Alexander D. Bird

This paper applies control theory to dendritic structures, showing how morphology and input organization affect resilience and energy cost, with implications for neuronal function.

The paper develops an energy-dependent integrate-and-fire model to explain how neurons adapt their biophysical parameters under metabolic stress to maintain an optimal energy-information trade-off.

The paper evaluates a computational model of hippocampal granule cells using information theory, finding they balance energy efficiency and functionality along a Pareto optimal front.

The paper shows that neuronal parameter diversity is structured by energy-information trade-offs, forming a Pareto front in parameter space.

The paper introduces information-theoretic measures for pattern separation that are robust and reliable, demonstrating their application in dentate granule cells and showing how structural changes affect performance.

The paper shows that the skewed spine size distribution in neurons is not affected by synaptic activity or plasticity and forms early in neuronal development.

The paper shows that neuronal models with more ion channel types have more stable and viable parameter combinations, suggesting complexity aids in achieving target excitability.

The paper shows that neuronal excitability in response to synaptic inputs is invariant to dendritic length, with voltage responses depending only on dendritic diameter and conductivity.

The paper introduces dendritic normalisation to improve learning in sparsely connected neural networks, showing significant performance gains over existing methods.

The paper develops an equation to estimate potential synaptic contacts in cortical neurons and finds an excess of putative connections that enable subcellular-level connectivity.