Research in Computational Neuroscience aims at developing detailed biophysical and abstract theoretical models of single cells, microcircuits and large neuronal networks in order to understand information processing and memory formation in the brain. Specifically, we built computational models and use them to investigate the biophysical and morphological mechanisms underlying learning and memory processes in various brain regions (hippocampus, prefrontal cortex, amygdala), paying particular attention to dendritic computations.
Computational modeling of the formation of memory traces.
One of the goals of neuroscience is to understand the process via which memories are encoded and stored in the brain. Recent experiments have demonstrated how memories are encoded in specific neuron groups in the brain. Traditionally, it is thought that the strengthening of synaptic connections via synaptic plasticity is the mechanism underlying memory formation in the cortex. New insights indicate that other factors, such as neuronal excitability and competition among neurons may crucially affect the formation of a memory trace. The transcription factor CREB has been shown to modulate the probability of allocation of memory to specific groups of neurons in the Lateral Amygdala. The goal of our computational work is to investigate the process of memory allocation and the properties of the memory trace. By creating a large scale computational model of the lateral amygdala, we aim to investigate how the modulation of excitability, synaptic plasticity, homeostatic plasticity and neuronal inhibition affect the formation of fear memories in the lateral amygdala. These simulations can provide insights in the relationships between memory traces and the role of CREB. Our results can be useful in understanding the outcomes of related behavioral and electrophysiological studies.