Spatial features of synaptic adaptation affecting learning performance
This work addresses the challenge of optimizing learning in neural networks for computational neuroscience, but it appears incremental as it builds on existing models of synaptic adaptation.
The researchers tackled the problem of how synaptic adaptation affects learning performance in neural networks by developing a model where adaptation signals propagate through extracellular space, finding that even fully excitatory networks achieve very good learning performances for Boolean rules, with performance highly sensitive to the extent of adaptation and spatial range of connections.
Recent studies have proposed that the diffusion of messenger molecules, such as monoamines, can mediate the plastic adaptation of synapses in supervised learning of neural networks. Based on these findings we developed a model for neural learning, where the signal for plastic adaptation is assumed to propagate through the extracellular space. We investigate the conditions allowing learning of Boolean rules in a neural network. Even fully excitatory networks show very good learning performances. Moreover, the investigation of the plastic adaptation features optimizing the performance suggests that learning is very sensitive to the extent of the plastic adaptation and the spatial range of synaptic connections.