摘要
在神經調控機制中，Balance Synaptic Input ( BSI ) 是近年熱門的概念，根據不同的BSI 強度以及抑制型訊號與刺激型訊號的比
例 (Inhibition / Excitatory ratio, I/E ratio)，可以調控神經網路的決策行為 (performance and reaction time )並產生speed-accuracy trade off ( SAT )現象。我們認為來自Prefrontal Cortex ( PFC )的訊號有著BSI 的特性並依此建立一個由PFC 注入Endogenous Balance Synaptic Input ( EBSI) 調控decision making 的model。

在適當的ΔI / ΔE 與EBSI 強度調控下，發現在EBSI model 中
也有SAT 的現象，且在stimulus 打開後ΔI / ΔE 與energy landscape 會隨著時間改變。而energy landscape 的變化與
performance and reaction time 有著高度正相關，藉此可理解
energy landscape 如何隨時間調控神經網路行為。

我們的model 成功建立一個無需外加訊號的EBSI 調控方式，
並使一個高度非線性的神經網路系統的dynamics 在適當條件下具
備簡單近似線性的關係，有助於我們解釋相當複雜的大腦皮質神經
網路特性。

Abstract
In each kind of nervous control theory, Balance Synaptic
Input (BSI) is a popular concept in recent years. Depending on the BSI strength and Inhibition/Excitatory ratio (I / E ratio), we can control the behavior of decision-making task (performance and reaction time) and produce a speed-accuracy trade off ( SAT ) phenomenon.

We believe that the signal from the Prefrontal Cortex (PFC) has the character of BSI and according to make the model control decision making by an Endogenous Balance Synaptic Input (EBSI) from PFC. By changing I/E ratio and EBSI strength we also found SAT phenomenon in EBSI model.

The energy landscape changes with the performance and
reaction time has a high positive correlation. By this, we
understand how the energy landscape to regulate the neural
network behavior with time.

Our model successfully established a need for no any
external signal EBSI control methods. And let a dynamics of
highly nonlinear neural network system under appropriate
conditions possess a simple approximate linear relationship. The EBSI model help us to explain the complex features of cortical neural networks.

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