Li Wenyuan, Ovchinnikov Igor V, Chen Honglin, Wang Zhe, Lee Albert, Lee Houchul, Cepeda Carlos, Schwartz Robert N, Meier Karlheinz, Wang Kang L
Department of Electrical Engineering, UCLA, Los Angeles, CA 90095, U.S.A.
Department of Mathematics, UCLA, Los Angeles, CA 90095, U.S.A.
Neural Comput. 2018 Sep;30(9):2418-2438. doi: 10.1162/neco_a_01103. Epub 2018 Jun 12.
The extreme complexity of the brain has attracted the attention of neuroscientists and other researchers for a long time. More recently, the neuromorphic hardware has matured to provide a new powerful tool to study neuronal dynamics. Here, we study neuronal dynamics using different settings on a neuromorphic chip built with flexible parameters of neuron models. Our unique setting in the network of leaky integrate-and-fire (LIF) neurons is to introduce a weak noise environment. We observed three different types of collective neuronal activities, or phases, separated by sharp boundaries, or phase transitions. From this, we construct a rudimentary phase diagram of neuronal dynamics and demonstrate that a noise-induced chaotic phase (N-phase), which is dominated by neuronal avalanche activity (intermittent aperiodic neuron firing), emerges in the presence of noise and its width grows with the noise intensity. The dynamics can be manipulated in this N-phase. Our results and comparison with clinical data is consistent with the literature and our previous work showing that healthy brain must reside in the N-phase. We argue that the brain phase diagram with further refinement may be used for the diagnosis and treatment of mental disease and also suggest that the dynamics may be manipulated to serve as a means of new information processing (e.g., for optimization). Neuromorphic chips, similar to the one we used but with a variety of neuron models, may be used to further enhance the understanding of human brain function and accelerate the development of neuroscience research.
