Department of Computer Science, University of Tübingen, Tübingen, Germany.
Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
PLoS Comput Biol. 2024 Oct 31;20(10):e1012510. doi: 10.1371/journal.pcbi.1012510. eCollection 2024 Oct.
Cortical neurons are versatile and efficient coding units that develop strong preferences for specific stimulus characteristics. The sharpness of tuning and coding efficiency is hypothesized to be controlled by delicately balanced excitation and inhibition. These observations suggest a need for detailed co-tuning of excitatory and inhibitory populations. Theoretical studies have demonstrated that a combination of plasticity rules can lead to the emergence of excitation/inhibition (E/I) co-tuning in neurons driven by independent, low-noise signals. However, cortical signals are typically noisy and originate from highly recurrent networks, generating correlations in the inputs. This raises questions about the ability of plasticity mechanisms to self-organize co-tuned connectivity in neurons receiving noisy, correlated inputs. Here, we study the emergence of input selectivity and weight co-tuning in a neuron receiving input from a recurrent network via plastic feedforward connections. We demonstrate that while strong noise levels destroy the emergence of co-tuning in the readout neuron, introducing specific structures in the non-plastic pre-synaptic connectivity can re-establish it by generating a favourable correlation structure in the population activity. We further show that structured recurrent connectivity can impact the statistics in fully plastic recurrent networks, driving the formation of co-tuning in neurons that do not receive direct input from other areas. Our findings indicate that the network dynamics created by simple, biologically plausible structural connectivity patterns can enhance the ability of synaptic plasticity to learn input-output relationships in higher brain areas.
皮层神经元是多功能且高效的编码单元，它们对特定刺激特征表现出强烈的偏好。调谐的锐度和编码效率被假设是由精细平衡的兴奋和抑制来控制的。这些观察结果表明，需要对兴奋性和抑制性群体进行详细的共同调谐。理论研究表明，一组可塑性规则可以导致在由独立、低噪声信号驱动的神经元中出现兴奋/抑制（E/I）共同调谐。然而，皮质信号通常是嘈杂的，并且源自高度重复的网络，从而在输入中产生相关性。这就提出了一个问题，即可塑性机制是否有能力在接收嘈杂、相关输入的神经元中自我组织共同调谐的连接。在这里，我们研究了在一个通过可塑性前馈连接接收来自递归网络输入的神经元中，输入选择性和权重共同调谐的出现。我们证明，虽然强噪声水平会破坏读取神经元中共同调谐的出现，但通过在群体活动中产生有利的相关结构，可以在非可塑性前突触连接中引入特定结构来重新建立共同调谐。我们进一步表明，结构化的递归连接可以影响完全可塑性递归网络中的统计数据，从而驱动那些不从其他区域接收直接输入的神经元中共同调谐的形成。我们的研究结果表明，由简单、生物学上合理的结构连接模式产生的网络动态可以增强突触可塑性在大脑高级区域学习输入-输出关系的能力。
