Philosophy

Summary

Philosophy (love of wisdom in ancient Greek) is a systematic study of general and fundamental questions concerning topics like existence, reason, knowledge, values, mind, and language. It is a rational and critical inquiry that reflects on its own methods and assumptions. Historically, many of the individual sciences, like physics and psychology, formed part of philosophy. But they are considered separate academic disciplines in the modern sense of the term. The main traditions in the history of philosophy include Western, Arabic-Persian, Indian, and Chinese philosophy. Western philosophy originated in Ancient Greece and covers a wide area of philosophical subfields. A central topic in Arabic-Persian philosophy is the relation between reason and revelation. Indian philosophy combines the spiritual problem of how to reach enlightenment with the exploration of the nature of reality and the ways of arriving at knowledge. Chinese philosophy focuses on practical issues in relation to right

Official source

https://en.wikipedia.org/wiki/Philosophy

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2011

Desargues and Leibniz: In the Black Box - A Mathematical Model of the Leibnizian Monad

Bernard Cache

Here Bernard Cache provides a detailed analysis of a paper written in 1636 by the French mathematician, architect and engineer, Girard Desargues. Desargues is best known as the founder of projective geometry. Cache explains how he initally developed this significant concept in response to the very practical problems of producing a perspectival drawing. The introduction of projective geometry, though, had potentially more far-reaching implications on philosophical thought, informing the theory of monads developed by the German philosopher and mathematician Gottfried Leibniz in 1714 to explain the metaphysics of simple substances. © 2011 John Wiley & Sons Ltd.

Wiley-Blackwell2011

Cyber-workstation for computational neuroscience

Babak Mahmoudi

A Cyber-Workstation (CW) to study in vivo, real-time interactions between computational models and large-scale brain subsystems during behavioral experiments has been designed and implemented. The design philosophy seeks to directly link the in vivo neurophysiology laboratory with scalable computing resources to enable more sophisticated computational neuroscience investigation. The architecture designed here allows scientists to develop new models and integrate them with existing models (e.g. recursive least-squares regressor) by specifying appropriate connections in a block-diagram. Then, adaptive middleware transparently implements these user specifications using the full power of remote grid-computing hardware. In effect, the middleware deploys an on-demand and flexible neuroscience research test-bed to provide the neurophysiology laboratory extensive computational power from an outside source. The CW consolidates distributed software and hardware resources to support time-critical and/or resource-demanding computing during data collection from behaving animals. This power and flexibility is important as experimental and theoretical neuroscience evolves based on insights gained from data-intensive experiments, new technologies and engineering methodologies. This paper describes briefly the computational infrastructure and its most relevant components. Each component is discussed within a systematic process of setting up an in vivo, neuroscience experiment. Furthermore, a co-adaptive brain machine interface is implemented on the CW to illustrate how this integrated computational and experimental platform can be used to study systems neurophysiology and learning in a behavior task. We believe this implementation is also the first remote execution and adaptation of a brain-machine interface.

2010