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 place cell formation
Studying the formation of place cells is an important step in understanding how representation of the external environment is coded in neural networks that constitute spatial maps. It is not currently known how place fields emerge in CA1 neurons. An influential model of place cell formation predicts the convergence of various grid field inputs which combine linearly to create the place field output of CA1 cells. In this study, we constructed a model of CA1 place cell formation through the convergence of grid field inputs to the distal dendrites of our model neuron. Firstly, a model of grid cell activity is created and represents the firing of grid cells. Then, different grid fields are used as synaptic inputs to stimulate the distal dendrites of the CA1 neuron, which is a biophysically constrained, detailed and compartmental neuron model. Apart from the excitatory, inhibitory synapses were placed in both the distal and proximal dendrites.
Compressed Sensing and Hippocampus
Compressed Sensing (CS) theory comprises a mathematical framework that describes how and under which conditions, restricted sampling of information can lead to condensed, yet concise, forms of the initial, sampled information entity. In this project, we aim to reveal evidence of the hypothesis that information flow and processing in the hippocampus complies with the principles of CS, i.e., hippocampus related regions and their respective circuitry are presented as a CS-based system whose different components collaborate to realize efficient memory encoding and decoding processes. This proposition introduces a unifying mathematical framework for hippocampal function and opens new avenues for exploring coding and decoding strategies in almost all of hippocampal regions.
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 (http://remod.gr) 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.
Bozelos P, Stefanou SS, Bouloukakis G, Melachrinos C, Poirazi P. “REMOD: A Tool for Analyzing and Remodeling the Dendritic Architecture of Neural Cells.”, Front Neuroanat. 2016 Jan 6;9:156. doi: 10.3389/fnana.2015.00156. eCollection 2015.
Pattern separation in Dentate Gyrus.
The aim of the project is to investigate the role of different mechanisms involved in pattern separation. Pattern separation is a computational task accomplished by hippocampal Dentate Gyrus (DG), and specifically by its principal neurons, the Granule Cells (GC). Towards this direction, we use a simplified, yet biologically relevant, computational model of DG. Using this model, we investigate the role of the indirect inhibitory circuitry formed by Mossy (MC) and Basket Cells and we examine its effect under MC loss condition. In addition, we examine the role of GC dendrites in the aforementioned task by alternating their morphological and biophysical characteristics.
Effect of Aging on Information Processing in the Hippocampus
Despite the body of research in neurobiological aging, the source and underlying mechanisms of aging induced alterations in neural learning and storage capacity remain elusive. Impaired memory in normal aging (or Alzheimer disease) could be attributed to several different changes in the structure and properties of neural tissue. We propose to complement experimental research via the introduction of in computo neural models. The model cells are formed via systematic expansions and refinements of a previously developed model of a normal CA1 pyramidal neuron. The form and kinetics of mathematical equations that model the various mechanisms are derived from biophysical studies regarding cellular properties of both young and old cells and are fine-tuned to match experimental findings. The aged model incorporates a number of known changes in membrane properties, including: (1) increased calcium influx mainly through L-type calcium channels, (2) larger and longer calcium spikes, (3) increased slow afterhyperpolarization current, (4) increased accommodation of spike trains etc. Responses of both models are compared to those of real neurons and parameters are adjusted accordingly to ensure that the models behavior is in agreement with as many experimental findings as possible.We use the model cells to examine the effects of aging on information/storage capacity of pyramidal neurons. In this project we are mostly interested in assessing whether -and in what ways- aging impairs compartmentalization and supralinear integration properties of dendritic subunits which could provide an explanation of reduced computational power in single neurons.
Temporal windows in the entorhinal–hippocampal loop
A recent experimental study (Mizuseki, Sirota, Pastalkova, & Buzsaki, 2009) has shown that the temporal delays between population activities in successive entorhinal and hippocampal anatomical stages are longer (about 70–80 ms) than expected from axon conduction velocities and passive synaptic integration of feed-forward excitatory inputs. In this project, we investigated the mechanisms that give rise to such long temporal delays in the hippocampus structures.
Stress and Learning Processes
Our major efforts in study are directed towards a degenerated states of CA1 and amygdala neurons, as this degeneration is imposed after the persistent exposure to the stress hormones which are secreted by the adrenal cortex after stressful events. In particular, based on a previous, experimentally derived computational model of a normal CA1 pyramidal cell, efforts have been made to embody the effects of the molecular parameters that appear altered with stress under the scope of electrophysiological modulations. We further expanded our investigation to amygdal pyramidal neurons. The purpose of the above investigation and implementation is driven from a deepest quest concerning the comparative juxtaposition of a computationally simulated, healthy CA1 and amygdala pyramidal neurons with the experimentally derived, chronically stressed analogous neuron models.