166 research outputs found
Attack-Resilient Supervisory Control of Discrete-Event Systems
In this work, we study the problem of supervisory control of discrete-event
systems (DES) in the presence of attacks that tamper with inputs and outputs of
the plant. We consider a very general system setup as we focus on both
deterministic and nondeterministic plants that we model as finite state
transducers (FSTs); this also covers the conventional approach to modeling DES
as deterministic finite automata. Furthermore, we cover a wide class of attacks
that can nondeterministically add, remove, or rewrite a sensing and/or
actuation word to any word from predefined regular languages, and show how such
attacks can be modeled by nondeterministic FSTs; we also present how the use of
FSTs facilitates modeling realistic (and very complex) attacks, as well as
provides the foundation for design of attack-resilient supervisory controllers.
Specifically, we first consider the supervisory control problem for
deterministic plants with attacks (i) only on their sensors, (ii) only on their
actuators, and (iii) both on their sensors and actuators. For each case, we
develop new conditions for controllability in the presence of attacks, as well
as synthesizing algorithms to obtain FST-based description of such
attack-resilient supervisors. A derived resilient controller provides a set of
all safe control words that can keep the plant work desirably even in the
presence of corrupted observation and/or if the control words are subjected to
actuation attacks. Then, we extend the controllability theorems and the
supervisor synthesizing algorithms to nondeterministic plants that satisfy a
nonblocking condition. Finally, we illustrate applicability of our methodology
on several examples and numerical case-studies
Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry
The backpropagation algorithm has experienced remarkable success in training large-scale artificial neural networks; however, its biological plausibility has been strongly criticized, and it remains an open question whether the brain employs supervised learning mechanisms akin to it. Here, we propose correlative information maximization between layer activations as an alternative normative approach to describe the signal propagation in biological neural networks in both forward and backward directions. This new framework addresses many concerns about the biological-plausibility of conventional artificial neural networks and the backpropagation algorithm. The coordinate descent-based optimization of the corresponding objective, combined with the mean square error loss function for fitting labeled supervision data, gives rise to a neural network structure that emulates a more biologically realistic network of multi-compartment pyramidal neurons with dendritic processing and lateral inhibitory neurons. Furthermore, our approach provides a natural resolution to the weight symmetry problem between forward and backward signal propagation paths, a significant critique against the plausibility of the conventional backpropagation algorithm. This is achieved by leveraging two alternative, yet equivalent forms of the correlative mutual information objective. These alternatives intrinsically lead to forward and backward prediction networks without weight symmetry issues, providing a compelling solution to this long-standing challenge
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources
Extraction of latent sources of complex stimuli is critical for making sense
of the world. While the brain solves this blind source separation (BSS) problem
continuously, its algorithms remain unknown. Previous work on
biologically-plausible BSS algorithms assumed that observed signals are linear
mixtures of statistically independent or uncorrelated sources, limiting the
domain of applicability of these algorithms. To overcome this limitation, we
propose novel biologically-plausible neural networks for the blind separation
of potentially dependent/correlated sources. Differing from previous work, we
assume some general geometric, not statistical, conditions on the source
vectors allowing separation of potentially dependent/correlated sources.
Concretely, we assume that the source vectors are sufficiently scattered in
their domains which can be described by certain polytopes. Then, we consider
recovery of these sources by the Det-Max criterion, which maximizes the
determinant of the output correlation matrix to enforce a similar spread for
the source estimates. Starting from this normative principle, and using a
weighted similarity matching approach that enables arbitrary linear
transformations adaptable by local learning rules, we derive two-layer
biologically-plausible neural network algorithms that can separate mixtures
into sources coming from a variety of source domains. We demonstrate that our
algorithms outperform other biologically-plausible BSS algorithms on correlated
source separation problems.Comment: NeurIPS 2022, 37 page
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