University of Calgary, Calgary, Canada.
Department of Cell Biology and Anatomy, Calgary, Canada.
Sci Rep. 2024 May 8;14(1):10536. doi: 10.1038/s41598-024-58524-7.
Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli, memories, or behaviours across trials. When these spikes occur as a repeating sequence, they can be used to encode and decode a potential time series. Here, we show both analytically and in simulations that the error incurred in approximating a time series with precisely timed and reliably emitted spikes decreases linearly with the number of neurons or spikes used in the decoding. This was verified numerically with synthetically generated patterns of spikes. Further, we found that if spikes were imprecise in their timing, or unreliable in their emission, the error incurred in decoding with these spikes would be sub-linear. However, if the spike precision or spike reliability increased with network size, the error incurred in decoding a time-series with sequences of spikes would maintain a linear decrease with network size. The spike precision had to increase linearly with network size, while the probability of spike failure had to decrease with the square-root of the network size. Finally, we identified a candidate circuit to test this scaling relationship: the repeating sequences of spikes with sub-millisecond precision in area HVC (proper name) of the zebra finch. This scaling relationship can be tested using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis.
精确时间和可靠发射的尖峰被假设为具有多种功能，包括提高跨试验刺激、记忆或行为的编码准确性和可重复性。当这些尖峰以重复序列的形式出现时，它们可以用于编码和解码潜在的时间序列。在这里，我们通过分析和模拟表明，用精确时间和可靠发射的尖峰来近似时间序列所产生的误差与解码中使用的神经元或尖峰数量呈线性关系。这在合成尖峰模式的数值上得到了验证。此外，我们发现，如果尖峰的时间精度不高或发射不可靠，则用这些尖峰进行解码会导致误差呈亚线性。然而，如果尖峰的精度或可靠性随着网络规模的增加而增加，那么用序列尖峰解码时间序列的误差将保持与网络规模的线性下降。尖峰精度必须随网络规模线性增加，而尖峰失败的概率必须随网络规模的平方根减小。最后，我们确定了一个候选电路来测试这种缩放关系：在斑胸草雀的 HVC（专有名称）区域中具有亚毫秒精度的重复尖峰序列。这种缩放关系可以使用神经数据和基于歌声频谱的记录来测试，同时利用神经发生导致的 HVC 网络规模的自然波动。
