# A biophysical minimal model to investigate age-related changes in CA1 pyramidal cell electrical activity

## TL;DR

This paper introduces a biophysical model to study how aging affects the electrical activity of brain cells, specifically in the hippocampus.

## Contribution

The paper presents a novel minimal biophysical model of CA1 pyramidal cells to investigate age-related changes in their electrical activity.

## Key findings

- The model reproduces variability in electrical activity seen in CA1 pyramidal cells by adjusting ion channel parameters.
- Increased L-type Ca2+ channel expression in the model mimics age-related changes observed in aged animals.
- The model predicts new age-related changes in PC bursting activity not previously reported.

## Abstract

Aging is a physiological process that is still poorly understood, especially with respect to effects on the brain. There are open questions about aging that are difficult to answer with an experimental approach. Underlying challenges include the difficulty of recording in vivo single cell and network activity simultaneously with submillisecond resolution, and brain compensatory mechanisms triggered by genetic, pharmacologic, or behavioral manipulations. Mathematical modeling can help address some of these questions by allowing us to fix parameters that cannot be controlled experimentally and investigate neural activity under different conditions. We present a biophysical minimal model of CA1 pyramidal cells (PCs) based on general expressions for transmembrane ion transport derived from thermodynamical principles. The model allows directly varying the contribution of ion channels by changing their number. By analyzing the dynamics of the model, we find parameter ranges that reproduce the variability in electrical activity seen in PCs. In addition, increasing the L-type Ca2+ channel expression in the model reproduces age-related changes in electrical activity that are qualitatively and quantitatively similar to those observed in PCs from aged animals. We also make predictions about age-related changes in PC bursting activity that, to our knowledge, have not been reported previously. We conclude that the model’s biophysical nature, flexibility, and computational simplicity make it a potentially powerful complement to experimental studies of aging.