6,476 research outputs found
Weighted p-bits for FPGA implementation of probabilistic circuits
Probabilistic spin logic (PSL) is a recently proposed computing paradigm
based on unstable stochastic units called probabilistic bits (p-bits) that can
be correlated to form probabilistic circuits (p-circuits). These p-circuits can
be used to solve problems of optimization, inference and also to implement
precise Boolean functions in an "inverted" mode, where a given Boolean circuit
can operate in reverse to find the input combinations that are consistent with
a given output. In this paper we present a scalable FPGA implementation of such
invertible p-circuits. We implement a "weighted" p-bit that combines stochastic
units with localized memory structures. We also present a generalized tile of
weighted p-bits to which a large class of problems beyond invertible Boolean
logic can be mapped, and how invertibility can be applied to interesting
problems such as the NP-complete Subset Sum Problem by solving a small instance
of this problem in hardware
Harmonic Analysis of Boolean Networks: Determinative Power and Perturbations
Consider a large Boolean network with a feed forward structure. Given a
probability distribution on the inputs, can one find, possibly small,
collections of input nodes that determine the states of most other nodes in the
network? To answer this question, a notion that quantifies the determinative
power of an input over the states of the nodes in the network is needed. We
argue that the mutual information (MI) between a given subset of the inputs X =
{X_1, ..., X_n} of some node i and its associated function f_i(X) quantifies
the determinative power of this set of inputs over node i. We compare the
determinative power of a set of inputs to the sensitivity to perturbations to
these inputs, and find that, maybe surprisingly, an input that has large
sensitivity to perturbations does not necessarily have large determinative
power. However, for unate functions, which play an important role in genetic
regulatory networks, we find a direct relation between MI and sensitivity to
perturbations. As an application of our results, we analyze the large-scale
regulatory network of Escherichia coli. We identify the most determinative
nodes and show that a small subset of those reduces the overall uncertainty of
the network state significantly. Furthermore, the network is found to be
tolerant to perturbations of its inputs
IEEE Access Special Section Editorial: Recent Advances on Hybrid Complex Networks: Analysis and Control
Complex networks typically involve multiple disciplines due to network dynamics and their statistical nature. When modeling practical networks, both impulsive effects and logical dynamics have recently attracted increasing attention. Hence, it is of interest and importance to consider hybrid complex networks with impulsive effects and logical dynamics. Relevant research is prevalent in cells, ecology, social systems, and communication engineering. In hybrid complex networks, numerous nodes are coupled through networks and their properties usually lead to complex dynamic behaviors, including discrete and continuous dynamics with finite values of time and state space. Generally, continuous and discrete sections of the systems are described by differential and difference equations, respectively. Logical networks are used to model the systems where time and state space take finite values. Although interesting results have been reported regarding hybrid complex networks, the analysis methods and relevant results could be further improved with respect to conservative impulsive delay inequalities and reproducibility of corresponding stability or synchronization criteria. Therefore, it is necessary to devise effective approaches to improve the analysis method and results dealing with hybrid complex networks
Double Deep-Q Learning-Based Output Tracking of Probabilistic Boolean Control Networks
In this article, a reinforcement learning (RL)-based scalable technique is presented to control the probabilistic Boolean control networks (PBCNs). In particular, a double deep- network (DD ) approach is firstly proposed to address the output tracking problem of PBCNs, and optimal state feedback controllers are obtained such that the output of PBCNs tracks a constant as well as a time-varying reference signal. The presented method is model-free and offers scalability, thereby provides an efficient way to control large-scale PBCNs that are a natural choice to model gene regulatory networks (GRNs). Finally, three PBCN models of GRNs including a 16-gene and 28-gene networks are considered to verify the presented results
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