Smashing the Mirror of Yamato: Sakaguchi Ango, Decadence & a Postmetaphysical Buddhist Critique of Culture

This article focuses on several key philosophical themes in the criticism of Sakaguchi Ango (1906–1955), one of postwar Japan’s most influential and controversial writers. Associated with the underground Kasutori culture as well as the Burai-ha of Tamura Taijirō (1911–1983), Oda Sakunosuke (1913–1947) and Dazai Osamu (1909–1948), Ango gained fame for two provocative essays on the theme of daraku or “decadence”—Darakuron and Zoku darakuron—pubished in 1946, in the wake of Japan’s traumatic defeat and the beginnings of the Allied Occupation. Less well-known is the fact that Ango spent his student years studying classical Buddhist texts in Sanskrit, Pali and Tibetan, and that he at at one time aspired to the priesthood. The article analyses the concept of daraku in the two essays noted above, particularly as it relates to Ango’s vision of a refashioned morality based on an interpretation of human subjectivity vis-à-vis the themes of illusion and disillusion. It argues that, despite the radical and modernist flavor of Ango’s essays, his “decadence” is best understood in terms of Mahāyāna and Zen Buddhist concepts. Moreover, when the two essays on decadence are read in tandem with Ango’s wartime essay on Japanese culture (Nihon bunka shikan, 1942), they form the foundation for a “postmetaphysical Buddhist critique of culture,” one that is pragmatic, humanistic, and non-reductively physicalist.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.