The Department of Computer Science at the University of Cyprus cordially invites you to the PhD Defense entitled:

Understanding the neural code through exploration of the causes of firing

Speaker: Mr. Achilleas Koutsou
Affiliation: University of Cyprus, Cyprus
Category: PhD Defense
Location: Room 148, Faculty of Pure and Applied Sciences (FST-01), 1 University Avenue, 2109 Nicosia, Cyprus (directions)
Date: Friday, June 26, 2015
Time: 17:00-18:00 EET
Host: Chris Christodoulou (

The neural code refers to the mechanisms with which single cells and networks of neurons transform information into sequences of spike trains. Discovering and understanding these mechanisms is fundamental to understanding information encoding, decoding and processing in the brain. A key aspect of solving the problem of the neural code is the ability to determine the operational mode of a single cell: whether it is a temporal integrator or a coincidence detector. More generally, this problem can be solved by identifying the temporal precision with which a neuron can distinguish between stimuli. This approach generalises the idea of a binary operational mode to a continuum which lies between the two extremes. In this work we propose a number of methods for addressing the problem of the neural code and more specifically the time scales of neural processing, which define the operational mode of neurons. Our first approach identifies the operational mode by determining the degree of synchrony that causes firing from observations of the subthreshold membrane potential slope prior to firing of a simple neuron model. We use this method to investigate the relationship between input synchrony and neural operational mode and the ways in which this relationship is shaped by neural and input parameters. In our second approach, we estimate the input parameters, and through them the input synchrony, of a stochastic model neuron driven by sinusoidal inputs, which represent periodic, synchronous fluctuations in pre-synaptic firing rates. Finally, we investigate ways in which a realistic biophysical neuron model is able to learn to distinguish input signals (modelled as delays between input spike pairs) at high temporal precision, on a scale of several milliseconds. As we will discuss, the results and our contributions do not actually provide a definitive answer to the question of which operational mode neurons employ. Instead, we arrive at general conclusions regarding the ways in which the operational mode is defined by various properties of the neuron and its behaviour.

Short Bio:
Achilleas Koutsou is a PhD candidate at the Department of Computer Science of the University of Cyprus under the supervision of Associate Professor Chris Christodoulou. He completed his undergraduate studies at the School of Computer Science of the University of Birmingham, UK in 2007 and his MSc studies in Intelligent Systems Engineering at the same department in 2008. His research interests lie in the field of Computational Neuroscience, focusing on neural coding and the role of synchrony in encoding and signal propagation. Over the last 6 years, he has worked on identifying ways to measure the temporal precision of neural coding in models of single neurons.

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