WP4 - Network Identification and Characterization within V1 and PFC
| Participating partner | Total Pm per organization |
|---|---|
| FORTH | 16 |
| PAGNI | - |
| NEUROCOM | 1 |
| UZH | - |
| PROMETRONICS | - |
| UA | - |
| BRUKER | - |
| HARVARD | 2 |
| UCLA | 2 |
Tasks 4.1 - 4.5
- Data Preprocessing, sanitization, preparation (FORTH, ALL)
- Develop and apply new correlation metrics (FORTH, HARVARD)
- Identify the significant motifs/subnetworks within in V1 L4 & L2/3 before and after learning. (FORTH, proMetronics, HARVARD, UA, UCLA)
- Identify the significant motifs/subnetworks in PFC before and after learning
- Predict the motifs of the layer L2/3 based on the events of L4 using deep-learning algorithms, such as RNNs and LSM networks (FORTH, ProMetronics, Harvard)
Publications
D4.1 TR that describes the sanitization, preprocessing process (FORTH, PDE, PU, M36)
D4.2 TR with the methodology for identifying motifs and comparative analysis of the STTC, K-SVD, and RQA and their outcomes on V1 & PFC, respectively (FORTH, PDE, PU, M24/M24)
D4.3 TR/Scientific publ. with the methodology for identifying the significant motifs/subnetworks within in V1 L4 & L2/3 and PFC before and after learning, respectively and the main findings (FORTH, PDE, PU,36M)
D4.4 TR with the methodology for identifying the significant motifs/subnetworks within in PFC before and after learning and with the main findings (FORTH, PDE, PU,36M)
D4.5 TR/Scientific publ. with the deep-learning model for predicting the motifs of L2/3 from L4 and main results (FORTH, PDE, PU, 36M)