Technological Change and the United States Navy, 1865–1945

This is not a technological history of the U.S. Navy per se but rather an explora- tion of how the dominant culture of the Navy’s leadership drove specific techno- logical choices in the transition from the sailing ship of the line to the battle- ship and then to the aircraft carrier. McBride’s thesis centers on two points: that the organization and culture of the U.S. Navy have traditionally been defined by its capital ships; and that new technol- ogies challenging the relevance of the current capital ship are generally resisted by senior leaders, who seek both to main- tain control over change and to inhibit any developments that suggest a transfer of power to individuals with the skills, functions, and organizational relation- ships of a new “technological paradigm.

The Cultural Challenge of Information Technology

There has been increasing speculation over the past several years that rapid advances in information technologies will enable tremendous leaps in future combat systems performance. Perhaps the most significant development is the prospect that new high-data-rate communication satellites will soon offer worldwide wireless information transmission capacities that can fully exploit the tremendous speed of modern infomration processing

Technological Change and the Future of Warfare,

Over the past several years, the U.S. mil- itary has officially embraced the idea that rapidly evolving technologies soon will lead to a profound change in the conduct of warfare. The need to inno- vate in response to a prospective revolu- tion in military affairs is the central theme of Joint Vision 2010 and similar force-planning documents. Some stud- ies, such as the congressionally man- dated National Defense Panel, have concluded that only immediate and radical transformation to new systems, new operational concepts, and new or- ganizations will enable the U.S. military to retain its battlefield dominance

The Limits of Transformation

In the shadow of the recent Iraq war, it is easy to accept that “growth and diffusion of stealth, precision, and information technology” has truly heralded the long-awaited revolution in military affairs. American leaders—from the President to the Pentagon military and civilian leadership—have called for dramatic transformation of each of the services to fit this revolution. In many ways, this is a far harder task.https://digital-commons.usnwc.edu/usnwc-newport-papers/1015/thumbnail.jp

A Heterosynaptic Learning Rule for Neural Networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.Comment: 19 page

How spiking neurons give rise to a temporal-feature map

A temporal-feature map is a topographic neuronal representation of temporal attributes of phenomena or objects that occur in the outside world. We explain the evolution of such maps by means of a spike-based Hebbian learning rule in conjunction with a presynaptically unspecific contribution in that, if a synapse changes, then all other synapses connected to the same axon change by a small fraction as well. The learning equation is solved for the case of an array of Poisson neurons. We discuss the evolution of a temporal-feature map and the synchronization of the single cells’ synaptic structures, in dependence upon the strength of presynaptic unspecific learning. We also give an upper bound for the magnitude of the presynaptic interaction by estimating its impact on the noise level of synaptic growth. Finally, we compare the results with those obtained from a learning equation for nonlinear neurons and show that synaptic structure formation may profit from the nonlinearity