberance, regulatory imprudence, and moral hazard. The particular manifestations of these four “deep” common features are quite different in the two cases.

A new functional role for lateral inhibition in the striatum: Pavlovian conditioning

The striatum has long been implicated in reinforcement learning and has been suggested by several neurophysiological studies as the substrate for encoding the reward value of stimuli. Reward prediction error (RPE) has been used in several basal ganglia models as the underlying learning signal, which leads to Pavlovian conditioning abilities that can be simulated by the Rescorla-Wagner model.&#xd;&#xa;&#xd;&#xa;Lateral inhibition between striatal projection neurons was once thought to have a winner-take-all function, useful in selecting between possible actions. However, it has been noted that the necessary reciprocal connections for this interpretation are too few, and the relative strength of these synaptic connections is weak. Still, modeling studies show that lateral inhibition does have an overall suppression effect on striatal activity and may play an important role in striatal processing. &#xd;&#xa;&#xd;&#xa;Neurophysiological recordings show task-relevant ensembles of responsive neurons at specific points in a behavioral paradigm (Barnes et al., 2005), which appear to be induced by lateral inhibition (see Ponzi and Wickens, 2010). We have developed a similarly responding, RPE-based model of the striatum by incorporating lateral inhibition. Model neurons are assigned to either the direct or the indirect pathway but lateral connections occur within and between these groups, leading to competition between both the individual neurons and their pathways. We successfully applied this model to the simulation of Pavlovian phenomena beyond those of the Rescorla-Wagner model, including negative patterning, unovershadowing, and external inhibition

Construction Dust Amelioration Techniques

INE/AUTC 12.0