最优解码在突触粗调下的极限
Limits of optimal decoding under synaptic coarse-tuning
AI总结：
研究探讨了突触粗调对信息传递的影响，发现强粗调下最优解码器与朴素解码器性能相似，突触波动限制了有效读取至低维流形。
摘要：
感觉信息在大脑中通过连续的处理阶段传播，其中各站之间的突触权重模式决定了下游神经元如何从上游群体中解码信息。尽管优化的突触连接可以增强信息传递，但需要精确的权重调节。最近的证据表明突触波动相当显著，这提出了两个根本问题：粗调突触连接如何影响信息传递？神经系统可以采取什么策略来维持可靠的通信，尽管突触波动？我们通过分析二元刺激辨别下的信噪比（SNR）来回答这些问题，采用两种解码方案：朴素的群体平均和优化的线性解码器。对于朴素解码器，我们发现SNR对突触不精确性基本不敏感，因为性能已受神经元响应相关噪声的限制。对于最优解码器，我们识别出三个不同的区域。在弱粗调下，SNR²与群体大小N线性相关。在中等粗调下，相关性降低。在强粗调下，最符合观察到的神经元异质性，SNR饱和，无法通过招募更大的群体来改善。这种限制即使在引入前馈或循环网络架构时也保持不变。这些发现表明，在生物相关性强粗调区域，朴素和最优解码器可以实现定性相似的性能。分析显示，突触波动下的有效读取受限于一个不变的低维流形，与朴素解码器对齐，可能指向一种根本原则，以在持续的突触重塑中实现稳健的神经计算。
英文摘要：
Sensory information propagates through successive processing stages in the brain, where synaptic weight patterns between stations determine how downstream neurons decode information from upstream populations. Although optimized synaptic connectivity can enhance information transmission, it requires precise weight tuning. Recent evidence depicting substantial synaptic volatility raises two fundamental questions: How does coarse-tuning of synaptic connectivity affect information transmission? What strategies could the nervous system employ to maintain reliable communication despite synaptic fluctuations? We addressed these questions by analyzing the signal-to-noise ratio ($SNR$) for binary stimulus discrimination under two decoding schemes: a naive population average and an optimized linear decoder. For the naive decoder, we found that $SNR$ remains largely insensitive to synaptic imprecision, since performance is already limited by correlated noise in neuronal responses. For the optimal decoder, we identified three distinct regimes. Under weak coarse-tuning, $SNR^2$ scales linearly with population size $N$. Under moderate coarse-tuning, scaling becomes sublinear. Under strong coarse-tuning, the regime most consistent with observed neuronal heterogeneity, $SNR$ saturates and can not be improved by recruiting larger populations. This limitation persists even when incorporating feedforward or recurrent network architectures. These findings suggest that in the biologically relevant regime of strong coarse-tuning, naive and optimal decoders can achieve qualitatively similar performance. The analysis shows that effective readout under synaptic volatility is constrained to an invariant low-dimensional manifold aligned with the naive decoder, potentially pointing to a fundamental principle for robust neural computation in the face of ongoing synaptic remodeling.
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