Using Machine Learning and Computer Simulations to Analyse Neuronal Activity in the Cerebellar Nuclei During Absence Epilepsy

Absence epilepsy is a neurological disorder that commonly occurs in children. Some studies have shown that absence seizures predominantly originate either in the thalamus or the cerebral cortex. Some cerebellar nuclei (CN) neurons project to these brain areas, as explained further in Fig. 2.6 in Chapter 2. Also, some CN neurons have been observed to show modulation during the absence seizures. This indicates that they somehow participate in the seizure and hence are referred to as "participating neurons" in this thesis. In this research, I demonstrate how machine learning techniques and computer simulations can be applied to investigate the properties and the input conditions present in these participating neurons. My investigation found a sub-group of CN neurons, with similar interictal spiking activity, spiking activity between the seizures, that are most likely to participate in seizures. To investigate the input conditions present in the CN neurons that produce this type of interictal activity, I used a morphologically realistic conductance based model of an excitatory CN projection neuron [66] and optimised the input parameters to this model using an Evolutionary Algorithm (EA). The results of the EA revealed that these participating CN neurons receive a synchronous and bursting input from Purkinje cells and bursting input with long intervals(approx. 500ms) from mossy fibre. The same interictal activity can also be produced when the Purkinje cell input to the CN neuron is asynchronous. The excitatory input in this case also had long interburst intervals but there is a decrease in excitatory and inhibitory synaptic weight. Surprisingly, a slight change in these input parameters can change the interictal spiking pattern to an ictal spiking pattern, the spiking pattern observed during absence seizures. I also discovered that it is possible to prevent a participating CN neuron from taking part in the seizures by blocking the Purkinje cell input

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Evolution of Dendritic Morphologies Using Deterministic and Nondeterministic Genotype to Phenotype Mapping

In this study, two morphological representations in the genotype, a deterministic and a nondeterministic representation, are compared when evolving a neuronal morphology for a pattern recognition task. The deterministic approach represents the dendritic morphology explicitly as a set of partitions in the genotype which can give rise to a single phenotype. The nondeterministic method used in this study encodes only the branching probability in the genotype which can produce multiple phenotypes. The main result is that the nondeterministic method instigates the selection of more symmetric dendritic morphologies which was not observed in the deterministic metho