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.

Neural morphology & function

- Ongoing

Investigating the role of structural plasticity in information processing capabilities of the hippocampal pyramidal neurons.

We are interested in investigating the active role of dendrites in synaptic integration and information processing by the pyramidal neurons of the CA1 and CA3 subregions in hippocampus. The determination of their distinct electrophysiological profile is critically dependent on two primary factors: the morphological characteristics of the dendritic arbor and the biophysical mechanisms embedded into the excitable membrane. At first, we developed a computational tool named REMOD ( that is aimed for the structural remodeling and morphometric analysis of neuronal reconstructions and, currently, we are validating biophysically realistic CA1 and CA3 pyramidal model neurons by using recently acquired morphological and electrophysiological recording data. Following, we intend to dissect the potential effect that this kind of structural plasticity exerts on neural processing and on the unique pattern completion/separation functions that naturally arise from the network-level hippocampal activity. This line of research is based on solid experimental work and breaks new ground in deciphering the effect of neuronal morphology in information processing capabilities of the brain, potentially leading to a better understanding of learning and memory.

The role of basal tree morphology in neuronal function.

This project seeks to investigate how morphological features of the basal tree shape dendritic integration. We have previously shown that specific morphological characteristics of the basal tree regulates the bursting or the regular spiking phenotype of layer 5 prefrontal cortex neurons (Psarrou et al, 2014). Now we investigate how the complexity of the basal tree mediates the functional segregation based on specific synaptic responses.

The role of dendritic functions in interneurons behavior.

The role of inhibition in regulation and proper function of neural circuits is one of the most challenging and open questions. Research on this topic focuses mainly on the effect of inhibitory neurons on the activity of principal neuronal populations, but not the opposite. Very little is known about the intrinsic and dendritic integration properties of inhibitory cells and how excitatory inputs shape their firing. In this work, we use computational modelling to characterize the dendritic integration properties of a fast spiking PV interneuron model, one of the main subtypes in layer V of the mammalian prefrontal cortex. A detailed biophysical model of a fast-spiking interneuron was created and extensively validated against experimental data. The model exhibits sodium spikes as well as supralinear calcium dynamics in its dendrites. These findings are in line with experimental data showing calcium-dependent nonlinear summation in PV interneurons and point to the ionic conductances that generate these non-linearities. Activation of uniformly distributed synapses along the dendrites generates two major modes of dendritic operations, the supralinear and the sublinear (Tzilivaki et al 2015).
It has been suggested that, due to their widespread connectivity, fast-spiking interneurons act as a uniform inhibitory blanket across the cortex and exhibit limited specificity. However their integrative properties at the single cell level remain unknown. As dendritic integration is key for neural computations, ongoing work investigates how different spatiotemporal patterns of synaptic activation may exploit interneurons nonlinear integration profile to shape the output of the PV interneuron and to maximize its dynamic range.