54,717 research outputs found
An O(n) method of calculating Kendall correlations of spike trains
The ability to record from increasingly large numbers of neurons, and the
increasing attention being paid to large scale neural network simulations,
demands computationally fast algorithms to compute relevant statistical
measures. We present an O(n) algorithm for calculating the Kendall correlation
of spike trains, a correlation measure that is becoming especially recognized
as an important tool in neuroscience. We show that our method is around 50
times faster than the O (n ln n) method which is a current standard for quickly
computing the Kendall correlation. In addition to providing a faster algorithm,
we emphasize the role that taking the specific nature of spike trains had on
reducing the run time. We imagine that there are many other useful algorithms
that can be even more significantly sped up when taking this into
consideration. A MATLAB function executing the method described here has been
made freely available on-line.Comment: 7 pages, 1 figure, 1 tabl
Rethinking the Problem of Linguistic Categorization for Global Search Engines
The fields of social psychology and neuroscience have known for several decades that culture affects the way people carve up the world. This perceptual difference is often, but not always, aligned with similar differences in linguistics categories. If correct, this problem of linguistic categorization may have considerable impact on search algorithms. This paper examines the relationship between culture and linguistic categorization for global search engines. A total of 43 American and Chinese participants completed two classification tests, one derived from social psychology and neuroscience and the other based on a common classification problem for full-text searching. These data suggest that Chinese participants are more field dependent, American participants are less field dependent, and that these results may offer important clues about adapting search algorithms for global computing systems
A Superconducting Nanowire-based Architecture for Neuromorphic Computing
Neuromorphic computing is poised to further the success of software-based
neural networks by utilizing improved customized hardware. However, the
translation of neuromorphic algorithms to hardware specifications is a problem
that has been seldom explored. Building superconducting neuromorphic systems
requires extensive expertise in both superconducting physics and theoretical
neuroscience. In this work, we aim to bridge this gap by presenting a tool and
methodology to translate algorithmic parameters into circuit specifications. We
first show the correspondence between theoretical neuroscience models and the
dynamics of our circuit topologies. We then apply this tool to solve linear
systems by implementing a spiking neural network with our superconducting
nanowire-based hardware.Comment: 29 pages, 10 figure
Leveraging Probabilistic Switching in Superparamagnets for Temporal Information Encoding in Neuromorphic Systems
Brain-inspired computing - leveraging neuroscientific principles underpinning
the unparalleled efficiency of the brain in solving cognitive tasks - is
emerging to be a promising pathway to solve several algorithmic and
computational challenges faced by deep learning today. Nonetheless, current
research in neuromorphic computing is driven by our well-developed notions of
running deep learning algorithms on computing platforms that perform
deterministic operations. In this article, we argue that taking a different
route of performing temporal information encoding in probabilistic neuromorphic
systems may help solve some of the current challenges in the field. The article
considers superparamagnetic tunnel junctions as a potential pathway to enable a
new generation of brain-inspired computing that combines the facets and
associated advantages of two complementary insights from computational
neuroscience -- how information is encoded and how computing occurs in the
brain. Hardware-algorithm co-design analysis demonstrates accuracy of
a state-compressed 3-layer spintronics enabled stochastic spiking network on
the MNIST dataset with high spiking sparsity due to temporal information
encoding
Ethical Reflections of Human Brain Research and Smart Information Systems
open access journalThis case study explores ethical issues that relate to the use of Smart Infor-mation Systems (SIS) in human brain research. The case study is based on the Human Brain Project (HBP), which is a European Union funded project. The project uses SIS to build a research infrastructure aimed at the advancement of neuroscience, medicine and computing. The case study was conducted to assess how the HBP recognises and deal with ethical concerns relating to the use of SIS in human brain research. To under-stand some of the ethical implications of using SIS in human brain research, data was collected through a document review and three semi-structured interviews with partic-ipants from the HBP. Results from the case study indicate that the main ethical concerns with the use of SIS in human brain research include privacy and confidentiality, the security of personal data, discrimination that arises from bias and access to the SIS and their outcomes.
Furthermore, there is an issue with the transparency of the processes that are involved in human brain research. In response to these issues, the HBP has put in place different mechanisms to ensure responsible research and innovation through a dedicated pro-gram. The paper provides lessons for the responsible implementation of SIS in research, including human brain research and extends some of the mechanisms that could be employed by researchers and developers of SIS for research in addressing such issues
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