Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks.
Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks.
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DOI：
10.1073/pnas.1716933115
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发表时间：
2018-03-27
影响因子：
11.1
通讯作者：
Zochowski MR
中科院分区：
文献类型：
--
作者：
Roach JP;Pidde A;Katz E;Wu J;Ognjanovski N;Aton SJ;Zochowski MR
神经元网络需要可靠地编码和重放活动模式和序列。在大脑中，空间编码神经元序列按照最近经验中的顺序在时间上向前和向后方向重放。到目前为止，还没有已知的网络级或生物物理机制可以在同一网络内产生两种重放模式。在这里，我们提出，共振（神经元的一种属性）与神经输入中的阈下振荡相结合，有助于固定和顺序活动模式的网络级学习，并导致正向和反向重放。大脑区域之间和内部的网络振荡对于学习和记忆任务的执行至关重要。虽然大量工作集中在神经振荡的产生上，但它们对神经元群的尖峰活动和信息编码的影响却鲜为人知。在这里，我们使用计算模型来证明共振响应的变化可以与振荡输入相互作用，以确保神经元网络将外部输入中表示的新信息正确编码为循环突触连接的权重。使用神经元网络模型，我们发现由于共振响应中输入电流相关的变化，网络中的各个神经元将安排其放电阶段以代表其各自输入的不同强度。随着网络对信息进行编码，神经元更加同步地放电，这种效应限制了进一步“学习”（以突触强度变化的形式）发生的程度。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；这些序列随后在没有外部输入（在阈下振荡的情况下）的情况下在正向和反向方向上再现（正如在体内学习后观察到的那样）。为了测试类似的机制是否可以在体内发挥作用，我们证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，阈下振荡的共振提供了一种合理的网络级机制，可以准确地编码和检索信息，而不会过度加强神经元之间的连接。
Networks of neurons need to reliably encode and replay patterns and sequences of activity. In the brain, sequences of spatially coding neurons are replayed in both the forward and reverse direction in time with respect to their order in recent experience. As of yet there is no network-level or biophysical mechanism known that can produce both modes of replay within the same network. Here we propose that resonance, a property of neurons, paired with subthreshold oscillations in neural input facilitate network-level learning of fixed and sequential activity patterns and lead to both forward and reverse replay. 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.
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DOI：
10.1098/rstb.2012.0532
发表时间：
2014-02-05
期刊：
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
影响因子：
--
作者：
Jeewajee A;Barry C;Douchamps V;Manson D;Lever C;Burgess N
通讯作者：
Burgess N
细胞外田地和电流的起源-EEG，ECOG，LFP和尖峰。
DOI：
10.1038/nrn3241
发表时间：
2012-05-18
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Buzsáki G;Anastassiou CA;Koch C
通讯作者：
Koch C
DOI：
10.1073/pnas.86.5.1698
发表时间：
1989-03-01
影响因子：
11.1
作者：
GRAY, CM;SINGER, W
通讯作者：
SINGER, W
影响因子：
64.8
作者：
LESTER, RAJ;CLEMENTS, JD;JAHR, CE
通讯作者：
JAHR, CE
影响因子：
2.5
作者：
Hutcheon, B;Miura, RM;Puil, E
通讯作者：
Puil, E