论文标题
基于电导的神经元网络中不规则活性的出现
Emergence of irregular activity in networks of strongly coupled conductance-based neurons
论文作者
论文摘要
皮质神经元的特征是发射不规则和速率广泛分布。平衡状态模型通过取消平均兴奋性和抑制性电流来解释这些观察结果,从而使波动驱动射击。在具有当前突触的神经元网络中,如果耦合强大,则平衡状态会动态出现,即,如果每个神经元$ k $的平均突触数量很大,并且突触功效为$ 1/\ sqrt {k} $。当突触基于电导率时,当耦合强大时,当前的波动会被抑制,从而质疑平衡状态思想对生物神经网络的适用性。我们分析了强烈耦合电导的神经元的网络,并表明如果突触为$ 1/\ log(k)$,则异步不规则活性和广泛的费率分布出现。在这样的网络中,与标准平衡状态模型不同,当前的波动很小,并且通过漂移扩散平衡来保持射击。如果输入小于临界值小于临界值,这取决于突触时间常数和耦合强度,并且对连接异质性比经典平衡状态模型更强大。我们的分析使对网络响应属性应如何发展随着输入的增加而进行实验测试的预测。
Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e. if the mean number of synapses per neuron $K$ is large and synaptic efficacy is of order $1/\sqrt{K}$. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synapses are of order $1/\log(K)$. In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.