Synaptic weights that correlate with presynaptic selectivity increase decoding performance.
Synaptic weights that correlate with presynaptic selectivity increase decoding performance.
复制标题
DOI：
10.1371/journal.pcbi.1011362
复制
发表时间：
2023-08
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
视觉皮层神经元的活动通常以调谐曲线为特征，这些曲线被认为是在发育和感觉体验期间由赫布可塑性形成的。这导致预测，神经回路应该被组织成使得具有相似功能偏好的神经元与更强的权重连接。为了支持这一观点，以前的实验和理论工作提供了证据的视觉皮层模型的特点是这样的功能子网络。然而，最近的一项实验研究发现，突触后偏好刺激是由给定刺激激活的棘的总数定义的，并且与它们的个体强度无关。虽然这一结果似乎与以前的文献相矛盾，但有许多因素可以定义给定的突触输入如何影响突触后选择性。在这里，我们设计了一个计算模型，其中突触后功能偏好是由给定刺激激活的输入数量定义的。使用可塑性规则，突触权重往往与突触前的选择性，是独立的前和突触后的活动之间的功能相似性，我们发现，该模型可以用来解码的方式，是可比的最大似然推理的刺激。大脑是由复杂的网络组成的，神经元之间的通讯是沿着突触连接进行的。这些连接可以改变和适应，我们称之为“可塑性”。然而，决定这些变化的具体规则在很大程度上仍然未知。在我们负责视觉和感知的视觉系统中，人们主要认为表现出类似活动的神经元之间的连接会变得更强，也被称为“赫布可塑性”。最近的一项研究揭示了似乎与这一观点相矛盾的结果，显示了突触数量的优势而不是强度。基于这些发现，我们开发了一个计算模型，并假设这些变化是如何发生的。我们发现，这种新模型具有基于突触前活动的可塑性机制，可以捕获实验结果并导致群体解码的益处。我们的模型并不一定与赫布模型相矛盾，而是可能与它共存。
The activity of neurons in the visual cortex is often characterized by tuning curves, which are thought to be shaped by Hebbian plasticity during development and sensory experience. This leads to the prediction that neural circuits should be organized such that neurons with similar functional preference are connected with stronger weights. In support of this idea, previous experimental and theoretical work have provided evidence for a model of the visual cortex characterized by such functional subnetworks. A recent experimental study, however, have found that the postsynaptic preferred stimulus was defined by the total number of spines activated by a given stimulus and independent of their individual strength. While this result might seem to contradict previous literature, there are many factors that define how a given synaptic input influences postsynaptic selectivity. Here, we designed a computational model in which postsynaptic functional preference is defined by the number of inputs activated by a given stimulus. Using a plasticity rule where synaptic weights tend to correlate with presynaptic selectivity, and is independent of functional-similarity between pre- and postsynaptic activity, we find that this model can be used to decode presented stimuli in a manner that is comparable to maximum likelihood inference. Brains are composed of complex networks, with communication taking place along synaptic connections between neurons. These connections can change and adapt, a process we call “plasticity”. However, the specific rules that dictate these changes remain largely unknown. In our visual system, which is responsible for vision and perception, it is primarily thought that connections get stronger between neurons exhibiting similar activity, also known as ‘Hebbian plasticity’. A recent study revealed results that seemed to contradict this idea, showing a strength in numbers of synapses rather than strength. Prompted by these findings, we developed a computational model with a hypothesis about how these changes could occur. We discovered that this new model, with a plasticity mechanism based on presynaptic activity, could capture experimental findings and lead to benefits in population decoding. Our model doesn’t necessarily contradict a Hebbian model, but rather, likely co-exists with it.
登录
查看更多内容
影响因子：
2.9
作者：
KATSUKI, H;KANEKO, S;SATOH, M
通讯作者：
SATOH, M
影响因子：
16.2
作者：
Gidon, Albert;Segev, Idan
通讯作者：
Segev, Idan
影响因子：
4.6
作者：
Gallinaro, Julia V.;Rotter, Stefan
通讯作者：
Rotter, Stefan
影响因子：
25
作者：
Clopath, Claudia;Buesing, Lars;Gerstner, Wulfram
通讯作者：
Gerstner, Wulfram
影响因子：
5.7
作者：
Driscoll, Laura N.;Duncker, Lea;Harvey, Christopher D.
通讯作者：
Harvey, Christopher D.