Transcendental Aspects, Ontological Commitments and Naturalistic Elements in Nietzsche's Thought

Nietzsche's views on knowledge have been interpreted in at least three incompatible ways - as transcendental, naturalistic or proto-deconstructionist. While the first two share a commitment to the possibility of objective truth, the third reading denies this by highlighting Nietzsche's claims about the necessarily falsifying character of human knowledge (his so-called error theory). This paper examines the ways in which his work can be construed as seeking ways of overcoming the strict opposition between naturalism and transcendental philosophy whilst fully taking into account the error theory (interpreted non-literally, as a hyperbolic warning against uncritical forms of realism). In doing so, it clarifies the nature of Nietzsche's ontological commitments, both in the early and the later work, and shows that his relation to transcendental idealism is more subtle than is allowed by naturalistic interpreters while conversely accounting for the impossibility of conceiving the conditions of the possibility of knowledge as genuinely a priori

Dedicated hippocampal inhibitory networks for locomotion and immobility

Network activity is strongly tied to animal movement; however, hippocampal circuits selectively engaged during locomotion or immobility remain poorly characterized. Here we examined whether distinct locomotor states are encoded differentially in genetically defined classes of hippocampal interneurons. To characterize the relationship between interneuron activity and movement, we usedin vivo, two-photon calcium imaging in CA1 of male and female mice, as animals performed a virtual-reality (VR) track running task. We found that activity in most somatostatin-expressing and parvalbumin-expressing interneurons positively correlated with locomotion. Surprisingly, nearly one in five somatostatin or one in seven parvalbumin interneurons were inhibited during locomotion and activated during periods of immobility. Anatomically, the somata of somatostatin immobility-activated neurons were smaller than those of movement-activated neurons. Furthermore, immobility-activated interneurons were distributed across cell layers, with somatostatin-expressing cells predominantly in stratum oriens and parvalbumin-expressing cells mostly in stratum pyramidale. Importantly, each cell's correlation between activity and movement was stable both over time and across VR environments. Our findings suggest that hippocampal interneuronal microcircuits are preferentially active during either movement or immobility periods. These inhibitory networks may regulate information flow in “labeled lines” within the hippocampus to process information during distinct behavioral states.SIGNIFICANCE STATEMENTThe hippocampus is required for learning and memory. Movement controls network activity in the hippocampus but it's unclear how hippocampal neurons encode movement state. We investigated neural circuits active during locomotion and immobility and found interneurons were selectively active during movement or stopped periods, but not both. Each cell's response to locomotion was consistent across time and environments, suggesting there are separate dedicated circuits for processing information during locomotion and immobility. Understanding how the hippocampus switches between different network configurations may lead to therapeutic approaches to hippocampal-dependent dysfunctions, such as Alzheimer's disease or cognitive decline.</jats:p

Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap with arXiv:1703.0352