[HTML][HTML] Prediction of time series gene expression and structural analysis of gene regulatory networks using recurrent neural networks
Methods for time series prediction and classification of gene regulatory networks (GRNs)
from gene expression data have been treated separately so far. The recent emergence of …
from gene expression data have been treated separately so far. The recent emergence of …
[HTML][HTML] A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG
The architecture of communication within the brain, represented by the human connectome,
has gained a paramount role in the neuroscience community. Several features of this …
has gained a paramount role in the neuroscience community. Several features of this …
[HTML][HTML] Photonic Stochastic Emergent Storage for deep classification by scattering-intrinsic patterns
Disorder is a pervasive characteristic of natural systems, offering a wealth of non-repeating
patterns. In this study, we present a novel storage method that harnesses naturally-occurring …
patterns. In this study, we present a novel storage method that harnesses naturally-occurring …
Satisfiability transition in asymmetric neural networks
F Aguirre-López, M Pastore… - Journal of Physics A …, 2022 - iopscience.iop.org
Asymmetry in the synaptic interactions between neurons plays a crucial role in determining
the memory storage and retrieval properties of recurrent neural networks. In this work, we …
the memory storage and retrieval properties of recurrent neural networks. In this work, we …
Network hierarchy and pattern recovery in directed sparse Hopfield networks
Many real-world networks are directed, sparse, and hierarchical, with a mixture of
feedforward and feedback connections with respect to the hierarchy. Moreover, a small …
feedforward and feedback connections with respect to the hierarchy. Moreover, a small …
[HTML][HTML] TOLOMEO, a novel machine learning algorithm to measure information and order in correlated networks and predict their state
M Miotto, L Monacelli - Entropy, 2021 - mdpi.com
We present ToloMEo (TOpoLogical netwOrk Maximum Entropy Optimization), a program
implemented in C and Python that exploits a maximum entropy algorithm to evaluate …
implemented in C and Python that exploits a maximum entropy algorithm to evaluate …
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 …
brain topology, recovers the scaling picture recently introduced by Deco et al., according to …
Photonic Stochastic Emergent Storage: Exploiting Scattering-intrinsic Patterns for Programmable Deep Classification
Disorder is a pervasive characteristic of natural systems, offering a wealth of non-repeating
patterns. In this study, we present a novel storage method that harnesses naturally-occurring …
patterns. In this study, we present a novel storage method that harnesses naturally-occurring …
Brain memory working. Optimal control behavior for improved Hopfield-like models
Several authors have recently highlighted the need for a new dynamical paradigm in the
modelling of brain working and evolution. In particular, the models should include the …
modelling of brain working and evolution. In particular, the models should include the …
Prediction of gene expression time series and structural analysis of gene regulatory networks using recurrent neural networks
Methods for time series prediction and classification of gene regulatory networks (GRNs)
from gene expression data have been treated separately so far. The recent emergence of …
from gene expression data have been treated separately so far. The recent emergence of …