26,501 research outputs found
Information Processing, Computation and Cognition
Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both ā although others disagree vehemently. Yet different cognitive scientists use ācomputationā and āinformation processingā to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism/computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debatesā empirical aspects
Computation vs. Information Processing: Why Their Difference Matters to Cognitive Science
Since the cognitive revolution, itās become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theorists of cognition be explicit and careful in choosing\ud
notions of computation and information and connecting them together. Much confusion can be avoided by doing so
Conditions for a Monotonic Channel Capacity
Motivated by results in optical communications, where the performance can
degrade dramatically if the transmit power is sufficiently increased, the
channel capacity is characterized for various kinds of memoryless vector
channels. It is proved that for all static point-to-point channels, the channel
capacity is a nondecreasing function of power. As a consequence, maximizing the
mutual information over all input distributions with a certain power is for
such channels equivalent to maximizing it over the larger set of input
distributions with upperbounded power. For interference channels such as
optical wavelength-division multiplexing systems, the primary channel capacity
is always nondecreasing with power if all interferers transmit with identical
distributions as the primary user. Also, if all input distributions in an
interference channel are optimized jointly, then the achievable sum-rate
capacity is again nondecreasing. The results generalizes to the channel
capacity as a function of a wide class of costs, not only power.Comment: This is an updated and expanded version of arXiv:1108.039
Sub-Nyquist Sampling: Bridging Theory and Practice
Sampling theory encompasses all aspects related to the conversion of
continuous-time signals to discrete streams of numbers. The famous
Shannon-Nyquist theorem has become a landmark in the development of digital
signal processing. In modern applications, an increasingly number of functions
is being pushed forward to sophisticated software algorithms, leaving only
those delicate finely-tuned tasks for the circuit level.
In this paper, we review sampling strategies which target reduction of the
ADC rate below Nyquist. Our survey covers classic works from the early 50's of
the previous century through recent publications from the past several years.
The prime focus is bridging theory and practice, that is to pinpoint the
potential of sub-Nyquist strategies to emerge from the math to the hardware. In
that spirit, we integrate contemporary theoretical viewpoints, which study
signal modeling in a union of subspaces, together with a taste of practical
aspects, namely how the avant-garde modalities boil down to concrete signal
processing systems. Our hope is that this presentation style will attract the
interest of both researchers and engineers in the hope of promoting the
sub-Nyquist premise into practical applications, and encouraging further
research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin
- ā¦