感覺皮層動力學特徵研究
作者:
小柯機器人發布時間:2020/8/11 18:53:36
英國劍橋大學Rodrigo Echeveste研究團隊近日取得一項新成果。他們檢測了針對基於採樣的概率推理進行優化的循環迴路中類皮質動力學。相關論文於2020年8月10日發表於《自然-神經科學》。
他們通過訓練視覺皮質超柱的循環興奮性-抑制性神經迴路模型來進行基於採樣的概率推斷,從而開發出針對這些現象的統一模型。經過優化的網絡顯示了幾個關鍵的生物學特性,包括分裂歸一化和刺激調製的噪聲可變性,刺激開始時抑制為主的瞬變以及強烈的伽馬振蕩。這些動力學特徵在加快推理速度和做出預測方面具有獨特的功能,他們在對清醒猴子的錄音進行再分析後證實了這一預測。
他們的結果表明,皮質動力學的基本圖案是有效執行相同計算功能的結果(基於快速採樣的推斷),並預測了這些圖案的進一步特性,可以在未來的實驗中進行測試。
據介紹,感覺皮層表現出一系列普遍存在的動力學特徵,例如持續的噪聲可變性、瞬態過衝和振蕩,到目前為止,這些動力學特徵已經擺脫了常見的、原則性的理論解釋。
附:英文原文
Title: Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference
Author: Rodrigo Echeveste, Laurence Aitchison, Guillaume Hennequin, Mt Lengyel
Issue&Volume: 2020-08-10
Abstract: Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory–inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function—fast sampling-based inference—and predict further properties of these motifs that can be tested in future experiments.
DOI: 10.1038/s41593-020-0671-1
Source: https://www.nature.com/articles/s41593-020-0671-1