A Search For Principles of Basal Ganglia Function

The basal ganglia are a group of subcortical nuclei that contain about 100 million neurons in humans. Different modes of basal ganglia dysfunction lead to Parkinson's disease and Huntington's disease, which have debilitating motor and cognitive symptoms. However, despite intensive study, both the internal computational mechanisms of the basal ganglia, and their contribution to normal brain function, have been elusive. The goal of this thesis is to identify basic principles that underlie basal ganglia function, with a focus on signal representation, computation, dynamics, and plasticity. This process begins with a review of two current hypotheses of normal basal ganglia function, one being that they automatically select actions on the basis of past reinforcement, and the other that they compress cortical signals that tend to occur in conjunction with reinforcement. It is argued that a wide range of experimental data are consistent with these mechanisms operating in series, and that in this configuration, compression makes selection practical in natural environments. Although experimental work is outside the present scope, an experimental means of testing this proposal in the future is suggested. The remainder of the thesis builds on Eliasmith & Anderson's Neural Engineering Framework (NEF), which provides an integrated theoretical account of computation, representation, and dynamics in large neural circuits. The NEF provides considerable insight into basal ganglia function, but its explanatory power is potentially limited by two assumptions that the basal ganglia violate. First, like most large-network models, the NEF assumes that neurons integrate multiple synaptic inputs in a linear manner. However, synaptic integration in the basal ganglia is nonlinear in several respects. Three modes of nonlinearity are examined, including nonlinear interactions between dendritic branches, nonlinear integration within terminal branches, and nonlinear conductance-current relationships. The first mode is shown to affect neuron tuning. The other two modes are shown to enable alternative computational mechanisms that facilitate learning, and make computation more flexible, respectively. Secondly, while the NEF assumes that the feedforward dynamics of individual neurons are dominated by the dynamics of post-synaptic current, many basal ganglia neurons also exhibit prominent spike-generation dynamics, including adaptation, bursting, and hysterses. Of these, it is shown that the NEF theory of network dynamics applies fairly directly to certain cases of firing-rate adaptation. However, more complex dynamics, including nonlinear dynamics that are diverse across a population, can be described using the NEF equations for representation. In particular, a neuron's response can be characterized in terms of a more complex function that extends over both present and past inputs. It is therefore straightforward to apply NEF methods to interpret the effects of complex cell dynamics at the network level. The role of spike timing in basal ganglia function is also examined. Although the basal ganglia have been interpreted in the past to perform computations on the basis of mean firing rates (over windows of tens or hundreds of milliseconds) it has recently become clear that patterns of spikes on finer timescales are also functionally relevant. Past work has shown that precise spike times in sensory systems contain stimulus-related information, but there has been little study of how post-synaptic neurons might use this information. It is shown that essentially any neuron can use this information to perform flexible computations, and that these computations do not require spike timing that is very precise. As a consequence, irregular and highly-variable firing patterns can drive behaviour with which they have no detectable correlation. Most of the projection neurons in the basal ganglia are inhibitory, and the effect of one nucleus on another is classically interpreted as subtractive or divisive. Theoretically, very flexible computations can be performed within a projection if each presynaptic neuron can both excite and inhibit its targets, but this is hardly ever the case physiologically. However, it is shown here that equivalent computational flexibility is supported by inhibitory projections in the basal ganglia, as a simple consequence of inhibitory collaterals in the target nuclei. Finally, the relationship between population coding and synaptic plasticity is discussed. It is shown that Hebbian plasticity, in conjunction with lateral connections, determines both the dimension of the population code and the tuning of neuron responses within the coded space. These results permit a straightforward interpretation of the effects of synaptic plasticity on information processing at the network level. Together with the NEF, these new results provide a rich set of theoretical principles through which the dominant physiological factors that affect basal ganglia function can be more clearly understood

A neural network based model for mass non-residential real estate price evaluation of Lisbon, Portugal

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementAn accurate estimation of the real estate value became very important to make correct purchase and sale transaction, calculate taxes, mortgages for loans. Mass appraisal systems that use modern methodology based on artificial intelligence significantly help to deal with these issues. Objectives of this article are: using artificial neural networks (AANs) build mass appraisal model to evaluate market price of non-residential real estate of Lisbon, Portugal; evaluate performance of AANs and compare it with results generated by other models based on different methodologies and prove AANs superiority in issues connected with real estate apprising

Information Encoding by Individual Neurons and Groups of Neurons in the Primary Visual Cortex

How is information about visual stimuli encoded into the responses of neurons in the cerebral cortex? In this thesis, I describe the analysis of data recorded simultaneously from groups of up to eight nearby neurons in the primary visual cortices of anesthetized macaque monkeys. The goal is to examine the degree to which visual information is encoded into the times of action potentials in those responses (as opposed to the overall rate), and also into the identity of the neuron that fires each action potential (as opposed to the average activity across a group of nearby neurons). The data are examined with techniques modified from systems analysis, statistics, and information theory. The results are compared with expectations from simple statistical models of action-potential firing and from models that are more physiologically realistic. The major findings are: (1) that cortical responses are not renewal processes with time-varying firing rates, which means that information can indeed be encoded in the detailed timing of action potentials; (2) that these neurons encode the contrast of visual stimuli primarily into the time difference between stimulus and response onset, which is known as the latency; (3) that this so-called temporal coding serves as a mechanism by which the brain might discriminate among stimuli that evoke similar firing rates; (4) that action potentials preceded by interspike intervals of different durations can encode different features of a stimulus; (5) that the rate of overall information transmission can depend on the type of stimulus in a manner that differs from one neuron to the next; (6) that the rate at which information is transmitted specifically about stimulus contrast depends little on stimulus type; (7) that a substantial fraction of the information rate can be confounded among multiple stimulus attributes; and, most importantly, (8) that averaging together the responses of multiple nearby neurons leads to a significant loss of information that increases as more neurons are considered. These results should serve as a basis for direct investigation into the cellular mechanisms by which the brain extracts and processes the information carried in neuronal responses

A Hybrid Neural Network Architecture for Texture Analysis in Digital Image Processing Applications

A new hybrid neural network model capable of texture analysis in a digital image processing environment is presented in this thesis. This model is constructed from two different types of neural network, self-organisation and back-propagation. Along with a brief resume of digital image processing concepts, an introduction to neural networks is provided. This contains appropriate documentation of the neural networks and test evidence is also presented to highlight the relative strengths and weaknesses of both neural networks. The hybrid neural network is proposed from this evidence along with methods of training and operation. This is supported by practical examples of the system's operation with digital images. Through this process two modes of operation are explored, classification and segmentation of texture content within images. Some common methods of texture analysis are also documented, with spatial grey level dependence matrices being chosen to act as a feature generator for classification by a back-propagation neural network, this provides a benchmark to assess the performance of the hybrid neural network. This takes the form of descriptive comparison, pictorial results, and mathematical analysis when using aerial survey images. Other novel approaches using the hybrid neural network are presented with concluding comments outlining the findings presented within this thesis

Intrinsic and Visually-Evoked Properties of Layer 2/3 Neurons in Mouse Primary Visual Cortex and Their Dependence on Sensory Experience

Neurons in Primary Visual Cortex (V1) are known to respond strongly to visual stimuli. Studies of neuronal responses in V1, carried out first in cats, but later primates and other mammals, have demonstrated that bars of light at particular orientations evoke strong, reliable responses in terms of increased firing rate of action potentials. Tuning of neuronal responses to certain stimulus parameters, such as orientation but also spatial and temporal frequencies, as well as the apparent dichotomy between simple and complex responses, have given rise to a number of influential models not just of V1 function, but more generally in the field of cortical physiology and computer vision. Owing to its small size and the plethora of available molecular and genetic tools, the visual cortex of the mouse may be a more tractable model system than that of much larger animals. Recent studies of neuronal responses in mouse V1 have shown that these are broadly similar to those of primates and carnivores, although not identical in all aspects. My thesis aims firstly to characterise intrinsic and sensory-evoked properties in regularspiking, putative pyramidal neurons in L2/3 of the mouse visual cortex using whole-cell patch clamp recording in vivo . In addition, the anatomical connectivity of individual neurons is characterised using virus-assisted circuit mapping. The majority of these neurons are found to be simple cells. Orientation tuning (the degree to which neuronal responses are selective to stimuli of a preferred orientation) is found to be quite variable, even within this singular group of neurons. The potential roles of intrinsic diversity, functional connectivity, and sensory experience in setting the orientation tuning of a particular neuron are investigated. These findings provide an insight in to how diverse responses to sensory stimuli can be generated in an apparently homogenous group of neurons

Piecewise truckload network procurement

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 107-110).Faced with procuring transportation over its freight network, a shipper can either bid out all of its lanes at once, or somehow divide up the network and bid it out in pieces. For large shippers, practical concerns such as attendant manpower requirements and exposure to financial/operational risks can make the former undesirable or even infeasible. Such a shipper therefore needs to determine how to best allocate the lanes in its freight network to different bids to be run at different times. This thesis addresses this allocation problem. Two related approaches are presented. The first focuses on explicitly preserving the synergies that arise in truckload network operations while attempting to balance the sizes of each bid, and is framed as a graph partitioning problem. The second treats lanes as independent entities and frames network allocation as a bin-packing problem, with constraints that attempt to achieve both balance and, implicitly, synergy preservation. These two approaches are illustrated and evaluated using a small subnetwork consisting of lanes from a large shipper. While the graph partitioning approach works in theory, the as yet unresolved question of what constitutes a "correct" synergy definition for network partitioning purposes, and the practical significance of the constraints considered in the bin-packing approach, make this second approach more attractive. The development of a lane allocation model that can explicitly consider inter-lane synergies as well as the kinds of constraints addressed in the second approach is left for future work.by Jefferson Huang.S.M

The Drosophila visual system: a super-efficient encoder

In order to survive and reproduce, every animal needs to run accurate and diverse visual processes efficiently. However, understanding how they see is limited by our lack of insight into how evolution optimises resource- and area-constrained neural machinery. It was shown recently in Drosophila that its photoreceptor cells, corresponding to individual “pixels” of the scene, react photomechanically to these light changes by generating an ultrafast counter-motion, a photoreceptor microsaccade. Each photoreceptor moves in a specific direction at its particular location inside the compound eye, transiently readjusting its own light input. These mirror-symmetrically opposing microsaccades cause small timing differences in the eye and the brain networks’ electrical signals, rapidly and accurately informing the fly of the 3D world structure. Remarkably, it has been shown that the Drosophila can resolve angles finer than 1°, five times less than what the optic laws would predict in a static fly eye. The results presented in this thesis demonstrate that hyperacute visual information is transmitted from the photoreceptors to the visual pathway and I report a deep learning approach for discovering how the Drosophila compound eyes' biological neural network (BNN) samples and represents hyperacute stimuli. Using in vivo two-photon calcium imaging on a transgenic fly, I recorded the responses of 17 flies’ L2 neurons, OFF neurons in the early visual pathway, while presenting fine resolution visual patterns. I showed that the Drosophila’s visual hyperacute information is transmitted from the photoreceptors to the medulla layer (2nd layer in the visual system). Additionally, I found that the L2 neurons show direction-specific acuity and proved that this is a consequence of the photoreceptors’ microsaccades. Next, I show that an artificial neural network (ANN), with precisely-positioned and photomechanically-moving photoreceptors, shaping and feeding visual information to a lifelike-wired neuropile, learns to reproduce natural response dynamics. Remarkably, this ANN predicts realistic stimulus-locked responses and synaptic connection eights at each eye location, mapping the eyes' experimentally verified hyperacute orientation sensitivity. By systematically altering sampling dynamics and connections, I further show that without the realistic orientation-tuned photoreceptor microsaccades and connectome, performance falters to suboptimal. My results demonstrate the importance of precise microsaccades and connectivity for efficient visual encoding and highlight the effect of morphodynamic information sampling on accurate perception

Financial predictions using intelligent systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a collection of practical techniques for analysing various market properties in order to design advanced self-evolving trading systems based on neural networks combined with a genetic algorithm optimisation approach. Nonlinear multivariate statistical models have gained increasing importance in financial time series analysis, as it is very hard to fmd statistically significant market inefficiencies using standard linear modes. Nonlinear models capture more of the underlying dynamics of these high dimensional noisy systems than traditional models, whilst at the same time making fewer restrictive assumptions about them. These adaptive trading systems can extract information about associated time varying processes that may not be readily captured by traditional models. In order to characterise the fmancial time series in terms of its dynamic nature, this research employs various methods such as fractal analysis, chaos theory and dynamical recurrence analysis. These techniques are used for evaluating whether markets are stochastic and deterministic or nonlinear and chaotic, and to discover regularities that are completely hidden in these time series and not detectable using conventional analysis. Particular emphasis is placed on examining the feasibility of prediction in fmancial time series and the analysis of extreme market events. The market's fractal structure and log-periodic oscillations, typical of periods before extreme events occur, are revealed through recurrence plots. Recurrence qualification analysis indicated a strong presence of structure, recurrence and determinism in the fmancial time series studied. Crucial fmancial time series transition periods were also detected. This research performs several tests on a large number of US and European stocks using methodologies inspired by both fundamental analysis and technical trading rules. Results from the tests show that profitable trading models utilising advanced nonlinear trading systems can be created after accounting for realistic transaction costs. The return achieved by applying the trading model to a portfolio of real price series differs significantly from that achieved by applying it to a randomly generated price series. In some cases, these models are compared against simpler alternative approaches to ensure that there is an added value in the use of these more complex models. The superior performance of multivariate nonlinear models is also demonstrated. The long-short trading strategies performed well in both bull and bear markets, as well as in a sideways market, showing a great degree of flexibility and adjustability to changing market conditions. Empirical evidence shows that information is not instantly incorporated into market pnces and supports the claim that the fmancial time series studied, for the periods analysed, are not entirely random. This research clearly shows that equity markets are partially inefficient and do not behave along lines dictated by the efficient market hypothesis

ANN application in maritime industry : Baltic Dry Index forecasting & optimization of the number of container cranes

This dissertation is a study of dry bulk freight index forecasting and port planning, both based on Artificial Neural network application. First the dry bulk market is reviewed, and the reason for the high fluctuation of freight rates through the demand-supply mechanism is examined. Due to the volatile BDI, the traditional linear regression forecasting method cannot guarantee the performance of forecasting, but ANN overcomes this difficulty and gives better performance especially in a short time. Besides, in order to improve the performance of ANN further, wavelet is introduced to pre-process the BDI data. But when the noise (high frequency parts) is stripped, the hidden useful data may also be eliminated. So the performance of different degrees of de-noising models is evaluated, and the best one (most suitable de-noising model) is chosen to forecast BDI, which avoids over de-noising and keeps a fair ability of forecasting. In the second case study, the collected container terminals and ranked, and the throughput of each combination (different crane number) is estimated by applying a trained BP network. The BP network with DEA output is combined, simulating the efficiency of each combination. And finally, the optimal container crane number is fixed due to the highest efficiency and practical reasons. The Conclusion and Recommendation chapter gives some further advice, and many recommendations are given