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<p>Even without external stimuli, neurons produce spontaneous bursts of activities. Theoretical and practical clinical considerations, suggest the importance of determining the in-vivo statistical profile of those spontaneous spikes bursts, however this task has not been accomplished yet. Currently, it is only accepted that the in-vivo value of the mean firing rate (<i>λ</i>) of those spontaneous bursts is below 0.1Hz, without knowing its specific value and its population distribution. Here we propose a framework to evaluate the neurons’ <i>λ</i> during rest of a given subject, using stochastic signal processing analysis of in-vivo brain fMRI and EEG. Our main hypothesis is that during rest the input to the neurons is mostly formed by a random neuronal noise, and although it fluctuates with zero mean, it affects the neurons’ signal output characteristics. Our results based on in-vivo human fMRI and EEG databases, suggest that different people have different and stable characteristic <i>λ</i> values, and that <i>λ</i> of different functional systems of the same subject correlate in their values. Moreover, we find here that the <i>λ</i> values of subjects correlate with their brain task performances, in particular for tasks which are known to be affected by changes in neuronal noise or neuronal excitability threshold.</p>

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Evaluating in-vivo spontaneous firing rate in the brain based on neuronal noise modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月26日

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  • Hila Dvir,
  • Shu Guo,
  • Rui Kang,
  • Daqing Li,
  • Shlomo Havlin

摘要

Even without external stimuli, neurons produce spontaneous bursts of activities. Theoretical and practical clinical considerations, suggest the importance of determining the in-vivo statistical profile of those spontaneous spikes bursts, however this task has not been accomplished yet. Currently, it is only accepted that the in-vivo value of the mean firing rate (λ) of those spontaneous bursts is below 0.1Hz, without knowing its specific value and its population distribution. Here we propose a framework to evaluate the neurons’ λ during rest of a given subject, using stochastic signal processing analysis of in-vivo brain fMRI and EEG. Our main hypothesis is that during rest the input to the neurons is mostly formed by a random neuronal noise, and although it fluctuates with zero mean, it affects the neurons’ signal output characteristics. Our results based on in-vivo human fMRI and EEG databases, suggest that different people have different and stable characteristic λ values, and that λ of different functional systems of the same subject correlate in their values. Moreover, we find here that the λ values of subjects correlate with their brain task performances, in particular for tasks which are known to be affected by changes in neuronal noise or neuronal excitability threshold.