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.

Learning & Memory

- 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

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.

Relevant Publications

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.

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

Dendritic Computations in Connection with Learning and Memory.

We are mostly interested in understanding the type of neural computations performed by various classes of neurons in the brain, and in particular the ones that are involved in learning and memory. Towards this target, our work focuses on the development and analysis of neurally inspired theoretical and machine-learning algorithms used to model memory capacity in the brain. This work deals with the effects of dendritic morphology and the biophysics underlying learning induced by synaptic plasticity, on the computational (memory) capacity of pyramidal neurons and their possible role in the formation/retrieval of memories. Resulting findings indicate that such model neurons with stellate-like dendritic morphology and multiple side branches that contain voltage-dependent membrane mechanisms are capable of performing nonlinear computations. Furthermore, the memory capacity of such neurons can outstrip that of linear cells by more than an order of magnitude (see Poirazi and Mel, Neuron, 2001). This difference in the memory capacity, as measured on a classification task, is even more prominent in populations of such model neurons. Towards a more thorough analysis of the types of synaptic integration performed in these cells, we have developed of a very detailed biophysical model of a CA1 pyramidal neuron, which can be downloaded from SenseLab and our Software site. The CA1 simulator is used to address questions regarding the type of neural computations performed in these cells and their possible role in short versus long-term memory storage, as well as the underlying morphological and biophysical mechanisms involved. Results of this work show that integration of synaptic inputs impinging on the dendrites or the apical trunk of the model cell can be linear or supralinear depending on the type (single pulse or high frequency), strength and location of the synaptic stimuli (see Poirazi et al, Neuron, 2003a). Furthermore, the cell’s nonlinear arithmetic can be described by a very simple paper-and-pencil calculation: the cell’s mean firing rate can be predicted by a simple formula that happens to also describe a conventional 2-layer “neural network”. In the first layer, synaptic inputs drive several dozen separately thresholded sigmoidal subunits—physically corresponding to the long, thin terminal dendrites that make up the bulk of the cell’s receptive surface. In the second layer, subunit outputs are summed within the main trunk and cell body prior to final thresholding (see Poirazi et al, Neuron, 2003b). This work provides significant insight information about the type of computations performed by CA1 neurons under various stimulus conditions.

Relevant Publications

Poirazi, P. and Mel, B.W. Impact of Active Dendritic Processing and Structural Plasticity on Learning and Memory.Neuron, vol 29, pg. 779-796, March 2001.

Poirazi, P. Brannon, T. and Mel, B.W. Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell. Neuron, vol 37, pg. 977-987, March 2003.

Poirazi, P. Brannon, T. and Mel, B.W. Pyramidal Neuron as 2-Layer Neural Network. Neuron, vol 37, pg. 989-999, March 2003.

Poirazi, P. Brannon, T. and Mel, B.W. About the Model (online supplement) Neuron, vol 37, pg. 988, March 2003.