Predictive feedback can account for biphasic responses in the lateral geniculate nucleus.
Predictive feedback can account for biphasic responses in the lateral geniculate nucleus.
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DOI：
10.1371/journal.pcbi.1000373
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发表时间：
2009-05
影响因子：
4.3
通讯作者：
Ballard DH
中科院分区：
文献类型：
--
作者：
Jehee JF;Ballard DH
双相神经反应特性，其中用于驱动神经反应的最佳刺激在短时间内从一种刺激模式改变为相反的刺激模式，已经在几个视觉区域中描述，包括外侧膝状体核（LGN）、初级视觉皮层（V1）和中颞区（MT）。我们描述了一个层次模型的预测编码和模拟，捕捉这些神经元响应特性的时间变化。我们专注于LGN-V1电路，发现在自然图像上训练后，该模型显示出大脑的LGN-V1连接结构，其中V1感受野的结构与LGN中中心-周围细胞的空间排列和属性有关。此外，LGN模型神经元的时空反应轮廓是双相结构，类似于猫LGN神经元的双相反应结构。此外，该模型显示了一个特定的模式的反馈的影响，其中LGN感受野对齐一个简单的细胞感受野区相同的极性减少他们的反应，而相反极性的神经元增加他们的反应与反馈。最近在神经生理学中观察到了这种阶段性逆转的影响模式。这些结果证实了预测反馈是大脑中的一般编码策略的想法。对于早期视觉脑中的许多神经元来说，驱动反应的最佳刺激在短时间内从一种刺激模式变为相反的刺激模式。例如，外侧膝状体核（LGN）中的许多神经元最初对明亮的刺激做出反应，但仅在20毫秒后就更喜欢暗刺激，并且在其他区域的神经元中也发现了类似的反应偏好变化。这些双相反应动力学的计算原因是什么？我们描述了一个层次模型的预测编码，解释这些响应特性。在该模型中，高级神经元试图预测其低级输入，而低级神经元则表示实际输入与高级预测之间的差异。在我们的模拟中，我们专注于LGN和V1区，并发现在自然图像上训练后，模型连接的布局类似于大脑的LGN-V1连接结构。此外，模型LGN神经元的反应在时间上是双相的，类似于神经生理学中发现的双相反应。此外，该模型显示了最近在神经生理学中观察到的来自高级区域的反馈的影响的特定模式。这些结果证实了预测反馈是大脑中的一般编码策略的想法。
Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain. For many neurons in the early visual brain the optimal stimulation for driving a response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time. For example, many neurons in the lateral geniculate nucleus (LGN) respond to a bright stimulus initially but prefer a dark stimulus only 20 milliseconds later in time, and similar changes in response preference have been found for neurons in other areas. What would be the computational reason for these biphasic response dynamics? We describe a hierarchical model of predictive coding that explains these response properties. In the model, higher-level neurons attempt to predict their lower-level input, while lower-level neurons signal the difference between actual input and the higher-level predictions. In our simulations we focus on the LGN and area V1 and find that after training on natural images the layout of model connections resembles the brain's LGN-V1 connectivity structure. In addition, the responses of model LGN neurons are biphasic in time, resembling biphasic responses as found in neurophysiology. Moreover, the model displays a specific pattern of influence of feedback from higher-level areas that was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.
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5.4
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