大腦的彈性決策能力中一個重要的特徵是能夠隨時的調整行為，比如說放慢決策速度以換取較高的決策正確率，或是當時間急迫時採取快速的決策行為。這樣的速度-準確率取捨行為可以藉由外在的指引、環境的影響或是自我意識而引發。而這個取捨行為發生的時間可以很快，極短於傳統的嘗試法（trial-and-error）學習所需的時間。過去學界致力於研究與獎賞相關（reward-dependent）的神經可塑性如何改善個體的決策行為，然而對於即時的決策行為調整背後的神經機制卻甚少研究。在本計畫第一年的研究中我們發現平衡性突觸輸入（balanced synaptic input,BSI）可以有效的調整一個決策神經網路模型的行為。本研究中我們近一步測試在BSI中詳細的平衡狀態（刺激與抑制的比例，E/I ratio）如何影響該模型的行為與神經活動。我們選了三個不同的平衡狀態來研究：1. 較多的刺激，2. 較多的抑制以及3.中間的狀態。我們發現在較多刺激的狀態時，增加BSI的強度會加快決策的速度但是增加錯誤率。這個效應隨著抑制輸入增加而減少，直到效應反轉。在較多的抑制狀態下，增加BSI強度卻會反過來降低錯誤率與增加決策時間。我們進一步用energy landscape來解釋為什麼不同E/I ratio會造成這麼大的不同。我們的研究建議E/I ratio可以被用來將BSI的效應在不同動態模式(dynamical regimes)中轉換。因此，藉由改變BSI的強度或是其E/I ratio，我們可以讓這個決策神經網路模型產生豐富的行為模式，這些模式在許多行為或是神經生理研究中都曾被觀察到過。

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