Physics Clues on the Mind Substrate and Attributes

Abstract

The last decade has witnessed a remarkable progress in our understanding of the brain. This has mainly been based on the scrutiny and modeling of the transmission of activity among neurons across lively synapses. A main conclusion, thus far, is that essential features of the mind rely on collective phenomena that emerge from a willful interaction of many neurons that, mediating other cells, form a complex network whose details keep constantly adapting to their activity and surroundings. In parallel, theoretical and computational studies developed to understand many natural and artificial complex systems, which have truthfully explained their amazing emergent features and precise the role of the interaction dynamics and other conditions behind the different collective phenomena they happen to display. Focusing on promising ideas that arise when comparing these neurobiology and physics studies, the present perspective article shortly reviews such fascinating scenarios looking for clues about how high-level cognitive processes such as consciousness, intelligence, and identity can emerge. We, thus, show that basic concepts of physics, such as dynamical phases and non-equilibrium phase transitions, become quite relevant to the brain activity while determined by factors at the subcellular, cellular, and network levels. We also show how these transitions depend on details of the processing mechanism of stimuli in a noisy background and, most important, that one may detect them in familiar electroencephalogram (EEG) recordings. Thus, we associate the existence of such phases, which reveal a brain operating at (non-equilibrium) criticality, with the emergence of most interesting phenomena during memory tasks.

Stochastic resonance in 2D materials based memristors

“…SR was first proposed in 1980 by Benzi et al 1 using numerical methods, and later studies applied the concept of SR in a wide variety of research fields, such as medical care 2 and biology 3,4 . Within this last one, SR is particularly useful for sensory neurobiology, and it has been employed for explaining the animal behaviours that lead to evolutionary success [3][4][5][6][7][8] . For example, paddlefishes use SR to detect food with their rostrum (an optimal amount of noise allows them to capture more plankton from their surroundings) 9 , and crickets use SR to escape from predator wasps 10 .…”

“…However, realizing solid-state micro/nano-electronic devices for the hardware implementation of electronic neurons and synapses exhibiting SR can be challenging. SR has been observed in Schmitt triggers 12 , tunnel diodes 13 , phase change materials in networked nonlinear systems 7,8,14,15 , and different types of photodetectors 16 . In these devices the concept of resonance is connected to a maximum signal-to-noise ratio or an optimal noise frequency that produces a better output response than without the noise or with noise of other frequencies.…”