Search Results for author:
Found 5 papers, 1 papers with code
Evidence of Scaling Regimes in the Hopfield Dynamics of Whole Brain Model
It is shown that a Hopfield recurrent neural network, informed by experimentally derived brain topology, recovers the scaling picture recently introduced by Deco et al., according to which the process of information transfer within the human brain shows spatially correlated patterns qualitatively similar to those displayed by turbulent flows.
Determining cancer cells division strategy
Using our novel approach, we found that the dynamics of the size distribution of leukemia Jurkat T-cells is quantitatively reproduced by (i) a sizer-like division strategy, with (ii) division times following an Erlang distribution given by the sum of at least three independent exponentially-distributed times and (iii) fluctuations up to 15\% of the inherited fraction of size at division with respect to the mother cell size.
Electrostatic complementarity at the interface drives transient protein-protein interactions
Understanding the molecular mechanisms driving the binding between bio-molecules is a crucial challenge in molecular biology.
Prediction of gene expression time series and structural analysis of gene regulatory networks using recurrent neural networks
We show that the prediction is extremely accurate for GRNs with different architectures.
Effect of dilution in asymmetric recurrent neural networks
These attractors form the set of all the possible limit behaviors of the neural network.
