There is compelling evidence that memory is supported by multiple, functionally independent subsystems that distinguish declarative from non-declarative memories (Tulving, 1972). The declarative subsystems, episodic and semantic memory, have been studied intensively, largely in isolation from each other. Relatively little attention has been paid to the interplay been episodic and semantic memory. This thesis constitutes a series of behavioural, neuroimaging, and computational investigations aimed at elucidating the factors and mechanisms that mediate interactions between episodic and semantic memory. Event-Related Potentials (ERPs) are used to isolate processes implicated in episodic and semantic memory interactions on the basis of known ERP effects. Experimental investigations vary factors that target semantic memory either directly or indirectly. Direct manipulations alter the semantic content of word pairs by modulating their lexicality (words vs. non-words) or coherence (categorical vs. non-categorical). Indirect manipulations focus episodic encoding towards semantic or non-semantic aspects of the to-be-encoded word pairs. This thesis investigates whether such manipulations influence episodic memory and if so, in what form. The behavioural and ERP data provide clear evidence for distinct episodic and semantic interactions at the level of semantic organisation and lexical representation. Episodic retrieval, which is supported by recollection and familiarity according to dual process theories (Yonelinas, 2002), reveals enhanced familiarity for semantically organised stimuli. This effect is dependent on semantically deep encoding strategies. By contrast differences in the lexicality of stimuli modulated both familiarity and recollection. To provide an account for why different types of interactions are obtained a computational memory model is proposed. This model uses a single network to simulate a dual process model of episodic retrieval and gives insight into processes that may support interactions between episodic and semantic memory. Thus, this thesis provides novel evidence for different types of episodic and semantic memory interactions dependent on the kind of semantic manipulation and specifies the mediating mechanisms leading to such interactions.

We examine the nature of causality as it exists within large-scale brain networks by first providing a rigorous conceptual analysis of probabilistic causality as distinct from deterministic causality. We then use information-theoretic methods, including the linear autoregressive modeling technique of Wiener-Granger causality (WGC), and Shannonian transfer entropy (TE), to explore and recover causal relations between two neural masses. Time series data were generated by Stefanescu-Jirsa 3D model of two coupled network nodes in The Virtual Brain (TVB), a novel neuroinformatics platform used to model resting state large-scale networks with neural mass models. We then extended this analysis to three nodes to investigate the equivalence of a concept in probabilistic causality known as ‘screening off’ with a method of statistical ablation known as conditional Granger causality. Finally, we review some of the empirical and theoretical work of nonlinear neurodynamics of Walter Freeman, as well as metastable coordination dynamics and investigate what impact they have had on consciousness research. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Flying fruit flies (Drosophila melanogaster) locate a concealed appetitive odour source most accurately in environments containing vertical visual contrasts (Frye et al, 2003). To investigate how visuomotor and olfactory responses interact to cause this phenomenon, I implement a tracking system capable of recording flies’ flight trajectories in three dimensions. I examine free-flight behaviour in three different visual environments, with and without food odour present. While odour localisation is facilitated by a random chequerboard pattern compared to a horizontally striped one, a single vertical landmark also facilitates odour localisation, but only if the odour source is situated close to the landmark. I implement a closed-loop systems-level model of visuomotor control consisting of three parallel subsystems which use wide-field optic flow cues to control flight behaviour. These are: an optomotor response to stabilise the model fly’s yaw orientation; a collision avoidance system to initiate rapid turns (saccades) away from looming obstacles; and a speed regulation system. This model reproduces in simulation many of the behaviours I observe in flies, including distinctive visually mediated ‘rebound’ turns following saccades. Using recordings of real odour plumes, I simulate the presence of an odorant in the arena, and investigate ways in which the olfactory input could modulate visuomotor control. In accordance with the principle of Occam’s razor, I identify the simplest mechanism of crossmodal integration that reproduces the observed pattern of visual effects on the odour localisation behaviour of flies. The resulting model uses the change in odour intensity to regulate the sensitivity of collision avoidance, resulting in visually mediated chemokinesis. Additionally, it is necessary to amplify the optomotor response whenever odour is present, increasing the model fly’s tendency to steer towards features of the visual environment. This could be viewed as a change in behavioural context brought about by the possibility of feeding. A novel heterogeneous visual environment is used to validate the model. While its predictions are largely borne out by experimental data, it fails to account for a pronounced odour-dependent attraction to regions of exclusively vertical contrast. I conclude that visual and olfactory responses of Drosophila are not independent, but that relatively simple interaction between these modalities can account for the observed visual dependence of odour source localisation.

The proper functioning of nervous systems requires electrical activity to be tightly regulated. Perturbations in the intrinsic properties of neurons, and in excitatory input, are imposed throughout nervous system development as cell morphology and network activity evolve. In mature nervous systems these changes continue as a result of synaptic plasticity and external stimuli. It is therefore likely that homeostatic mechanisms exist to regulate membrane conductances that determine the excitability of individual neurons, and several mechanisms have been characterised to date. This thesis characterises a novel in vitro model for homeostatic control of intrinsic excitability. The principal finding is that cultured hippocampal neurons respond to chronic depolarisation over a period of days by attenuating their response to injected current. This effect was found to depend on the level of depolarisation and the length of treatment, and is accompanied by changes in both active and passive membrane conductances. In addition, the effect is reversible and dependent on L-type calcium channel activity. Using experimental data to parameterise a conductance-based computer model suggests that the changes in conductance properties account for the observed differences in excitability.

The central theme of this thesis is the development of both a generalised computational model for large-scale brain networks and the neuroinformatics platform that enables a systematic exploration and analysis of those models. In this thesis we describe the mathematical framework of the computational model at the core of the tool The Virtual brain (TVB), designed to recreate collective whole brain dynamics by virtualising brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. We also review previous studies related to brain network models and multimodal neuroimaging integration and detail how they are related to the general model presented in this work. Practical examples describing how to build a minimal *in silico* primate brain model are given. Finally, we explain how the resulting software tool, TVB, facilitates the collaboration between experimentalists and modellers by exposing both a comprehensive simulator for brain dynamics and an integrative framework for the management, analysis, and simulation of structural and functional data in an accessible, web-based interface. / The central theme of this thesis is the development of both a generalised computational model for large-scale brain networks and the neuroinformatics platform that enables a systematic exploration and analysis of those models. In this thesis we describe the mathematical framework of the computational model at the core of the tool The Virtual brain (TVB), designed to recreate collective whole brain dynamics by virtualising brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. We also review previous studies related to brain network models and multimodal neuroimaging integration and detail how they are related to the general model presented in this work. Practical examples describing how to build a minimal *in silico* primate brain model are given. Finally, we explain how the resulting software tool, TVB, facilitates the collaboration between experimentalists and modellers by exposing both a comprehensive simulator for brain dynamics and an integrative framework for the management, analysis, and simulation of structural and functional data in an accessible, web-based interface.

The distinctiveness of brain structures and circuits depends on interacting gene products, yet the organization of these molecules (the "transcriptome") within and across brain areas remains unclear. High-throughput, neuroanatomically-specific gene expression datasets such as the Allen Human Brain Atlas (AHBA) and Allen Mouse Brain Atlas (AMBA) have recently become available, providing unprecedented opportunities to quantify molecular neuroanatomy. This dissertation seeks to clarify how transcriptomic organization relates to conventional neuroanatomy within and across species, and to introduce the use of gene expression data as a bridge between genotype and phenotype in complex behavioral disorders. The first part of this work examines large-scale, regional transcriptomic organization separately in the mouse and human brain. The use of dimensionality reduction methods and cross-sample correlations both revealed greater similarity between samples drawn from the same brain region. Sample profiles and differentially expressed genes across regions in the human brain also showed consistent anatomical specificity in a second human dataset with distinct sampling properties. The frequent use of mouse models in clinical research points to the importance of comparing molecular neuroanatomical organization across species. The second part of this dissertation describes three comparative approaches. First, at genome scale, expression profiles within homologous brain regions tended to show higher similarity than those from non-homologous regions, with substantial variability across regions. Second, gene subsets (defined using co-expression relationships or shared annotations), which provide region-specific, cross-species molecular signatures were identified. Finally, brain-wide expression patterns of orthologous genes were compared. Neuron and oligodendrocyte markers were more correlated than expected by chance, while astrocyte markers were less so. The localization and co-expression of genes reflect functional relationships that may underlie high-level functions. The final part of this dissertation describes a database of genes that have been implicated in speech and language disorders, and identifies brain regions where they are preferentially expressed or co-expressed. Several brain structures with functions relevant to four speech and language disorders showed co-expression of genes associated with these disorders. In particular, genes associated with persistent developmental stuttering showed stronger preferential co-expression in the basal ganglia, a structure of known importance in this disorder.

Thesis (M.A.)--University of Central Florida, 2009. / Adviser: Madelyn Flammia. Includes bibliographical references (p. 91-104).

Visual perception in mammals is made possible by the visual system and the visual cortex. However, precisely how visual information is coded in the brain and how training can improve this encoding is unclear. The ability to see and process visual information is not an innate property of the visual cortex. Instead, it is learnt from exposure to visual stimuli. We first considered how visual perception is learnt, by studying the perceptual learning of contrast discrimination in macaques. We investigated how changes in population activity in the visual cortices V1 and V4 correlate with the changes in behavioural response during training on this task. Our results indicate that changes in the learnt neural and behavioural responses are directed toward optimising the performance on the training task, rather than a general improvement in perception of the presented stimulus type. We report that the most informative signal about the contrast of the stimulus within V1 and V4 is the transient stimulus-onset response in V1, 50 ms after the stimulus presentation begins. However, this signal does not become more informative with training, suggesting it is an innate and untrainable property of the system, on these timescales at least. Using a linear decoder to classify the stimulus based on the population activity, we find that information in the V4 population is closely related to the information available to the higher cortical regions involved with decision making, since the performance of the decoder is similar to the performance of the animal throughout training. These findings suggest that training the subject on this task directs V4 to improve its read out of contrast information contained in V1, and cortical regions responsible for decision making use this to improve the performance with training. The structure of noise correlations between the recorded neurons changes with training, but this does not appear to cause the increase in behavioural performance. Furthermore, our results suggest there is feedback of information about the stimulus into the visual cortex after 300 ms of stimulus presentation, which may be related to the high-level percept of the stimulus within the brain. After training on the task, but not before, information about the stimulus persists in the activity of both V1 and V4 at least 400 ms after the stimulus is removed. In the second part, we explore how information is distributed across the anatomical layers of the visual cortex. Cortical oscillations in the local field potential (LFP) and current source density (CSD) within V1, driven by population-level activity, are known to contain information about visual stimulation. However the purpose of these oscillations, the sites where they originate, and what properties of the stimulus is encoded within them is still unknown. By recording the LFP at multiple recording sites along the cortical depth of macaque V1 during presentation of a natural movie stimulus, we investigated the structure of visual information encoded in cortical oscillations. We found that despite a homogeneous distribution of the power of oscillations across the cortical depth, information was compartmentalised into the oscillations of the 4 Hz to 16 Hz range at the granular (G, layer 4) depths and the 60Hz to 170Hz range at the supragranular (SG, layers 1–3) depths, the latter of which is redundant with the population-level firing rate. These two frequency ranges contain independent information about the stimulus, which we identify as related to two spatiotemporal aspects of the visual stimulus. Oscillations in the visual cortex with frequencies < 40 Hz contain information about fast changes in low spatial frequency. Frequencies > 40 Hz and multi-unit firing rates contain information about properties of the stimulus related to changes, both slow and fast, at finer-grained spatial scales. The spatiotemporal domains encoded in each are complementary. In particular, both the power and phase of oscillations in the 7 Hz to 20Hz range contain information about scene transitions in the presented movie stimulus. Such changes in the stimulus are similar to saccades in natural behaviour, and this may be indicative of predictive coding within the cortex.

Time is not only the fundamental organizing principle of the universe, it is also the primary organizer of information about the world we perceive. Our brain encodes these perceptions in sequential patterns of spiking activity. But different stimuli lead to different information encoded on different timescales; sometimes the same stimulus carries information pertaining to different perceptions on different timescales. The orders of time are many and the computational circuits of the brain must disentangle these interwoven threads to decode the underlying structure. This thesis deals with solutions to this disentanglement problem implemented not at the network level, but in smaller systems and single neurons that represent the past by clever use of internal mechanisms. Often, these solutions involve the intricate tools of the neural dendrite or other peculiar aspects of neural circuits that are well known to physiologists and biologists but disregarded in favor of more homogeneous models by many theoreticians. It is at the intersection of the diverse biological reality of the brain and the difficulty of the computational problem to disentangle the threads of temporal order that we find new and powerful computational principles: Symbolic computation on the level of single neurons via dendritic plateau potentials, embedding history in delayed feedback dynamics or consecutive filter responses, or the idea that learning a generalized differential description of a systems can largely forgo the need to remember the past – instead, patterns can freely be generated. Together, the different challenges that information ordered in different, asynchronous times present require a diverse palette of solutions. At the same time, computation and the structure imposed by time are deeply connected.

The analysis of microcircuitry (the connectivity at the level of individual neuronal processes and synapses), which is indispensable for our understanding of brain function, is based on serial transmission electron microscopy (TEM) or one of its modern variants. Due to technical limitations, most previous studies that used serial TEM recorded relatively small stacks of individual neurons. As a result, our knowledge of microcircuitry in any nervous system is very limited. We applied the software package TrakEM2 to reconstruct neuronal microcircuitry from TEM sections of a small brain, the early larval brain of Drosophila melanogaster. TrakEM2 enables us to embed the analysis of the TEM image volumes at the microcircuit level into a light microscopically derived neuro-anatomical framework, by registering confocal stacks containing sparsely labeled neural structures with the TEM image volume. We imaged two sets of serial TEM sections of the Drosophila first instar larval brain neuropile and one ventral nerve cord segment, and here report our first results pertaining to Drosophila brain microcircuitry. Terminal neurites fall into a small number of generic classes termed globular, varicose, axiform, and dendritiform. Globular and varicose neurites have large diameter segments that carry almost exclusively presynaptic sites. Dendritiform neurites are thin, highly branched processes that are almost exclusively postsynaptic. Due to the high branching density of dendritiform fibers and the fact that synapses are polyadic, neurites are highly interconnected even within small neuropile volumes. We describe the network motifs most frequently encountered in the Drosophila neuropile. Our study introduces an approach towards a comprehensive anatomical reconstruction of neuronal microcircuitry and delivers microcircuitry comparisons between vertebrate and insect neuropile.