Temporal Coding in an Olfactory Circuit

嗅觉回路中的时间编码

• 批准号: 9511764
• 负责人: Gilles Laurent
• 金额: $ 24.96万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-01 至 1999-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9511764&HistoricalAwards=false
• 关键词: Temporal; Coding; Olfactory; Circuit

9511764 Laurent Although brains and computers are thought to be quite similar, the mechanisms which they use to encode and store information are fundamentally different. This is partly because computers were developed by engineers using tools of formal logic to solve rather limited sets of problems, while brains were developed over millions of years with selective pressure of evolution as the only engineer. Indeed, brains evolved to increase the chances of survival of their "owners", and thus primarily to facilitate the detection of prey, predators and mates. Consequently, they succeeded in solving the very complex problems of pattern recognition, problems which present computers are notoriously poor at handling. How do we recognize something as complex as a face or an odor, how do we store their infinite number of possible appearances, how does our brain represent them with neuronal activity patterns? To understand this, we must understand coding in the nervous system; we need to determine the rules that underlie stimulus representation in the brain. Conventional views of coding by the brain hold that information resides in the average number of impulses produced per unit time (rate coding); the role of relative timing of impulses produced by groups of neurons has not been carefully evaluated. The work of this young scientist focuses on circuits that process smells. Experiments are designed to determine whether the relative timing of neuronal impulses (temporal coding) is a potential carrier for information.

小行星9511764 虽然人们认为大脑和计算机非常相似，但它们用来编码和存储信息的机制却有着根本的不同。这部分是因为计算机是由工程师使用形式逻辑工具来解决相当有限的问题而开发的，而大脑是在数百万年的进化压力下作为唯一的工程师开发的。事实上，大脑的进化是为了增加“主人”的生存机会，因此主要是为了便于发现猎物、捕食者和配偶。因此，他们成功地解决了非常复杂的模式识别问题，而这些问题是目前计算机处理能力极差的。我们如何识别像面孔或气味这样复杂的东西，我们如何存储它们无限多的可能外观，我们的大脑如何用神经元活动模式来表示它们？为了理解这一点，我们必须了解神经系统中的编码;我们需要确定大脑中刺激表征的规则。 关于大脑编码的传统观点认为，信息存在于每单位时间产生的脉冲的平均数量（速率编码）中;神经元组产生的脉冲的相对定时的作用尚未得到仔细评估。这位年轻科学家的工作重点是处理气味的电路。 实验的目的是确定是否神经冲动的相对定时（时间编码）是一个潜在的信息载体。