Dynamics and information encoding in adaptive neural networks

The efficacy of computation in any medium depends greatly on how information is encoded. Biological neural networks capture information and carry out computation in linked cell groups rather than individual neurons. Biocomputing is also known for its rapid adaptation and learning from limited data. We propose that these unique characteristics are key to the observed intelligence in living beings, and thus we study them as foundations responsible for collective, adaptive information processing and learning. In this study we perform optogenetic stimulation and calcium imaging on living neural networks to explore how complex dynamical information is encoded in groups of neurons. Furthermore, we observe how this encoding evolves over time due to network plasticity.