15,036 research outputs found
Coverage, Continuity and Visual Cortical Architecture
The primary visual cortex of many mammals contains a continuous
representation of visual space, with a roughly repetitive aperiodic map of
orientation preferences superimposed. It was recently found that orientation
preference maps (OPMs) obey statistical laws which are apparently invariant
among species widely separated in eutherian evolution. Here, we examine whether
one of the most prominent models for the optimization of cortical maps, the
elastic net (EN) model, can reproduce this common design. The EN model
generates representations which optimally trade of stimulus space coverage and
map continuity. While this model has been used in numerous studies, no
analytical results about the precise layout of the predicted OPMs have been
obtained so far. We present a mathematical approach to analytically calculate
the cortical representations predicted by the EN model for the joint mapping of
stimulus position and orientation. We find that in all previously studied
regimes, predicted OPM layouts are perfectly periodic. An unbiased search
through the EN parameter space identifies a novel regime of aperiodic OPMs with
pinwheel densities lower than found in experiments. In an extreme limit,
aperiodic OPMs quantitatively resembling experimental observations emerge.
Stabilization of these layouts results from strong nonlocal interactions rather
than from a coverage-continuity-compromise. Our results demonstrate that
optimization models for stimulus representations dominated by nonlocal
suppressive interactions are in principle capable of correctly predicting the
common OPM design. They question that visual cortical feature representations
can be explained by a coverage-continuity-compromise.Comment: 100 pages, including an Appendix, 21 + 7 figure
Emergence of Hierarchy on a Network of Complementary Agents
Complementarity is one of the main features underlying the interactions in
biological and biochemical systems. Inspired by those systems we propose a
model for the dynamical evolution of a system composed by agents that interact
due to their complementary attributes rather than their similarities. Each
agent is represented by a bit-string and has an activity associated to it; the
coupling among complementary peers depends on their activity. The connectivity
of the system changes in time respecting the constraint of complementarity. We
observe the formation of a network of active agents whose stability depends on
the rate at which activity diffuses in the system. The model exhibits a
non-equilibrium phase transition between the ordered phase, where a stable
network is generated, and a disordered phase characterized by the absence of
correlation among the agents. The ordered phase exhibits multi-modal
distributions of connectivity and activity, indicating a hierarchy of
interaction among different populations characterized by different degrees of
activity. This model may be used to study the hierarchy observed in social
organizations as well as in business and other networks.Comment: 13 pages, 4 figures, submitte
Self Organized Scale-Free Networks from Merging and Regeneration
We consider the self organizing process of merging and regeneration of
vertices in complex networks and demonstrate that a scale-free degree
distribution emerges in a steady state of such a dynamics. The merging of
neighbor vertices in a network may be viewed as an optimization of efficiency
by minimizing redundancy. It is also a mechanism to shorten the distance and
thus decrease signaling times between vertices in a complex network. Thus the
merging process will in particular be relevant for networks where these issues
related to global signaling are of concern
Adaptive-network models of swarm dynamics
We propose a simple adaptive-network model describing recent swarming
experiments. Exploiting an analogy with human decision making, we capture the
dynamics of the model by a low-dimensional system of equations permitting
analytical investigation. We find that the model reproduces several
characteristic features of swarms, including spontaneous symmetry breaking,
noise- and density-driven order-disorder transitions that can be of first or
second order, and intermittency. Reproducing these experimental observations
using a non-spatial model suggests that spatial geometry may have a lesser
impact on collective motion than previously thought.Comment: 8 pages, 3 figure
Order out of Randomness : Self-Organization Processes in Astrophysics
Self-organization is a property of dissipative nonlinear processes that are
governed by an internal driver and a positive feedback mechanism, which creates
regular geometric and/or temporal patterns and decreases the entropy, in
contrast to random processes. Here we investigate for the first time a
comprehensive number of 16 self-organization processes that operate in
planetary physics, solar physics, stellar physics, galactic physics, and
cosmology. Self-organizing systems create spontaneous {\sl order out of chaos},
during the evolution from an initially disordered system to an ordered
stationary system, via quasi-periodic limit-cycle dynamics, harmonic mechanical
resonances, or gyromagnetic resonances. The internal driver can be gravity,
rotation, thermal pressure, or acceleration of nonthermal particles, while the
positive feedback mechanism is often an instability, such as the
magneto-rotational instability, the Rayleigh-B\'enard convection instability,
turbulence, vortex attraction, magnetic reconnection, plasma condensation, or
loss-cone instability. Physical models of astrophysical self-organization
processes involve hydrodynamic, MHD, and N-body formulations of Lotka-Volterra
equation systems.Comment: 61 pages, 38 Figure
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Quantum speedup for active learning agents
Can quantum mechanics help us in building intelligent robots and agents? One
of the defining characteristics of intelligent behavior is the capacity to
learn from experience. However, a major bottleneck for agents to learn in any
real-life situation is the size and complexity of the corresponding task
environment. Owing to, e.g., a large space of possible strategies, learning is
typically slow. Even for a moderate task environment, it may simply take too
long to rationally respond to a given situation. If the environment is
impatient, allowing only a certain time for a response, an agent may then be
unable to cope with the situation and to learn at all. Here we show that
quantum physics can help and provide a significant speed-up for active learning
as a genuine problem of artificial intelligence. We introduce a large class of
quantum learning agents for which we show a quadratic boost in their active
learning efficiency over their classical analogues. This result will be
particularly relevant for applications involving complex task environments.Comment: Minor updates, 14 pages, 3 figure
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