Description: Dynamical Neural Fields are mathematical models that describe the dynamics of neuronal activity across a spatial domain. These models focus on how neurons interact with each other and how these interactions can lead to patterns of activity that change over time. Unlike static models, Dynamical Neural Fields consider the temporal evolution of neuronal activity, allowing for a more realistic representation of cognitive and learning processes in the brain. These fields are characterized by their ability to model the propagation of activity through neural networks, taking into account factors such as excitation and inhibition, as well as the influence of spatial context. Their relevance lies in their application to understanding complex phenomena such as perception, memory, and decision-making, providing a theoretical framework that can be used to simulate and predict neuronal behaviors under various conditions. In the realm of neuromorphic computing, these models are fundamental for the development of systems that mimic the functionality of the human brain, enabling advances in artificial intelligence and robotics.
History: Dynamical Neural Fields were introduced in the 1990s by neuroscientist and psychologist David H. Ballard, who aimed to model complex cognitive processes. Over the years, these models have evolved and been refined, integrating concepts from neural network theory and neuroscience. Their development has been influenced by advances in understanding neuronal dynamics and neuromorphic computing, allowing for their application in various research areas.
Uses: Dynamical Neural Fields are primarily used in cognitive neuroscience research, where they help model processes such as visual perception, attention, and memory. They are also applied in the development of artificial intelligence systems that aim to mimic the functioning of the human brain, as well as in robotics, where they are used for movement control and decision-making in dynamic environments.
Examples: An example of the use of Dynamical Neural Fields is in the simulation of visual perception, where it models how visual stimuli are processed by the brain to generate a response. Another example is their application in robotic systems that require rapid and adaptive decision-making in changing environments, such as robots navigating complex spaces.
