www.nature.com/articles/s41...
www.nature.com/articles/s41...
Code: github.com/netneurolab/...
Code: github.com/netneurolab/...
We implement dMRI brain networks as reservoirs. Again, hierarchical modularity positively contributes to computational performance.
Amazingly, reservoir timescales correlate with empirical timescales derived from MEG.
We implement dMRI brain networks as reservoirs. Again, hierarchical modularity positively contributes to computational performance.
Amazingly, reservoir timescales correlate with empirical timescales derived from MEG.
Again, we find that higher-order hierarchical modular networks consistently outperform their lower-order counterparts.
Again, we find that higher-order hierarchical modular networks consistently outperform their lower-order counterparts.
More complex motifs containing at least three edges are all enriched in higher-order hierarchical modular networks, supporting more complex computations.
More complex motifs containing at least three edges are all enriched in higher-order hierarchical modular networks, supporting more complex computations.
Higher-order hierarchical modular reservoirs show more variability in timescales, yielding a bigger pool of timescales and richer temporal expansion of input signals.
Higher-order hierarchical modular reservoirs show more variability in timescales, yielding a bigger pool of timescales and richer temporal expansion of input signals.
Higher-order hierarchical modular networks consistently perform best, particularly at criticality.
Higher-order hierarchical modular networks consistently perform best, particularly at criticality.
We then implement them as reservoirs to evaluate their cognitive capacity.
We then implement them as reservoirs to evaluate their cognitive capacity.
code: github.com/netneurolab/...
code: github.com/netneurolab/...