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3D Modeling of Dendritic Spines with Synaptic Plasticity

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Free energy and dendritic self-organization.

Stefan J Kiebel¹, Karl J Friston

¹Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.

Frontiers in Systems Neuroscience

|October 21, 2011

Summary

Neurons selectively filter inputs by pruning dendritic spines to minimize surprise, aligning with free-energy principles. This process optimizes neuronal processing and connects it to machine learning methods.

Keywords:

Bayesian inference dendrite dendritic computation free energy multi-scale non-linear dynamical system single neuron synaptic reconfiguration

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Area of Science:

Computational Neuroscience
Neuroscience
Machine Learning

Background:

Single neurons exhibit selectivity for specific presynaptic input sequences via dendritic filtering.
Understanding the mechanistic basis of this neuronal input selectivity is crucial.

Purpose of the Study:

To provide a principled and mechanistic account of neuronal input selectivity using a free-energy principle.
To model how dendrites self-organize to minimize surprise from presynaptic inputs.

Main Methods:

Application of a variational free-energy principle to a dendrite.
Modeling selective pruning of dendritic spines based on postsynaptic gain thresholds.
Optimizing postsynaptic gain with respect to free energy.

Main Results:

Dendritic spine pruning occurs when postsynaptic gain falls below a threshold, driven by free-energy minimization.
This pruning mechanism allows dendrites to select presynaptic signals that match their internal generative models.
Neuronal processing is linked to surprise minimization and Bayesian inference principles.

Conclusions:

The study offers a principled explanation for how neurons achieve selective sampling of presynaptic inputs.
Connects elemental neuronal processing (dendritic filtering) to broader computational principles like Bayesian model selection.
Highlights the role of free-energy minimization in neuronal organization and information processing.