Computational Neuropharmacology

Dynamical Neuropharmacology: Development of a New Methodology

The concepts and methods of Systems Biology are being extended to neuropharmacology, to test and design drugs against neurological and psychiatric disorders. Computational modeling by integrating compartmental neural modeling technique and detailed kinetic description of pharmacological modulation of transmitter – receptor interaction is offered as a method to test the electrophysiological and behavioral effects of putative drugs.

TRENDS in Pharmacological Sciences

I. Aradi and P. Érdi: Computational neuropharmacology:dynamical approaches in drug discovery. Trends in Pharmacological Sciences 27(5) (2006) 240-243.

B. Ujfalussy, T. Kiss, G. Orbán, WE. Hoffmann, P. Érdi and M. HajósPharmacological and Computational Analysis of alpha-subunit Preferential GABAA Positive Allosteric Modulators on the Rat Septo-Hippocampal Activity. Neuropharmacology 52(3) 733-743 (2007)

Érdi P, Kiss T, Tóth J, Ujfalussy B and Zalányi L: From systems biology to dynamical neuropharmacology: Proposal for a new methodology. IEE Proceedings in Systems Biology 153(4) 299-308 (2006)

T. Kiss and P. Érdi: From electric patterns to drugs: perspectives of computational neuroscience in drug design. BioSystems 86(1-3) 46-52 (2006)

P. Érdi and J. Tóth: Towards a dynamic neuropharmacology: Integrating network and receptor levels. In: Brain, Vision and Artifical Intelligence.M. De Gregorio, V. Di Maio, M. Frucci and C. Musio (eds). Lecture Notes in Computer Science 3704, Springer, Berlin Helidelberg 2005, pp. 1-14. 

Computational Approach to the Schizophrenia

It has been hypothesized that some schizophrenic phenomena are best understood in terms of abnormal interactions between different brain regions. Preliminary data suggest that during associative learning task hippocampus is involved in the encoding (learning) and the prefrontal cortex in the retrieval of associative memories. Specific changes in the fMRI activities have also been observed based on comparative studies between stable schizophrenia patients and healthy control subjects. Disconnectivity, observed between brain regions in schizophrenic patients could result from abnormal modulation of N-methyl-D-aspartate (NMDA)-dependent plasticity implicated in schizophrenia.

Cooperation with Vaibhav A. Diwadkar

Our initial modeling efforts were directed toward a simple model to simulate the behavioral associative learning task, with model output as learning curves depicting performance over each iteration of recall. As will be evident, the model incorporates the separation between encoding/consolidation and cued recall while also retaining biological plausible relationships between model architecture and neural systems, as well as known learning parameters in the brain. In particular, the model accounts for (i) the separation between “where” and “what” regions (ii) reduced synaptic plasticity in schizophrenia and reduced cognitive capacity in schizophrenia.

A model has been built order to compare the (i) activities with the fMRI data; (ii) the performance with the behavioral data. 


P Érdi, B Ujfalussy, L Zalányi, VA Diwadkar: Computational approach to schizophrenia: Disconnection syndrome and dynamical pharmacology. In: A selection of papers of The BIOCOMP 2007 International Conference L. M. Ricciardi (ed.) Proceedings of the American Institute of Physics (accepted for publication) 

VA Diwadkar, B Flaugher, T Jones, L Zalányi, B Ujfalussy, MS Keshavan, P Érdi: Impaired associative learning in schizophrenia: Behavioral and computational studies. Cognitive Neurodynamics (accepted for publication) 

P Érdi, VA Diwadkar, B Ujfalussy: The schizophrenic brain: A broken hermeneutic circle. In Artificial Neural Networks – ICANN 2008 Volume Editors: V Kurkova, R Neruda, J Koutnik, Springer-Verlag (accepted for publication)