Department of Bioengineering, Imperial College London, London, United Kingdom.
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States.
Elife. 2024 May 7;12:RP88053. doi: 10.7554/eLife.88053.
Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.
表象漂移是指尽管行为看似稳定，但大脑中的神经表象具有动态性。尽管已经在许多不同的脑区观察到漂移，但尚不清楚其背后的机制。由于内在神经兴奋性被认为在调节记忆分配中起着关键作用，兴奋性的波动可能会使先前存储的记忆集合重新激活产生偏差，并因此成为漂移的动力。在这里，我们提出了一个具有内在兴奋性缓慢波动的基于速率的可塑性递归神经网络。我们首先表明，神经网络集合的后续重新激活会导致该集合的漂移。该模型预测漂移是由先前活跃的神经元与兴奋性较高的神经元共同激活引起的，这会导致递归权重的重塑。与先前的实验工作一致，漂移的集合与其时间历史有关。至关重要的是，我们表明，从集合活动中解码时间信息需要漂移的渐进性质。最后，我们表明，记忆可以通过与主要区域具有可塑性突触的输出神经元来保存和解码。
