scispace - formally typeset
Search or ask a question
Author

Dawid Przyczyna

Bio: Dawid Przyczyna is an academic researcher from AGH University of Science and Technology. The author has contributed to research in topics: Reservoir computing & Neuromorphic engineering. The author has an hindex of 5, co-authored 15 publications receiving 91 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, the authors present recent achievements in the design of logic devices (binary, ternary and fuzzy) implemented in molecular and nanoscale components, photoelectrochemical chemosensing, photoactive memristive devices and reservoir computing systems.

33 citations

Journal ArticleDOI
TL;DR: This review focuses on the synthesis, properties and selected applications of heavy pnictogen chalcohalides, i.e. compounds of the MQX stoichiometry, where M = As, Sb, and Bi; Q = O, S, Se, and Te; and X = F, Cl, Br and I.

32 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a review of neuromimetic processes, including neuronal dynamics, synaptic plasticity, and higher-level signal and information processing, along with more sophisticated implementations, including signal processing, speech recognition and data security.
Abstract: The story of information processing is a story of great success. Todays' microprocessors are devices of unprecedented complexity and MOSFET transistors are considered as the most widely produced artifact in the history of mankind. The current miniaturization of electronic circuits is pushed almost to the physical limit and begins to suffer from various parasitic effects. These facts stimulate intense research on neuromimetic devices. This feature article is devoted to various in materio implementation of neuromimetic processes, including neuronal dynamics, synaptic plasticity, and higher-level signal and information processing, along with more sophisticated implementations, including signal processing, speech recognition and data security. Due to vast number of papers in the field, only a subjective selection of topics is presented in this review.

19 citations

Journal ArticleDOI
TL;DR: To the best of the authors' knowledge, this contribution presents the simplest hardware realization of a classification system capable of performing neural network tasks without any sophisticated data processing.
Abstract: Materials exhibiting memory or those capable of implementing certain learning schemes are the basic building blocks used in hardware realizations of the neuromorphic computing. One of the common goals within this paradigm assumes the integration of hardware and software solutions, leading to a substantial efficiency enhancement in complex classification tasks. At the same time, the use of unconventional approaches towards signal processing based on information carriers other than electrical carriers seems to be an interesting trend in the design of modern electronics. In this context, the implementation of light-sensitive elements appears particularly attractive. In this work, we combine the abovementioned ideas by using a simple optoelectronic device exhibiting a short-term memory for a rudimentary classification performed on a handwritten digits set extracted from the Modified National Institute of Standards and Technology Database (MNIST)(being one of the standards used for benchmarking of such systems). The input data was encoded into light pulses corresponding to black (ON-state) and white (OFF-state) pixels constituting a digit and used in this form to irradiate a polycrystalline cadmium sulfide electrode. An appropriate selection of time intervals between pulses allows utilization of a complex kinetics of charge trapping/detrapping events, yielding a short-term synaptic-like plasticity which in turn leads to the improvement of data separability. To the best of our knowledge, this contribution presents the simplest hardware realization of a classification system capable of performing neural network tasks without any sophisticated data processing.

15 citations

Journal ArticleDOI
TL;DR: This work states that the material-based workhorse for current hardware platforms is largely based on standard CMOS technologies, intrinsically following the von-Neumann-Turing prescription; the authors do know that the brain hardware operates in a massively parallel way through a densely interconnected physical network of neurons.
Abstract: The enormous amount of data generated nowadays worldwide is increasingly triggering the search for unconventional and more efficient ways of processing and classifying information, eventually able to transcend the conventional von-Neumann-Turing computational central dogma. It is, therefore, greatly appealing to draw inspiration from less conventional but computationally more powerful systems such as the neural architecture of the human brain. This neuromorphic route has the potential to become one of the most influential and long-lasting paradigms in the field of unconventional computing. The material-based workhorse for current hardware platforms is largely based on standard CMOS technologies, intrinsically following the above mentioned von-Neumann-Turing prescription; we do know, however, that the brain hardware operates in a massively parallel way through a densely interconnected physical network of neurons. This requires challenging the intrinsic definition of the single units and the architecture of computing machines. (...)

13 citations


Cited by
More filters
Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: The application of ferroelectric materials (i.e. solids that exhibit spontaneous electric polarisation) in solar cells has a long and controversial history as mentioned in this paper, and the recent successful application of inorganic and hybrid perovskite structured materials (e.g. BiFeO3, CsSnI3, CH3NH3PbI3) emphasises that polar semiconductors can be used in conventional photovoltaic architectures.
Abstract: The application of ferroelectric materials (i.e. solids that exhibit spontaneous electric polarisation) in solar cells has a long and controversial history. This includes the first observations of the anomalous photovoltaic effect (APE) and the bulk photovoltaic effect (BPE). The recent successful application of inorganic and hybrid perovskite structured materials (e.g. BiFeO3, CsSnI3, CH3NH3PbI3) in solar cells emphasises that polar semiconductors can be used in conventional photovoltaic architectures. We review developments in this field, with a particular emphasis on the materials known to display the APE/BPE (e.g. ZnS, CdTe, SbSI), and the theoretical explanation. Critical analysis is complemented with first-principles calculation of the underlying electronic structure. In addition to discussing the implications of a ferroelectric absorber layer, and the solid state theory of polarisation (Berry phase analysis), design principles and opportunities for high-efficiency ferroelectric photovoltaics are presented.

248 citations

01 Jan 2019
TL;DR: The memristor can be defined as any 2-terminal device that exhibits the fingerprints of "pinched" hysteresis loops in the v-i plane as discussed by the authors.
Abstract: From a pedagogical point of view, the memristor is defined in this tutorial as any 2-terminal device obeying a state-dependent Ohm’s law. This tutorial also shows that from an experimental point of view, the memristor can be defined as any 2-terminal device that exhibits the fingerprints of “pinched” hysteresis loops in the v–i plane. It also shows that memristors endowed with a continuum of equilibrium states can be used as non-volatile analog memories. This tutorial shows that memristors span a much broader vista of complex phenomena and potential applications in many fields, including neurobiology. In particular, this tutorial presents toy memristors that can mimic the classic habituation and LTP learning phenomena. It also shows that sodium and potassium ion-channel memristors are the key to generating the action potential in the Hodgkin-Huxley equations, and that they are the key to resolving several unresolved anomalies associated with the Hodgkin-Huxley equations. This tutorial ends with an amazing new result derived from the new principle of local activity, which uncovers a minuscule life-enabling Goldilocks zone, dubbed the edge of chaos, where complex phenomena, including creativity and intelligence, may emerge. From an information processing perspective, this tutorial shows that synapses are locally-passive memristors, and that neurons are made of locally-active memristors.

135 citations