Abstract

The plasticity in the medial Prefrontal Cortex (mPFC) of rodents or lateral prefrontal cortex in non human primates (lPFC), plays a key role neural circuits involved in learning and memory. Several genes, like brain-derived neurotrophic factor (BDNF), cAMP response element binding (CREB), Synapsin I, Calcium/calmodulin-dependent protein kinase II (CamKII), activity-regulated cytoskeleton-associated protein (Arc), c-jun and c-fos have been related to plasticity processes. We analysed differential expression of related plasticity genes and immediate early genes in the mPFC of rats during learning an operant conditioning task. Incompletely and completely trained animals were studied because of the distinct events predicted by our computational model at different learning stages. During learning an operant conditioning task, we measured changes in the mRNA levels by Real-Time RT-PCR during learning; expression of these markers associated to plasticity was incremented while learning and such increments began to decline when the task was learned. The plasticity changes in the lPFC during learning predicted by the model matched up with those of the representative gene BDNF. Herein, we showed for the first time that plasticity in the mPFC in rats during learning of an operant conditioning is higher while learning than when the task is learned, using an integrative approach of a computational model and gene expression.

Highlights

  • Computational theories have been widely used in order to study the emergent properties of neural circuits [1,2]

  • In a first approach run in vivo, we showed that mRNA gene expression related to plasticity is differentially modified during the course of learning of an operant conditioning task

  • All genes studied are up-regulated in the medial Prefrontal Cortex (mPFC) of animals that belong to 50–65% of correct responses (50%CR)

Read more

Summary

Introduction

Computational theories have been widely used in order to study the emergent properties of neural circuits [1,2] In this sense, several models have been designed to describe the neuronal mechanisms underlying visual tasks, feeding behavior, reward prediction and operant conditioning, among others, integrating different brain areas [3,4,5,6]. Synaptic plasticity modifications are calculated as hebbian and anti-hebbian law, simulating long term potentiation (LTP) and long term depression (LTD), respectively This model is a behavioral and neurophysiological plausible neural network representation; it has not been yet confirmed by biological evidence. There are several genes associated with plasticity, among which the most important are brain derived neurotrophic factor (BDNF), cAMP Response Element Binding Protein (CREB), Synapsin I, Calcium/Calmodulin protein kinase II (CamKII), activity-regulated cytoskeletonassociated protein (Arc), c-fos and c-jun. The AP-1 subunits c-fos and c-jun are closely related to learning processes, plasticity and neuronal activation in rat cortex and hippocampus [24,25,26,27,28]

Objectives
Methods
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.