神經決策理論( decision-making theory ) 運用資訊競爭來進行決策，使用兩群( 或兩群以上 )神經元建構出決策系統( decision-making model )，當外在的刺激透過感知系統形成神經衝動進入決策系統，決策系統的資訊即開始累積，使得兩群神經群的神經衝動強度( firing rate )逐步增加，因兩群神經元會互相抑制，故當雙方的神經衝動強度(firing rate)達一定水準時，會出現一組勝出而另一組神經衝動強度快速下降的情況，此時即稱為作出決策( decision )；近幾年更有學者透過模型的簡化，以動態系統理論分析，找到迴路在何種刺激強度下有較好的決策結果，這讓我們瞭解到神經系統本身存在一些狀態，透過調控神經元之間的連結強度，可以得到完美的工作區間，在許多生物相關的研究中，更進一步討論調控的機制，使得神經決策理論在神經科學領域占有一席之地。
但在實驗控制下的刺激，展現出的最佳決策狀態，在變動環境中依然成立嗎？變動的環境是否會引導出其他在非變動環境中未曾見到的重要性質？思考以上的問題，我們猜測或許每一種決策系統的參數，其展現的行為，都曾是生物演化過程中，為求生存的選擇之一，若我們只專注在經過高度演化後的現況，是難以窺探出其中簡約的模型，於是乎我們反向思考，在演化之初，簡單的神經系統會面臨怎樣的難題？透過選擇會迫使系統改進什麼部份？由簡單結構累積至複雜結構的過程，或許可以讓我們重新過濾高度演化的現況，已知的哪些理論是比較符合的。
因此我們建構模擬真實物理狀態的環境，讓簡單的神經系統(我們稱為虛擬蟲virtual worm)與物理環境互動，透過分析神經迴路的最佳化狀態，並比對虛擬蟲的行為，說明個體行為與神經迴路的關聯性，並確認可能的優化方向，在實驗的過程中，我們發現在神經決策理論中指出的性質，並相當意外地發現到三點：一、不同的刺激條件導致不同的狀態，初始的刺激條件，對虛擬蟲有決定性的影響；二、簡單的系統中，亦存在工作記憶(working memory)，可進一步優化個體的行為；三、若環境刺激太強，並不利於決策的進行，感知系統的靈敏程度亦扮演重要的角色。

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