Projects

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.

Amygdala

- Ongoing

Computational modeling of the formation of memory traces in amygdala

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.

Relevant Publications

Papoutsi A., Kastellakis G., Psarrou M., Anastasakis S., Poirazi P. Coding and Decoding with Dendrites, Journal of Physiology-Paris (2013).

 

Anastasakis, S, Kastellakis G and Poirazi, P. Computational modeling of fear memory allocation in amygdalar neuronal populations”, HSCBB 2012, Heraklion, Crete, 2012

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.

Relevant Publications

Kastellakis G. and Poirazi P.
Cellular and dendritic memory allocation
The Computing Dendrite, Springer Series in Computational Neuroscience, Ed. Torben-Nielsen, B. Remme, M., Cuntz, H., Volume 11, 2014, pp 415-432 (2013)

 

Papoutsi A., Sidiropoulou K. and Poirazi P.
Memory Beyond Synaptic Plasticity: The Role of Intrinsic Neuronal Excitability.
MEMORY MECHANISMS IN HEALTH AND DISEASE by World Scientific Publishing Co. pg. 53-80, (2012)

 

Synaptic clustering within dendrites: An emerging theory of memory formation.
Kastellakis G, Cai DJ, Mednick SC, Silva AJ, Poirazi P.
Prog Neurobiol. 2015 Mar;126:19-35. doi: 10.1016/j.pneurobio.2014.12.002. Epub 2015 Jan 8.

- Completed

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.