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.
Structured connectivity and microcircuits function in the Prefrontal Cortex
PA is the spiking activity that persists beyond the stimulus presentation and is considered to be the cellular correlate of working memory. In the prefrontal cortex (PFC) in particular, pyramidal neurons were shown to form hyper-clusters, compared to other sensory regions. Yet, very little is known about the functional properties of these microcircuits and their role in Persistent Activity (PA). Motivated by the above this work probes the role of realistic connectivity constraints in shaping the functional output of PFC, through simulations of biophysically and morphologically detailed PFC circuits.
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.
Single-neuron model of persistent activity
In this work, we investigated how NMDA spikes in the basal dendrites of single PFC model neurons participate in the emergence of the persistent spiking acivity. We have pinpointed that input structure to the distal versus the proximal basal dendrites participates in this phenotype.
Microcircuit Models of Sustained Activity in the Prefrontal Cortex
Neurons in the prefrontal cortex display sustained activity in response to environmental or internal stimuli, that is continue to fire until the behavioral outcome or a reward signal. Mostly large-scale modeling studies have proposed intensive recurrence and slow excitation mediated by NMDA receptors as crucial mechanisms able to support the sustained excitation in these neurons. In addition, electrophysiological studies suggest that single-cell intrinsic currents also underlie the delayed excitation of prefrontal neurons. This project is focused on the interplay of both the computational and electrophysiological approaches in characterizing the activity observed at layer 5 prefrontal pyramidal neurons when connected in small microcircuits. Towards this goal we used morphologically simplified compartmental models of layer 5 neurons (both pyramidal and interneurons) implemented in the NEURON simulation environment. These neurons were fully interconnected in a small network, the properties of which are extensively based on anatomical and electrophysiological data. This microcircuit was used to characterize: a) the interplay of single cell ionic with synaptic currents for the emergence of sustained excitability (Papoutsi 2013) b) the role of dendritic non-linearities in the emergence of persistent firing (Papoutsi 2014) and c) the role of different types of interneurons in sustained activity porperties (Konstantoudaki 2014). Understanding the properties that make these neurons special in carrying temporal distinct information by using a bottom-up approach is a key issue in unraveling the complicated dynamics and flexibility of prefrontal neurons during behavioral tasks.