Universidade de São Paulo, Instituto de Física de São Carlos, São Carlos São Paulo, Brazil.
Front Neuroinform. 2009 Jul 20;3:24. doi: 10.3389/neuro.11.024.2009. eCollection 2009.
This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.
本工作提出了一种数字信号处理方法，用于表示和建模皮质网络中信号的传输和组合。信号动力学用扩散来建模，这使得任意两个节点之间的信息处理都可以用有限脉冲响应（FIR）滤波器来完全描述。研究了无时间衰减和有时间衰减的扩散。发现猫和猕猴皮质组织所基于的所有滤波器都是低通性质的，使得皮质信号处理可以用各自的截止频率来概括（高截止频率意味着信号在混合过程中几乎没有改变）。报告并讨论了几个发现，包括时间活动衰减的加入往往会提供更多样化的截止频率这一事实。在这些网络中，每个社区的过滤强度都不同。此外，参与物体识别的大脑区域在猫和猕猴网络中都表现出最高的截止频率。
