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Paper Information

Title: 

INTERNEURONS AND MEMORY MODELING IN HIPPOCAMPUS BY ARTIFICIAL NEURONAL NETWORK

Type: POSTER
Author(s): GANJKHANI M.*,MORADI K.,MORADZADEH SH.
 
 *DEPT. OF PHYSIOLOGY AND PHARMACOLOGY, ZANJAN UNIVERSITY OF MEDICAL SCIENCES
 
Name of Seminar: IRANIAN CONGRESS OF PHYSIOLOGY AND PHARMACOLOGY
Type of Seminar:  CONGRESS
Sponsor:  PHYSIOLOGY AND PHARMACOLOGY SOCIETY, MASHHAD UNIVERSITY OF MEDICAL SCIENCE
Date:  2007Volume 18
 
 
Abstract: 

Introduction: Behavior depends on the interaction of genes and environment. Learning and memory is the most important mechanism underlying environmental changes in human behavior. Indeed the important part of our personality is indebted to our memory. Language and many of routine behaviors are dependent to memory. Consequently, mechanism underlying memory systems is in the core of attention of physiologists, psychologists and even philosophers. Hippocampus is one of the important brain regions for memory formation. This region has different aspects to be concerned: this region has computational abilities; on the other hand it has the highest level in the brain hierarchy; and has a well known role in episodic memory and diseases like Alzheimer and Schizophrenia. However, there is not a universally accepted model, could explain the every aspects of the hippocampal function. Moreover, there is not a comprehensive model regarding to interneuron's function in hippocampal memory circuits.
Methods: In this study, using computational modeling approach, which is the only method could potentially explain the relation of brain and behavior, we tried to answer the role of interneurons in hippocampal memory.
Results: In this model we showed that: How interneurons control the activity of the network during encoding and decoding of memory in the hippocampus. How sometimes poor actions of interneurons leads to the dejavu phenomenon, which means feeling familiarity to events we did not experienced before. Also in this model, capacity of hippocampal memory systems has been computed.
Conclusion: our model is helpful in understanding the mechanisms underlying memory functions and explains the involvement of interneurons in this regard.

 
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