Spontaneously active monolayer neuronal networks cultured on photoetched multimicroelectrode plates (MMEPs) offer great potential for use in studying neuronal networks. However, there are many problems associated with frequent, long-term use of MMEPs. The major problems include (1) polysiloxane insulation deterioration and breakdown, (2) and loss of gold at the gold electroplated indium-tin oxide (ITO) electrodes. The objective of this investigation was to correct these major problems. Quality control measures were employed to monitor MMEP fabrication variables. The phenotypes of polysiloxane degradation were identified and classified. Factors that were found to contribute most to insulation deterioration were (1) moisture contamination during MMEP insulation, (2) loss of the quartz barrier layer from excessive exposure to basic solutions, and (3) repetitive use in culture. As a result, the insulation equipment and methods were modified to control moisture-dependent insulation deterioration, and the KOH reprocessing solution was replaced with tetramethylguanidine to prevent damage to the quartz. The problems associated with gold electroplating were solved via the addition of a pulsed-DC application of gold in a new citrate buffered electroplating solution.

Cryopreservation in suspension is commonplace for a variety of cell types. However, cryopreservation of adherent cells has achieved limited success. This research aimed to cryopreserve adherent nerve cell networks in vitro in a manner that preserved network morphology and physiology. Successful implementation would enable long term storage of adherent neuronal networks on microelectrode arrays and on-demand access for use in pharmacological and toxicological testing. Based upon morphological assessments, excellent post-thaw preservation was obtained and post-thaw cultures survived in a transitional medium for up to 3.5 hours. However, transitions to native culture medium post-thaw presented difficulties, ultimately resulting in necrosis. A discussion of methods to supplement the current research and increase post-thaw viability is included in the thesis.

Experimental and computational investigations addressing how various neural functions are achieved in the brain converged in recent years to a unified idea that the neural activity underlying most of the cognitive functions is distributed over large scale networks comprising various cortical and subcortical areas. Modeling approaches represent these areas and their connections using diverse models of neurocomputational units engaged in graph-like or neural field-like structures. Regardless of the manner of network implementation, simulations of large scale networks have encountered significant difficulties mainly due to the time delay introduced by the long range connections. To decrease the computational effort, it is common to assume severe approximations to simplify the descriptions of the neural dynamics associated with the system's units. In this dissertation we propose an alternative framework allowing the prevention of such strong assumptions while efficiently representing th e dynamics of a complex neural network. First, we consider the dynamics of small scale networks of globally coupled non-identical excitatory and inhibitory neurons, which could realistically instantiate a neurocomputational unit. We identify the most significant dynamical features the neural population exhibits in different parametric configuration, including multi-cluster dynamics, multi-scale synchronization and oscillator death. Then, using mode decomposition techniques, we construct analytically low dimensional representations of the network dynamics and show that these reduced systems capture the dynamical features of the entire neural population. The cases of linear and synaptic coupling are discussed in detail. In chapter 5, we extend this approach for spatially extended neural networks. / We consider the dynamical behavior of a neural field-like network, which incorporates many biologically realistic characteristics such as heterogeneous local and global connectivity as well as dispersion in the neural membrane excitability. We show that in this case as well, we can construct a reduced representation, which may capture well the dynamical features of the full system. The method outlined in this dissertation provides a consistent way to represent complex dynamical features of various neural networks in a computationally efficient manner. / by Roxana A. Stefanescu. / Thesis (Ph.D.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Fidgeting and otherwise constant movements in individuals can be beneficial in those who suffer from Attention Deficit/Hyperactivity Disorder or Generalized Anxiety Disorder as well as others. However this constant movement can also be a distraction to others as well as protrude an air of no self confidence. It is the drawbacks from these actions that we wish to address. By developing an intelligent system that can detect these motions and alert the user, it will allow the wearer of the device to self correct. It is in this self control that one may exhibit more confidence or simply reduce the level of irritation experienced by those in the immediate vicinity. We have designed and built a low cost, mobile, lightweight, untethered, wearable prototype device. It will detect these actions and deliver user selectable biofeedback through a light emitting diode, buzzer, vibromotor or an electric shock to allow for self control. / by Scott A. Barnard. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

This study investigated electroencephalographic differences related to cue (central left- or right-directed arrows) in a covert endogenous visual spatial attention task patterned after that of Hopf and Mangun (2000). This was done with the intent of defining the timing of components in relation to cognitive processes within the cue-target interval. Multiple techniques were employed to do this. Event-related potentials (ERPs) were examined using Independent Component Analysis. This revealed a significant N1, between 100:200 ms post-cue, greater contralateral to the cue. Difference wave ERPs, left minus right cue-locked data, divulged significant early directing attention negativity (EDAN) at 200:400 ms post-cue in the right posterior which reversed polarity in the left posterior. Temporal spectral evolution (TSE) analysis of the alpha band revealed three stages, (1) high bilateral alpha precue to 120 ms post-cue, (2) an event related desynchronization (ERD) from approximately 120 ms: 500 ms post-cue, and (3) an event related synchronization (ERS) rebound, 500: 900 ms post-cue, where alpha amplitude, a measure of activity, was highest contralateral to the ignored hemifield and lower contralateral to the attended hemifield. Using a combination of all of these components and scientific literature in this field, it is possible to plot out the time course of the cognitive events and their neural correlates. / by Edward Justin Modestino. / Vita. / Thesis (Ph.D.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Wong, Chun Kit. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves ). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

There are more neurons in the human brain than seconds in a lifetime. Given this incredible number how can we hope to understand the computations carried out by the full ensemble of neural firing patterns? And neural activity is not the only substrate available for computations. The incredible diversity of function found within biological organisms is matched by an equally rich reservoir available for computation. If we are interested in the metamorphosis of a caterpillar to a butterfly we could explore how DNA expression changes the cell. If we are interested in developing therapeutic drugs we could explore receptors and ion channels. And if we are interested in how humans and other animals interpret incoming streams of sensory information and process them to make moment-by-moment decisions then perhaps we can understand much of this behavior by studying the firing rates of neurons. This is the level and approach we will take in this thesis. Given this diversity of potential reservoirs for computation, combined with limitations in recording technologies, it can be difficult to satisfactorily conclude that we are studying the full set of neural dynamics involved in a particular task. To overcome this limitation, we augment the study of neural activity with the study of artificial recurrent neural networks (RNNs) trained to mimic the behavior of humans and other animals performing experimental tasks. The inputs to the RNN are time-varying signals representing experimental stimuli and we adjust the parameters of the RNN so its time-varying outputs are the desired behavioral responses. In these artificial RNNs we have complete information about the network connectivity and moment-by-moment firing patterns and know, by design, that these are the only computational mechanisms being used to solve the tasks. If the artificial RNN and electrode recordings of real neurons have the same dynamics we can be more confident that we are studying the sufficient set of biological dynamics involved in the task. This is important if we want to make claims about the types of dynamics required, and observed, for various computational tasks, as is the case in Chapter 2 of this thesis. In Chapter 2 we develop tests to identify several classes of neural dynamics. The specific neural dynamic regimes we focus on are interesting because they each have different computational capabilities, including, the ability to keep track of time, or preserve information robustly against the flow of time (working memory). We then apply these tests to electrode recordings from nonhuman primates and artificial RNNs to understand how neural networks are able to simultaneously keep track of time and remember previous experiences in working memory. To accomplish both computational goals the brain is thought to use distinct neural dynamics; stable neural trajectories can be used as a clock to coordinate cognitive activity whereas attractor dynamics provide a stable mechanism for memory storage but all timing information is lost. To identify these neural regimes we decode the passage of time from neural data. Additionally, to encode the passage of time, stabilized neural trajectories can be either high-dimensional as is the case for randomly connected recurrent networks (chaotic reservoir networks) or low-dimensional as is the case for artificial RNNs trained with backpropagation through time. To disambiguate these models we compute the cumulative dimensionality of the neural trajectory as it evolves over time. Recurrent neural networks can also be used to generate hypotheses about neural computation. In Chapter 3 we use RNNs to generate hypotheses about the diverse set of neural response properties seen during spatial navigation, in particular, grid cells, and other spatial correlates, including border cells and band-like cells. The approach we take is 1) pick a task that requires navigation (spatial or mental), 2) create a RNN to solve the task, and 3) adjust the task or constraints on the neural network such that grid cells and other spatial response patterns emerge naturally as the network learns to perform the task. We trained RNNs to perform navigation tasks in 2D arenas based on velocity inputs. We find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. Surprisingly, the order of the emergence of grid-like and border cells during network training is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and other spatial correlates observed in the Entorhinal Cortex of the mammalian brain may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits. All the tasks we have considered so far in this thesis require memory, but in Chapter 4 we explicitly explore the interactions between multiple memories in a recurrent neural network. Memory is the hallmark of recurrent neural networks, in contrast to standard feedforward neural networks where all signals travel in one direction from inputs to outputs and the network contains no memory of previous experiences. A recurrent neural network, as the name suggests, contains feedback loops giving the network the computational power of memory. In this chapter we train a RNN to perform a human psychophysics experiment and find that in order to reproduce human behavior, noise must be added to the network, causing the RNN to use more stable discrete memories to constrain less stable continuous memories.

Orientador: Carlos Camargo Alberts / Banca: Fernando Frei / Banca: Beatriz de Mello Beisiegel / Resumo: Entre os grupos de vertebrados, os mamíferos carnívoros têm sido utilizados como espécies-alvo em diversos projetos ambientais. Atualmente, apresentam populações pequenas e muitas vezes em declínio classificados com algum grau de ameaça de extinção, consequência das alterações na paisagem causadas pelas atividades humanas. No Brasil, atualmente, a maior causa da diminuição das populações de mamíferos carnívoros é a redução ou perda de habitat ocasionada pela expansão agrícola, pecuária, exploração mineral e urbanização. A predação por onças-pardas tem sido documentada em diversas regiões e, como consequência, a perseguição a esses animais é fortemente observada. A fim de identificar os fatores que poderiam estar associados às predações aos rebanhos domésticos por onças-pardas no oeste do Estado de São Paulo, realizamos entrevistas com produtores rurais, utilizando para isso questionários semiestruturados, abordando características das propriedades, do manejo e da paisagem. Realizamos 54 entrevistas e identificamos propriedades que passaram por eventos de predação nos últimos oito anos. Bovino foi o grupo de animais mais frequentemente predado. Através da Regressão Logística, foi possível obter um modelo de previsão de ataques com 83% de concordância entre estimado e observado, onde o número de suínos e a distância do rio para a sede, presentes em cada propriedade, foram positivamente relacionados aos casos de predação. Através das análises das Redes Neurais Artificiais, foi possível especular a presença de um ciclo predador-presa ocorrendo entre o puma concolor e uma presa ainda desconhecida. Práticas de manejo adequadas podem reduzir significativamente as perdas de animais domésticos e assim reduzir possíveis conflitos humanos com predadores selvagens / Abstract: Among the vertebrate groups, carnivorous mammals have been used as target species in several environmental projects. Currently they present small and often declining populations, classified as with some degree of threat of extinction, consequence of the changes in landscape caused by human activities. In Brazil, the major current cause of the decline in carnivorous mammal populations is the reduction or loss of habitat caused by agricultural expansion, livestock farming, mineral exploration, and urbanization. Predation by pumas has been documented in several regions and as a consequence the chase of these animals is strongly observed. In order to identify the factors that could be linked to domestic herds predation by pumas in the west of São Paulo State, we conducted interviews with rural producers, using semi-structured questionnaires, addressing properties characteristics, management and landscape. We have made 54 interviews and identified some rural properties that experienced predation events in the last eight years. Bovines was the group most frequently predated. Throught the Logistic Regression, it was possible to get a model of prediction of attacks with 83% of agreement between estimated and observed, where the number of swines and the distance from the river to the principal house present in each property was positively related to predation cases. Through the analysis of Artificial Neural Networks it was possible to speculate the presence of a predator-prey cycle occurring between the puma concolor and a prey still unknown. Appropriate management practices can significantly reduce the losses of domestic animals and thus reduce possible human conflicts with wild predators / Mestre

Abstract not available

Graduation date: 1994