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Neural coding and computation using noisy oscillations.

机译：使用噪声振荡的神经编码和计算。

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摘要

Rhythmic local field potential (LFP) oscillations are a commonly observed phenomenon in the brains of diverse animal species from mollusks to humans. This activity is thought to reflect periodic and synchronized action potential firing by a population of neurons in the vicinity of a recording electrode. In awake animals, LFP oscillations are noisy, exhibiting fluctuations in amplitude and frequency, and can synchronize across multiple recording sites in distant brain regions. The power and spatial coherence of LFP oscillations have been associated with many aspects of brain function, including sensation, attention and working memory. However, the role of spatially coherent noisy oscillations in neural coding and computation remains poorly understood. Are there specific computational advantages to coordinating noise in the brain? We address this problem here through studies of oscillation-induced synchronization of neurons in acute brain slices, the awake mouse olfactory bulb and a numerically simulated network model.;Using patch clamp recordings from cortical pyramidal neurons in vitro, we show that noisy oscillatory synaptic input encodes information about a neuron's firing rate in the precise timing of its action potentials. Because of this encoding, two neurons that receive the same noisy oscillatory input fire synchronously when both are driven at the same firing rate, but desynchronize at different firing rates. We show that this rate-specific synchrony (RSS) paradigm can support many-are-equal pattern recognition computation in an in vitro network model, and that RSS is robust to non-ideal cell and stimulus conditions, as expected in vivo. Based on this work, we propose that RSS occurs in the awake brain in the presence of spatially coherent noisy oscillations.;We test the RSS hypothesis in vivo by analyzing multi-electrode recordings of LFP oscillations and mitral cell action potentials from the awake mouse olfactory bulb (OB) during passive odor exposure. We use spike-field coherence (SFC) estimation to show that single mitral cells become entrained to odor-evoked beta-band (11-29 Hz) LFP oscillations in a graded, odor-dependent manner. When two mitral cells exhibit high SFC during the same odor trial, they also show an increased rate of synchronous action potentials. We confirm that this synchrony is firing rate-specific, and that the strength of RSS depends on SFC magnitude at the time scale of 1-2 beta oscillation cycles.;Finally, we demonstrate the feasibility of synchrony-based odor recognition in the mouse OB using a numerically simulated network model in which the noisy oscillation properties and pairwise RSS statistics are matched to those observed in vivo. We also use systematic parameter variation to show that the strength and time-evolution of RSS can be optimized in the brain by "tuning" properties of the noisy stimulus and neuron population. We conclude this work by proposing experimental strategies to directly explore the role of RSS in stimulus encoding and pattern recognition computation in behaving animals.
机译：在软体动物到人类的各种动物的大脑中，有节奏的局部场电位（LFP）振荡是一种常见现象。人们认为这种活动反映了记录电极附近神经元的周期性和同步动作电位放电。在清醒的动物中，LFP振荡嘈杂，振幅和频率会出现波动，并且可以在遥远的大脑区域中的多个记录位置进行同步。 LFP振荡的力量和空间连贯性已与脑功能的许多方面相关联，包括感觉，注意力和工作记忆。但是，在神经编码和计算中空间相干噪声振荡的作用仍然知之甚少。协调大脑中的噪声是否有特定的计算优势？我们通过研究急性脑切片中神经元的振荡诱导的同步，清醒的小鼠嗅球和数值模拟的网络模型来解决此问题;使用体外皮质锥体神经元的膜片钳记录，我们证明了嘈杂的振荡突触输入在神经元的动作电位的精确时间编码有关神经元放电速率的信息。由于这种编码，当两个神经元以相同的发射速率被驱动时，接收到相同噪声振荡输入的两个神经元却同步发射，但是以不同的发射速率去同步。我们表明，这种速率特定的同步（RSS）范式可以在体外网络模型中支持许多相等的模式识别计算，并且RSS对非理想的细胞和刺激条件具​​有鲁棒性，如体内预期的那样。基于这项工作，我们建议在空间相干噪声振荡存在的情况下，RSS会在清醒的大脑中发生。我们通过分析LFP振荡的多电极记录和来自清醒小鼠嗅觉的二尖瓣细胞动作电位，在体内测试RSS假设。被动气味暴露过程中的灯泡（OB）。我们使用尖峰场相干性（SFC）估计来显示单个二尖瓣细胞以分级，依赖于气味的方式被带到气味诱发的β带（11-29 Hz）LFP振荡。当两个二尖瓣细胞在相同的气味试验中表现出较高的SFC时，它们的同步动作电位也会增加。我们确认这种同步性是特定于发射速率的，并且RSS的强度取决于1-2个beta振荡周期的时间尺度上SFC的大小。;最后，我们证明了在小鼠OB中基于同步性气味识别的可行性使用数值模拟的网络模型，其中噪声振荡特性和成对的RSS统计与体内观察到的相匹配。我们还使用系统的参数变化来显示RSS，可以通过“调整”嘈杂刺激和神经元种群的属性来优化RSS的强度和时间演变。我们通过提出实验策略来直接探索RSS在行为动物的刺激编码和模式识别计算中的作用来结束这项工作。

著录项

  • 作者

    Markowitz, David Aaron.;

  • 作者单位

    Princeton University.;

  • 授予单位 Princeton University.;
  • 学科 Biology Neuroscience.;Biophysics General.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 115 p.
  • 总页数 115
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:30

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