Speaker
Description
In the traditional view, we often see a single neuron as less computationally efficient
than a multilayer artificial neural network. But is this truly the case? Our investigation delves
deep into the computational efficiency of morphologically complex neurons, especially their
ability to distinguish between different synaptic patterns. We posed a question: What's the
simplest dendritic structure that can master tasks usually reserved for multilayered artificial
networks? This exploration not only challenges long-held beliefs about single neuron
capabilities but also bridges the gap between biological and artificial neural computation.
Furthermore, building upon the foundational homeostatic models pioneered by Eve
Marder, we introduced an enhanced model tailored for morphologically complex neurons.
Central to our method is the fine-tuning of diverse ion channel composition throughout the
whole dendritic tree, ensuring a good balance of homeostatic activity.
Our findings reveal that training to recognize synaptic patterns and the homeostatic
tuning of ion channels can be unified under one computational strategy. This perspective
encourages a more holistic understanding of dendritic tree adaptation, encompassing both
synaptic and ion channel modifications. In essence, our study offers a fresh lens through
which to understand neuronal learning, merging the worlds of artificial and biological neural
networks.
