The human brain is one of the most energy-efficient intelligent systems in nature. With only about 20 watts of metabolic power, it supports perception, memory, learning, thinking and motor control. Yet how the brain performs such complex information handling at such low energy cost remains a fundamental question.
In a recent study published in National Science Review , Ma and Guo from Nanjing University of Aeronautics and Astronautics proposed a new theoretical framework called highly energy-efficient information-handling dynamics of the brain. The work offers a physical perspective on how neural systems may store and process large amounts of information through Dynamic electrophysiological activities.
Traditional descriptions of brain function often focus on how individual neurons generate action potentials, how synapses transmit signals, or how brain regions interact. These approaches have greatly advanced neuroscience, but they do not fully explain why the brain is so energy efficient. The new study shifts attention from isolated neural events to the spatio-temporal organization of neural ensembles.
In the study, local functional neural circuits were abstracted as neural ensembles. Biological neurons were further parameterized as digital neurons containing both morphological and electrophysiological information. Based on an energy-minimization principle, the researchers constructed a neural sphere model that unifies structure and function, allowing simulation of local brain activity under metabolic constraints. Through systematic simulations, the team examined neural ensembles with different topological connections, initial potential states and neuronal scales. They found that neuronal populations can generate rich electrophysiological activities with periodic features. These results suggest that information in the brain may not be stored only in single neurons or synapses. Instead, it may be encoded in dynamic electrical patterns formed by the cooperation of many neurons. To explain these results, the researchers introduced concepts from nonlinear dynamics, chaos theory and fractal theory. In their framework, different inputs correspond to different initial conditions of a neuronal ensemble. These initial conditions can evolve into periodic potential waveforms, which act as dynamical carriers of information. Such waveforms can both preserve information and participate in further processing.
The framework also led to striking quantitative estimates. The maximum storage capacity of the human brain was predicted to reach 7.48 × 10 18 bytes, about three orders of magnitude higher than previous estimates based on the linear summation of synaptic states. The maximum computational power was estimated to be 6.24 × 10 18 floating-point operations per second, equivalent to approximately 78,000 modern graphics processing units. Even more remarkably, the energy required for computation and communication in the human brain was estimated to be only about 1.26 times the Landauer limit, the physical lower bound for information erasure. This corresponds to an energy efficiency of up to 79%, suggesting that the brain may operate close to a thermodynamic limit for information processing. However, the energy consumption of the current advanced artificial intelligence chips exceeds the Landauer limit by 10 9 times. Compared to the elegant natural intelligent systems, there is a significant potential for improvement in energy efficiency in current semiconductor chip technology.
The authors provided an inspiring physical picture: the brain’s efficiency may arise not only from its biological components, but also from the way information is organized in time, space and collective dynamics. This theory may open new directions for brain science and provide inspiration for more energy-efficient brain-inspired computing architectures.
National Science Review
Computational simulation/modeling