Reservoir computing is a paradigm in machine learning whose processing capabilities rely on the dynamical behavior of recurrent neural networks. We present a mixed analog and digital implementation of this concept with a nonlinear analog electronic circuit as a main computational unit. In our approach, the reservoir network can be replaced by a single nonlinear element with delay via time-multiplexing. We analyze the influence of noise on the performance of the system for two benchmark tasks: 1) a classification problem and 2) a chaotic time-series prediction task. Special attention is given to the role of quantization noise, which is studied by varying the resolution in the conversion interface between the analog and digital worlds.

Consciousness remains a formidable challenge. Different theories of consciousness have proposed different mechanisms to account for phenomenal experience. Here, appealing to Global Workspace Theory, Higher-Order Theories, Social Theories, and Predictive Processing, we introduce a novel framework -the Self-Organizing Metarerpresentational Account (SOMA), in which consciousness is viewed as something that the brain learns to do. The brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of metarepresentations that qualify target first-order representations. Experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. Thus, consciousness is the brain's (unconscious, embodied, enactive, non-conceptual) theory about itself.

We present a novel encryption scheme, wherein an encryption key is generated by two distant complex nonlinear units, forced into synchronization by a chaotic driver. The concept is sufficiently generic to be implemented on either photonic, optoelectronic or electronic platforms. The method for generating the key bitstream from the chaotic signals is reconfigurable. Although derived from a deterministic process, the obtained bit series fulfill the randomness conditions as defined by the National Institute of Standards test suite. We demonstrate the feasibility of our concept on an electronic delay oscillator circuit and test the robustness against attacks using a state-of-the-art system identification method.

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