Description

Learning in the brain involves the intricate modeling of regularities and causal relationships among various sensory-motor inputs and internal brain states. Our brains must establish associations between sensory inputs presented over behavioral timescales ranging from seconds to minutes. However, microscopic biological learning models, such as Hebbian learning, which suggests that the simultaneous firing of neurons strengthens their connections, mainly operate on a timescale of milliseconds. Various mechanisms can potentially bridge these two scales. Persistence and slow decay of the activity of neuronal assemblies representing stimuli can lead to the simultaneous activity of neuronal assemblies that encode different stimuli separated by several seconds. Another option is the slow decay and rise of concentrations of certain chemicals like calcium or the presence of neurotransmitters, which enlarge the time window of plasticity. This can itself be thought of as an effective behavioral-scale plasticity rule. Another potential mechanism is that our neuronal system generates repetitions of sequences of encoded states at the millisecond scale. Consequently, the replay of these temporally compressed sequences can facilitate the formation of temporal associative sequences through synaptic plasticity rules. Understanding how these sequences are initially generated and the mechanisms behind their replay constitutes an active research area in the field of memory formation.

Of particular interest to our research group is the generation of associative neuronal sequences in the context of spatial learning tasks and navigation. We aim to provide biologically plausible models explaining how spatial information is encoded, stored, and retrieved in the brain, shedding light on the fundamental processes underlying spatial cognition.

Requirements

Python programming skills are required.

Contact

Masud Ehsani (masud.ehsani@rub.de)