29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

Oscillatory dynamics as a mechanism of integration in complex networks of neurons

The large-scale integrative mechanisms of the brain, the means by which the activity of functionally segregated neuronal regions are combined, are not well understood. There is growing agreement that a flexible mechanism of integration must be present in order to support the myriad changing cognitive demands under which we are placed. Neuronal communication through phase-coherent oscillation stands as the prominent theory of cognitive integration. The work presented in this thesis explores the role of oscillation and synchronisation in the transfer and integration of information in the brain. It is first shown that complex metastable dynamics suitable for modelling phase-coherent neuronal synchronisation emerge from modularity in networks of delay and pulse-coupled oscillators. Within a restricted parameter regime these networks display a constantly changing set of partially synchronised states where some modules remain highly synchronised while others desynchronise. An examination of network phase dynamics shows increasing coherence with increasing connectivity between modules. The metastable chimera states that emerge from the activity of modular oscillator networks are demonstrated to be synchronous with a constant phase relationship as would be required of a mechanism of large-scale neural integration. A specific example of functional phase-coherent synchronisation within a spiking neural system is then developed. Competitive stimulus selection between converging population encoded stimuli is demonstrated through entrainment of oscillation in receiving neurons. The behaviour of the model is shown to be analogous to well-known competitive processes of stimulus selection such as binocular rivalry, matching key experimentally observed properties for the distribution and correlation of periods of entrainment under differing stimuli strength. Finally two new measures of network centrality, knotty-centrality and set betweenness centrality, are developed and applied to empirically derived human structural brain connectivity data. It is shown that human brain organisation exhibits a topologically central core network within a modular structure consistent with the generation of synchronous oscillation with functional phase dynamics

Network approaches to understanding the functional effects of focal brain lesions

Complex network models of functional connectivity have emerged as a paradigm shift in brain mapping over the past decade. Despite significant attention within the neuroimaging and cognitive neuroscience communities, these approaches have hitherto not been extensively explored in neurosurgery. The aim of this thesis is to investigate how the field of connectomics can contribute to understanding the effects of focal brain lesions and to functional brain mapping in neurosurgery. This datasets for this thesis include a clinical population with focal brain tumours and a cohort focused on healthy adolescent brain development. Multiple network analyses of increasing complexity are performed based upon resting state functional MRI. In patients with focal brain tumours, the full complement of resting state networks were apparent, while also suggesting putative patterns of network plasticity. Connectome analysis was able to identify potential signatures of node robustness and connections at risk that could be used to individually plan surgery. Focal lesions induced the formation of new hubs while down regulating previously established hubs. Overall these data are consistent with a dynamic rather than a static response to the presence of focal lesions. Adolescent brain development demonstrated discrete dynamics with distinct gender specific and age-gender interactions. Network architecture also became more robust, particularly to random removal of nodes and edges. Overall these data provide evidence for the early vulnerability rather than enhanced plasticity of brain networks. In summary, this thesis presents a combined analysis of pathological and healthy development datasets focused on understanding the functional effects of focal brain lesions at a network level. The coda serves as an introduction to a forthcoming study, known as Connectomics and Electrical Stimulation for Augmenting Resection (CAESAR), which is an evolution of the results and methods herein.MGH is funded by the Wellcome Trust Neuroscience in Psychiatry Network with additional support from the National Institute for Health Research Cambridge Biomedical Research Centre

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets

The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain structure and function and for diagnostic applications. Developments of deep learning models in the past decade have revolutionized photographic image and speech recognition, bringing promise to do the same to other fields of science. However, there are many practical and theoretical challenges in the translation of such methods to the unique context of MRIs of the brain. This thesis presents a theoretical underpinning for whole-brain classification of extremely large datasets of multi-site MRIs, including machine learning model architecture, dataset curation methods, machine learning visualization methods, encoding of MRI data, and feature extraction. To replicate large sample sizes typically applied to deep learning models, a dataset of over 50,000 functional and structural MRIs was amassed from nine different databases, and the undertaken analyses were conducted on three covariates commonly found across these collections: sex, resting state/task, and autism spectrum disorder. I find that deep learning is not only a method that has promise for clinical application in the future, but also a powerful statistical tool for analyzing complex, nonlinear relationships in brain data where conventional statistics may fail. However, results are also dependent on factors such as dataset imbalances, confounding factors such as motion and head size, selected methods of encoding MRI data, variability of machine learning models and selected methods of visualizing the machine learning results. In this thesis, I present the following methodological innovations: (1) a method of balancing datasets as a means of regressing out measurable confounding factors; (2) a means of removing spatial biases from deep learning visualization methods; (3) methods of encoding functional and structural datasets as connectivity matrices; (4) the use of ensemble models and convolutional neural network architectures to improve classification accuracy and consistency; (5) adaptation of deep learning visualization methods to study brain connections utilized in the classification process. Additionally, I discuss interpretations, limitations, and future directions of this research.Gates Cambridge Scholarshi