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Hyperdimensional computing (HDC) is a bio-inspired machine learning paradigm utilizing hyperdimensional spaces for data representation. HDC significantly improves the ability to learn from sparse data and enhances noise robustness, and also enables ...

This paper proposes and experimentally validates a new paradigm for computing with wireless signals over-the-air (OTA). It demonstrates the first fully connected (FC) neural network (NN) constructed entirely using channel propagation and signal ...

Understanding the algorithmic behaviors that are in principle realizable in a chemical system is necessary for a rigorous understanding of the design principles of biological regulatory networks. Further, advances in synthetic biology herald the time when ...

The Turing completeness of continuous chemical reaction networks (CRNs) states that any computable real function can be computed by a continuous CRN on a finite set of molecular species, possibly restricted to elementary reactions, i.e. with at ...

Conventional von Neumann architectures cannot successfully meet the demands of emerging computation and data-intensive applications. These shortcomings can be improved by embracing new architectural paradigms using emerging technologies. In particular, ...

This paper builds on our earlier work on designing demodulators for diffusion-based molecular communication using a Markovian approach. The demodulation filters take the form of an ordinary differential equation which computes log-posteriori probability ...

Scaling the architecture of synthetic gene networks remains one of the most challenging aspects of genetic engineering. We propose a new framework that combines analog signals and digital computation. The analog signals are used to communicate between ...

Understanding the algorithmic behaviors that are in principle realizable in a chemical system is necessary for a rigorous understanding of the design principles of biological regulatory networks. Further, advances in synthetic biology herald the time ...

Understanding the dynamical and computational capabilities of neural models represents an issue of central importance. Here, we consider a model of first-order recurrent neural networks provided with the possibility to evolve over time and involved in a ...

Concrete and virtual machines play a central role in the both Unconventional Computing (machines as computers) and in Math Education (influence of artifacts on reaching/producing abstract thought). Here we will examine some fallouts in these fields for ...

Analog computation has some inherent benefits over traditional digital computation methods and fosters a continued interest in research, specifically in power systems, due to its associated strengths. Among these advantages are physically realizable ...

In this paper we compare the computational power of the Extended Analog Computer (EAC) with partial recursive functions. We first give a survey of some part of computational theory in discrete and in real space. In the last section we show that the EAC ...

In this paper, we aim at an analog characterization of the classical P ≠ NP conjecture of Structural Complexity. We consider functions over continuous real and complex valued variables. Subclasses of functions can be defined using Laplace transforms ...

We prove computability and complexity results for an original model of computation called the continuous space machine. Our model is inspired by the theory of Fourier optics. We prove our model can simulate analog recurrent neural networks, thus ...

Regarding effectivity of functions on the reals, there have been several proposed models of analog, continuous-time computation, as opposed to the digital, discrete nature of the type-2 computability. We study one of them, Moore's real (primitive) ...

In the last years, recursive functions over the reals (Theoret. Comput. Sci. 162 (1996) 23) have been considered, first as a model of analog computation, and second to obtain analog characterizations of classical computational complexity classes (...

We propose certain non-Turing models of computation, but our intent is not to advocate models that surpass the power of Turing machines (TMs), but to defend the need for models with orthogonal notions of power. We review the nature of models and argue ...

This paper considers undecidability in the imitation game, the so-called Turing Test. In the Turing Test, a human, a machine, and an interrogator are the players of the game. In our model of the Turing Test, the machine and the interrogator are ...

It has been argued that neural networks and other forms of analog computation may transcend the limits of Turing-machine computation; proofs have been offered on both sides, subject to differing assumptions. In this article I argue that the important ...

``Neural computing'' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or ...