299,503 research outputs found
The architecture of information processing in biological systems
Biological systems process information at different scales and adapt to their
changing environment. Informed both by experimental observations and
theoretical constraints, we propose a chemical model for sensing that
incorporates energy consumption, information storage, and negative feedback. We
show that a biochemical architecture enclosing these minimal mechanisms leads
to the emergence of dynamical memory and adaptation. Crucially, adaptation is
associated with both an increase in the mutual information between external and
internal variables and a reduction of dissipation of the internal chemical
processes. By simultaneously minimizing energy consumption and maximizing
information, we find that far-from-equilibrium sensing dominates in the
low-noise regime. Our results, in principle, can be declined at different
biological scales. We employ our model to shed light on large-scale neural
adaptation in zebrafish larvae under repeated visual stimulation. We find
striking similarities between predicted and observed behaviors, capturing the
emergent adaptation of neural response. Our framework draws a path toward the
unraveling of the essential ingredients that connect information processing,
adaptation, and memory in biological systems
Multi-hop Diffusion LMS for Energy-constrained Distributed Estimation
We propose a multi-hop diffusion strategy for a sensor network to perform
distributed least mean-squares (LMS) estimation under local and network-wide
energy constraints. At each iteration of the strategy, each node can combine
intermediate parameter estimates from nodes other than its physical neighbors
via a multi-hop relay path. We propose a rule to select combination weights for
the multi-hop neighbors, which can balance between the transient and the
steady-state network mean-square deviations (MSDs). We study two classes of
networks: simple networks with a unique transmission path from one node to
another, and arbitrary networks utilizing diffusion consultations over at most
two hops. We propose a method to optimize each node's information neighborhood
subject to local energy budgets and a network-wide energy budget for each
diffusion iteration. This optimization requires the network topology, and the
noise and data variance profiles of each node, and is performed offline before
the diffusion process. In addition, we develop a fully distributed and adaptive
algorithm that approximately optimizes the information neighborhood of each
node with only local energy budget constraints in the case where diffusion
consultations are performed over at most a predefined number of hops. Numerical
results suggest that our proposed multi-hop diffusion strategy achieves the
same steady-state MSD as the existing one-hop adapt-then-combine diffusion
algorithm but with a lower energy budget.Comment: 14 pages, 12 figures. Submitted for publicatio
Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining
Service Function Chaining (SFC) allows the forwarding of a traffic flow along
a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT).
Software Defined Networking (SDN) solutions can be used to support SFC reducing
the management complexity and the operational costs. One of the most critical
issues for the service and network providers is the reduction of energy
consumption, which should be achieved without impact to the quality of
services. In this paper, we propose a novel resource (re)allocation
architecture which enables energy-aware SFC for SDN-based networks. To this
end, we model the problems of VNF placement, allocation of VNFs to flows, and
flow routing as optimization problems. Thereafter, heuristic algorithms are
proposed for the different optimization problems, in order find near-optimal
solutions in acceptable times. The performance of the proposed algorithms are
numerically evaluated over a real-world topology and various network traffic
patterns. The results confirm that the proposed heuristic algorithms provide
near optimal solutions while their execution time is applicable for real-life
networks.Comment: Extended version of submitted paper - v7 - July 201
Bounded Rational Decision-Making in Changing Environments
A perfectly rational decision-maker chooses the best action with the highest
utility gain from a set of possible actions. The optimality principles that
describe such decision processes do not take into account the computational
costs of finding the optimal action. Bounded rational decision-making addresses
this problem by specifically trading off information-processing costs and
expected utility. Interestingly, a similar trade-off between energy and entropy
arises when describing changes in thermodynamic systems. This similarity has
been recently used to describe bounded rational agents. Crucially, this
framework assumes that the environment does not change while the decision-maker
is computing the optimal policy. When this requirement is not fulfilled, the
decision-maker will suffer inefficiencies in utility, that arise because the
current policy is optimal for an environment in the past. Here we borrow
concepts from non-equilibrium thermodynamics to quantify these inefficiencies
and illustrate with simulations its relationship with computational resources.Comment: 9 pages, 2 figures, NIPS 2013 Workshop on Planning with Information
Constraint
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