5 research outputs found
Energy efficiency of information transmission by electrically coupled neurons
The generation of spikes by neurons is energetically a costly process. This
paper studies the consumption of energy and the information entropy in the
signalling activity of a model neuron both when it is supposed isolated and
when it is coupled to another neuron by an electrical synapse. The neuron has
been modelled by a four dimensional Hindmarsh-Rose type kinetic model for which
an energy function has been deduced. For the isolated neuron values of energy
consumption and information entropy at different signalling regimes have been
computed. For two neurons coupled by a gap junction we have analyzed the roles
of the membrane and synapse in the contribution of the energy that is required
for their organized signalling. Computational results are provided for cases of
identical and nonidentical neurons coupled by unidirectional and bidirectional
gap junctions. One relevant result is that there are values of the coupling
strength at which the organized signalling of two neurons induced by the gap
junction takes place at relatively low values of energy consumption and the
ratio of mutual information to energy consumption is relatively high.
Therefore, communicating at these coupling values could be energetically the
most efficient option
Computational Modeling Under Uncertainty: Challenges and Opportunities
peer reviewedComputational Biology has increasingly become an important tool for biomedical and translational research. In particular, when generating novel hypothesis despite fundamental uncertainties in data and mechanistic understanding of biological processes underpinning diseases. While in the present book, we have reviewed the necessary background and existing novel methodologies that set the basis for dealing with uncertainty, there are still many “grey”, or less well-defined, areas of investigations offering both challenges and opportunities. This final chapter in the book provides some reflections on those areas, namely: (1) the need for novel robust mathematical and statistical methodologies to generate hypothesis under uncertainty; (2) the challenge of aligning those methodologies in a context that requires larger computational resources; (3) the accessibility of modeling tools for less mathematical literate researchers; and (4) the integration of models with –omics data and its application in clinical environments