Creation and Annihilation of Magnetic Skyrmions for Neuromorphic Computing Applications

In this work we present the creation, annihilation and dynamics of a topologically protected magnetic structure, a skyrmion, for neuromorphic computing application. We study the effect of Dzyaloshinskii Moriya interaction (DMI) and surface anisotropy on the skyrmion density. The relation between skyrmion annihilation threshold anisotropy Kth and DMI coefficient is evaluated. Furthermore, the skyrmion diameter dependence on these two parameters is studied. Using MOKE analysis we study the effect of external magnetic field on the skyrmion density and predict the threshold magnetic field for the transition of magnetic texture from Labriynth domains to skyrmions. These results are further supported by the MuMax simulations. The spin orbit torque SOT manipulation of skyrmion size and density is also presented for skyrmion applications in the race-track memory and neuromorphic computing. Motivated by the results, we propose a Skyrmionic neuromorphic device and using SOT switching mechanism, show its applicability as spintronic synapse and neuron. The MuMax simulations are coupled to the Non- Equilibrium Green’s Function formalism to model the neuron and synapse behavior. Finally, we conclude with the possibility of using these devices for pattern recognition and other unconventional computing paradigms.

Creation and Annihilation of Magnetic Skyrmions for Neuromorphic Computing Applications

In this work we present the creation, annihilation and dynamics of a topologically protected magnetic structure, a skyrmion, for neuromorphic computing application. We study the effect of Dzyaloshinskii Moriya interaction (DMI) and surface anisotropy on the skyrmion density. The relation between skyrmion annihilation threshold anisotropy Kth and DMI coefficient is evaluated. Furthermore, the skyrmion diameter dependence on these two parameters is studied. Using MOKE analysis we study the effect of external magnetic field on the skyrmion density and predict the threshold magnetic field for the transition of magnetic texture from Labriynth domains to skyrmions. These results are further supported by the MuMax simulations. The spin orbit torque SOT manipulation of skyrmion size and density is also presented for skyrmion applications in the race-track memory and neuromorphic computing. Motivated by the results, we propose a Skyrmionic neuromorphic device and using SOT switching mechanism, show its applicability as spintronic synapse and neuron. The MuMax simulations are coupled to the Non- Equilibrium Green’s Function formalism to model the neuron and synapse behavior. Finally, we conclude with the possibility of using these devices for pattern recognition and other unconventional computing paradigms.

Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing

Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex are organized in cortical columns that behave as fuzzy sets. Fuzzy sets theory has embraced uncertainty modeling when membership functions have been reinterpreted as possibility distributions. The terms of Bayes’ formula are conceivable as fuzzy sets and Bayes’ inference becomes a fuzzy inference. According to the QBism, quantum probabilities are also Bayesian. They are logical constructs rather than physical realities. It derives that the Born rule is nothing but a kind of Quantum Law of Total Probability. Wavefunctions and measurement operators are viewed epistemically. Both of them are similar to fuzzy sets. The new link that is established between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability could spark new ideas for the development of artificial intelligence and unconventional computing.

Mining Logical Circuits in Fungi

Living substrates are capable for nontrivial mappings of electrical signals due to the substrate nonlinear electrical characteristics. This property can be used to realize Boolean functions. Input logical values are represented by amplitude or frequency of electrical stimuli. Output logical values are decoded from electrical responses of living substrates. We demonstrate how logical circuits can be implemented in mycelium bound composites. The mycelium bound composites (fungal materials) are getting growing recognition as building, packaging, decoration and clothing materials. Presently the fungal materials are passive. To make the fungal materials adaptive, i.e. sensing and computing, we should embed logical circuits into them. We demonstrate experimental laboratory prototypes of many-input Boolean functions implemented in fungal materials from oyster fungi P. ostreatus. We characterize complexity of the functions discovered via complexity of the space-time configurations of one-dimensional cellular automata governed by the functions. We show that the mycelium bound composites can implement representative functions from all classes of cellular automata complexity including the computationally universal. The results presented will make an impact in the field of unconventional computing, experimental demonstration of purposeful computing with fungi, and in the field of intelligent materials, as the prototypes of computing mycelium bound composites.