The NEURON simulation environment
Neural Computation
Being There: Putting Brain, Body, and World Together Again
Being There: Putting Brain, Body, and World Together Again
Metalearning and neuromodulation
Neural Networks - Computational models of neuromodulation
Neuromodulation, theta rhythm and rat spatial navigation
Neural Networks - Computational models of neuromodulation
Neuromodulation and Plasticity in an autonomous robot
Neural Networks - Computational models of neuromodulation
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
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Without a doubt the most sophisticated behavior seen in biological agents is demonstrated by organisms whose behavior is guided by a nervous system. Thus, the construction of behaving devices based on principles of nervous systems may have much to offer. Our group has built series of brain-based devices (BBDs) over the last fifteen years to provide a heuristic for studying brain function by embedding neurobiological principles on a physical platform capable of interacting with the real world. These BBDs have been used to study perception, operant conditioning, episodic and spatial memory, and motor control through the simulation of brain regions such as the visual cortex, the dopaminergic reward system, the hippocampus, and the cerebellum. Following the brain-based model, we argue that an intelligent machine should be constrained by the following design principles: (i) it should incorporate a simulated brain with detailed neuroanatomy and neural dynamics that controls behavior and shapes memory, (ii) it should organize the unlabeled signals it receives from the environment into categories without a priori knowledge or instruction, (iii) it should have a physical instantiation, which allows for active sensing and autonomous movement in the environment, (iv) it should engage in a task that is initially constrained by minimal set of innate behaviors or reflexes, (v) it should have a means to adapt the device's behavior, called value systems, when an important environmental event occurs, and (vi) it should allow comparisons with experimental data acquired from animal nervous systems. Like the brain, these devices operate according to selectional principles through which they form categorical memory, associate categories with innate value, and adapt to the environment. This approach may provide the groundwork for the development of intelligent machines that follow neurobiological rather than computational principles in their construction.