皮层内部神经元作为对抗性歧视者的作用
Ari S Benjamin1, Konrad P Kording1
1Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
PLoS computational biology
|September 28, 2023
概括
大脑可能会使用对抗性学习来处理感官信息. 皮层内部神经元可以充当歧视者,在清醒状态和梦境状态之间切换可塑性规则,以实现学习.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 大脑通过经验来学习感官表现,但根本的算法尚未完全理解.
- 生成模型和对抗算法是感官学习的领先计算理论.
- 将这些理论与特定的大脑细胞类型联系起来,对于理解细胞机制至关重要.
研究的目的:
- 研究皮质内神经元在感官学习中的作用.
- 为了确定内部神经元是否可以在对抗性学习算法中充当歧视者.
- 探索这种系统的可塑性规则和计算特性.
主要方法:
- 提出了一个计算模型,其中皮层内部神经元在对抗性学习框架中充当歧视者.
- 内部神经元可塑性的特征在于在Hebbian (清醒) 和anti-Hebbian (梦想) 规则之间切换.
- 评估了模型在循环神经网络中的学习表征中的表现.
主要成果:
- 提出的具有内部神经元作为区分器的对抗算法在具有反复连接的网络中的学习表征方面表现出色.
- 该算法的可扩展性受到网络大小的限制.
- 唤起状态和生成样本之间的振荡活动可以部分缓解可扩展性问题.
结论:
- 皮层内部神经元可以通过在对抗算法中充当歧视者来调解感官学习.
- 这一策略涉及国家依赖的可塑性规则 (Hebbian/anti-Hebbian).
- 该模型为生物感官学习机制提出了一个可信和可测试的假设.
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