Neurocybernetics bridges the gap between neuroscience and cybernetics, employing methodologies from both fields to study and model complex neural systems. The objective is to understand how brains process information and control actions, and to possibly replicate such mechanisms in artificial systems.
Imagine you are on a boat, and you are trying to reach an island in the middle of a storm. You use a map (your brain) to perceive your surroundings and adjust your path according to the changing wind and waves (control system). Neurocybernetics is like being a really good sailor, who uses the information from the map judiciously, adjusting the controls, based on what’s happening around to reach the island safely.
Neurocybernetics is an interdisciplinary field combining knowledge from cybernetics and neuroscience to understand how the brain processes information, makes decisions and controls actions, with the goal of mimicking these capabilities in artificial systems. Cybernetics primarily focuses on systems’ control, communication, and information processing, whereas neuroscience dives into the neurobiological aspects of information processing in living organisms, mostly brains.
The field of neurocybernetics has contributed significantly to our understanding of the brain’s complexity and the mechanisms underlying cognition and behavior. For instance, studying how neural networks perform computations and how modifications in these networks can lead to changes in behavior has clarified many cognitive processes, including learning and memory.
Neurocybernetic models develop mathematical equations and algorithms that represent the structure and function of neural systems at various complexity levels, from individual neurons to complete neural networks. These models often utilize machine learning and AI techniques to refine and test their validity, incorporating a feedback loop – a hallmark of cybernetics – to make adjustments based on the model’s performance.
Modeling can either be data-driven (parameters derived from empirical data) or theory-driven (parameters derived from assimilating theoretical frameworks). These models serve as crucial tools in neuroscience for hypothesis testing, obtaining insights unattainable from experimental data alone, and guiding future experimental design.
Understanding and replicating brain mechanisms have several applications, including creating robust AI systems. For example, replicating decision-making processes can enhance AI’s capability for complex, dynamic environments. The field also aids in constructing advanced neuro-prosthetics, improving neural decoding for Brain-Machine Interfaces (BMI), and advancing therapeutic strategies in diseases like Alzheimer’s and Parkinson’s by providing insights into neural dysfunction.
While neurocybernetics has shown remarkable potential, it also confronts inherent challenges, including the enormous complexity of the brain, understanding and modeling non-linear dynamics and complexity in neural systems, and the ethical and societal implications of developing AI systems that mimic human cognition and behavior.