2015 Fiscal Year Annual Research Report
不確実な環境における脳の記憶・推定のメカニズムに迫る
| Project/Area Number |
15J07963
|
|---|
| Research Institution | Kyoto University |
|---|
Principal Investigator |
李 玉哲 京都大学, 生命科学研究科, 特別研究員(DC2)
|
|---|
| Project Period (FY) |
2015-04-24 – 2017-03-31
|
| Keywords | Fear conditioning / Partial reinforcement / Mathematical model / Statistical inference / Shock procedure / Resistance to extinction |
|---|
| Outline of Annual Research Achievements |
Uncertainty of fear conditioning is crucial for acquisition and extinction of fear memory. Fear memory acquired through partial pairings of conditioned stimulus (CS) and unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although PREE has been explained by psychological theories, neural mechanisms underlying PREE remain largely unclear.
In this study, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. Our simulation revealed that unpredictability of no US during extinction was represented by the combined responses of the three types of neurons, being critical for the PREE.
In addition, we extended the model to include amygdala subregions and the medial prefrontal cortex (mPFC) to address a recent finding that the ventral mPFC (vmPFC) is required for the consolidation of extinction memory but not for memory retrieval.
Furthermore, model simulations lead us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neurons, in turn, to further inhibit the fear neurons during re-extinction.
|
| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Our research purposes are:
1. To clarify neural mechanism of how fear is over estimated as extinction resistant fear memory
2. To propose a novel therapy to diminish the extinction resistant fear memory
During the first year’s research, to examine how fear memory is learned in fear conditioning with partial reinforcement and is resistant to extinction, we have developed two neural circuit models: we first constructed a basic model of a neural circuit consisting of fear, persistent and extinction neurons; we then extended the basic model to include nuclei in the amygdala and vmPFC. The two models successfully reproduced the resistance to extinction of fear, and demonstrated consistent results of several other fear conditioning experiments. And the comparison with a statistical mode revealed that the responses of the neural circuit encoded uncertainty of the environment.
Based on simulation of the models, we also proposed a shock procedure to diminish extinction-resistant fear memory through additional fear conditioning with a stronger US.
Thus our current result shows we have accomplished our research plan for the first year. And we are submitting our paper about current results to PLoS ComPutational Biology (under review).
|
| Strategy for Future Research Activity |
Research plan:
1.Our current neural circuit model is a trial-by-trial response model. Next, we plan to modify this model to a time-dependent model, to capture more detailed behavior of neurons. And we hope using this new time-dependent model can explain more phenomena of fear learning and memory.
2.We have already implemented a statistical model in our current results, which successfully reproduced the resistance to extinction. But our current statistical model failed to reproduce the spontaneous recovery of fear, because it could not capture the probability changes during resting phases. To improve this point, we are going to introduce more parameters to our current model, such as ‘phase-change signals’, to make this model become more applicable to many other phenomena.
|
Research Products
(3 results)
