Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109;
Department of Physics, University of Michigan, Ann Arbor, MI 48109.
Proc Natl Acad Sci U S A. 2018 Mar 27;115(13):E3017-E3025. doi: 10.1073/pnas.1716933115. Epub 2018 Mar 15.
Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations' spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input current-dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further "learning" (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of subthreshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that resonance with subthreshold oscillations provides a plausible network-level mechanism to accurately encode and retrieve information without overstrengthening connections between neurons.
脑区之间和内部的网络振荡对于学习和记忆任务的表现至关重要。虽然大量的工作集中在神经振荡的产生上，但它们对神经元群体的放电活动和信息编码的影响知之甚少。在这里，我们使用计算建模来证明共振响应的转变可以与振荡输入相互作用，以确保神经元网络正确地编码新的信息，这些信息以外部输入的权重形式表示为递归突触连接。使用神经元网络模型，我们发现由于共振响应的输入电流依赖性变化，网络中的单个神经元将调整其放电相位，以表示各自输入的不同强度。随着网络对信息进行编码，神经元的同步性更强，这种效应限制了进一步“学习”（以突触强度变化的形式）的程度。我们还证明了神经元放电的顺序模式可以准确地存储在网络中；在没有外部输入的情况下（在亚阈值振荡的情况下），这些序列可以在正向和反向（在体内学习后观察到）方向上重现。为了测试类似的机制是否可以在体内发挥作用，我们表明海马神经元的周期性刺激以频率依赖的方式协调网络活动和功能连接。我们得出的结论是，与亚阈值振荡的共振为在不过度强化神经元之间连接的情况下准确地编码和检索信息提供了一种合理的网络级机制。
