Name
Abstract | ||
|---|---|---|
In this paper an insect brain-inspired neural processing architecture was developed to be applied on board of a bio-robot for solving feature-to-action tasks. The system, accounting on visual features, is able to solve a classification problems using a spatial temporal approach that is typical of bio-inspired neural architectures. The proposed neural structure, taking inspiration from a specific neuropile of the insect brain, called mushroom bodies, is applied to solve tasks shown in insect experiments where non-elemental learning strategies are taken into account. An important peculiarity of the hidden processing layer of the proposed multi-layer architecture is the local, CNN-like connectivity among the spiking neurons, opening the way for an hardware implementation on neuromorphic chips. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ECCTD.2017.8093284 | 2017 European Conference on Circuit Theory and Design (ECCTD) |
Keywords | Field | DocType |
nonelemental learning strategies,multilayer architecture,feature-action learning,bio-robot,feature-to-action tasks,spatial temporal approach,neuropile,insect inspired spatial-temporal cellular processing,insect brain-inspired neural processing architecture,mushroom bodies,CNN-like connectivity | Architecture,Neural processing,Computer science,Visualization,Neuromorphic engineering,Action learning,Artificial intelligence,Mushroom bodies | Conference |
ISBN | Citations | PageRank |
978-1-5386-3975-7 | 1 | 0.37 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Arena | 1 | 261 | 47.43 |
| Luca Patané | 2 | 104 | 17.31 |
| Angelo Spinosa | 3 | 1 | 0.37 |