Exploration of the effects of neuron model properties on network dynamics

The human brain is a complex system, consisting of over 80 billion neurons and the connection between these neurons. Due to the complexity of the brain, we are still far away from successfully being able to simulate the brain in detail. With modern computational technology we must make the tradeoff between modelling the neurons in detail, at the expense of network size, or to reduce the complexity of the neuron model in order to simulate a larger network. When making simplifications in modelling neural networks, modelers have different mathematical models at their disposal. One potential challenge in computational neuroscience is that different research groups may use different models in their effort to model the same phenomenon. In this thesis we explore how using different mathematical models to model the synapse between neurons may affect the dynamics in a network of neurons. For our work we use the integrate-and-fire neuron model, where the neurons are arranged in a Brunel network. We use three different waveform functions to model the current in the synapses between neurons. These include the delta function, the alpha function, and the exponential decay function. The dynamics of the networks are explored using spiking statistics that include the firing rate, the coefficient of variation, the correlation coefficient and Wasserstein distances. We explore the dynamics in networks of different sizes and in different firing regimes, keeping all parameters fixed. Our results show that the alpha synapse model have the highest firing rate in all regimes and network sizes. The exponential decay synapse model has the lowest firing rate in all regimes and network sizes, and the firing rate of the delta synapse model fall in between. The results also give an indication the separation into clearly defined firing regimes may not be as consistent as reported by others. All three synapse models are able to clearly separate between the regular and irregular regimes, but they all display difficulty in separating the between the irregular regimes

Causality, Emergentism, and Mentality

The author here combines the insights of John Dupré and Jaeqwon Kim to supply a new answer to the big conundrum in the philosophy of mind: the mind/body problem. He begins with an overview of Dupré’s take on the classic modern take of causality and determinism, concluding that if Dupré is right and probabilistic catastrophism is true, this theory of probability has important consequences for the mind/body problem. Specifically, probabilistic catastrophism can address the question forced by Kim’s emergentism: how do irreducible mental properties interact with the closed causal nexus of the physical world? The paper concludes by considering what this new thesis means for the question over whether mentality can be multiply realized—if other things could behave like brains

“In my fiction I never say anything which is not absolutely true”: Reassessing Constance Fenimore Woolson’s Literary Realism

Despite her immense popularity in the nineteenth century, Constance Fenimore Woolson\u27s reputation dwindled substantially in the decades which followed. While her works have been rediscovered over the past thirty years, they are often categorized as regionalist writing or, in the case of her penultimate novel, Jupiter Lights, melodrama. What many fail to consider, however, is that Woolson very much considered herself a realist author, and may have been remembered as such were it not for the influence of William Dean Howells and his peers, whose very narrow parameters for literary realism excluded Woolson, among others. Unfortunately, those parameters are still with us today, and exclude many authors whose realities do not conform to Howells’s original scope. In this thesis, I examine the biographical and historical context for Woolson’s lesser-known works, arguing that they demonstrate a type of empathetic realism which must not be ignored by current scholars of American literature