Control of network activity through neuronal response modulation
Neural network learning is typically treated as the problem of setting synaptic connection strengths to better perform a task. This requires supervision. However, anatomical data suggest that direct synaptic modification by such a supervisor circuit would be unfeasible. We investigate supervision at the level of neurons rather than synapses. By modulating the response properties of cells in the network, this form of supervised learning is able to successfully train a network to perform a function approximation task. We examine the nature of this modulation, and consider its implications for supervised learning.
modulation neural networks learning supervision