Work in the Computational Biology Laboratory of IMBB-FORTH, which is headed by Dr. Panayiota Poirazi, has used computational models to show that a single neuronal cell in the brain is capable of detecting spatiotemporal differences of incoming signals and encoding them into its response pattern.

This work, which is published today in PLoS Computational Biology, is particularly important as it predicts that a single neuron has the capacity to detect the spatial and temporal characteristics of memory-related signals, encode these characteristics into its response using a temporal code and transmit this information to higher brain regions. So far, such properties have been attributed only to large neuronal populations. The new work reveals for the first time the computational processing capability of individual cells in the brain, the extent of which had not been previously appreciated.

It is possible that this temporal switch mechanism serves as a familiarity detector: known environmental stimuli, such as previously seen objects, are quickly recognized by the neuron leading to an enhanced response, thus indicating the recognition of a previously established memory. On the contrary, novel environmental stimuli are associated with long delays (which may result from the search process), leading to a suppression of the neuronal output, thus allowing some other region to process and store the new memory. This model prediction is in agreement with the existing literature and awaits experimental verification.

An important finding of this work concerns the way such information is coded in the brain. According to the computational model, individual CA1 pyramidal neurons utilize a temporal code comprising of the time-intervals prior to the onset of the response and between spikes within bursts to transmit information about the temporal association (extent of familiarity) as well as the location of incoming signals within their dendritic tree.

Overall, this work contributes significantly to our understanding of the mechanisms underlying memory formation in the hippocampus. In addition, this helps us to understand how memory deficits appear in normal aging and in neurodegenerative conditions, such as Alzhemeir’s and Parkinson’s disease. Furthermore, the findings of this work may be applied in neuromorphic engineering and neuroprosthetics for the development of intelligent artificial systems that mimic brain microanatomy.

For more information contact:
Dr. Panayiota Poirazi
Computational Biology Laboratory
Institute of MolecularBiology and Biotechnology, FORTH
Τηλ.: 2810 391139, e-mail: poirazi@imbb.forth.gr
http://www.imbb.forth.gr/people/poirazi/index.html

Relevant Publication:
Pissadaki E.K., Sidiropoulou K., Reczko M., and Poirazi P. Encoding of Spatio-temporal Input Characteristics by a CA1 Pyramidal Neuron Model, PLoS Computational Biology, December, 2010 (http://www.ploscompbiol.org/doi/pcbi.1001038).