Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit.
Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit.
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DOI：
10.1371/journal.pcbi.1004315
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发表时间：
2015-08
影响因子：
4.3
通讯作者：
Chklovskii DB
中科院分区：
文献类型：
--
作者：
Bharioke A;Chklovskii DB
尽管只有有限的动态范围，但神经元必须忠实地对可能变化多个数量级的信号进行编码。对于一个相关信号，这种动态范围约束可以通过减去过去可以预测的信号分量来缓解，这种策略被称为预测编码，依赖于学习输入统计数据。然而，输入自然信号的统计也可能在很短的时间尺度上发生变化，例如，在视觉场景中跟随扫视。为了降低具有快速变化统计量的信号的传输成本，实现预测编码的神经元电路也必须快速适应其特性。在实验中，在不同的感觉模式下，感觉神经元在输入变化的100毫秒内表现出这种适应。在这里，我们首先展示了在反馈抑制回路中连接的线性神经元可以实现预测编码。然后，我们表明，在这样的反馈抑制电路中添加整流非线性，使其能够在大范围的输入中自动适应和近似最佳线性预测编码网络的性能，同时保持其潜在的时间和突触特性不变。我们证明，这种非线性网络的线性化时间滤波器的结果变化与实验中观察到的不同感觉模式的快速适应相匹配，在不同的脊椎动物物种中。因此，非线性反馈抑制网络可以自动适应快速变化的信号，保持自然输入的准确神经元传递所需的动态范围。探索自然景观的动物接收到的感官输入变化迅速，超过许多数量级。尽管神经元的动态范围有限，适应时间也相对较慢，但它们必须忠实地传递这些输入。通过有限动态范围信道传输信号的一种被广泛接受的策略是预测编码，它只传输过去无法预测的信号成分。预测编码算法对意外输入的响应最大，使其在描述感官传递方面具有吸引力。然而，最近的实验证据表明，神经元回路适应迅速，在快速输入变化后做出最佳反应。在这里，我们通过在预测编码电路中引入固定的非线性来调和预测编码算法与这种自动适应。由此产生的网络自动“适应”其线性化响应以适应不同的输入。事实上，它近似于实现预测编码的最佳线性电路的性能，而无需改变其内部参数。此外，将这种非线性添加到预测编码电路中仍然允许输入被无损压缩，从而允许额外的下游操作。最后，我们证明了非线性电路动力学匹配听觉和视觉神经元的响应。因此，我们认为这种非线性电路可能是一种通用的电路基元，可以应用于不同的神经电路中，只要有必要提供传输信号质量的自动改进，以实现快速变化的输入分布。
Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs. An animal exploring a natural scene receives sensory inputs that vary, rapidly, over many orders of magnitude. Neurons must transmit these inputs faithfully despite both their limited dynamic range and relatively slow adaptation time scales. One well-accepted strategy for transmitting signals through limited dynamic range channels–predictive coding–transmits only components of the signal that cannot be predicted from the past. Predictive coding algorithms respond maximally to unexpected inputs, making them appealing in describing sensory transmission. However, recent experimental evidence has shown that neuronal circuits adapt quickly, to respond optimally following rapid input changes. Here, we reconcile the predictive coding algorithm with this automatic adaptation, by introducing a fixed nonlinearity into a predictive coding circuit. The resulting network automatically “adapts” its linearized response to different inputs. Indeed, it approximates the performance of an optimal linear circuit implementing predictive coding, without having to vary its internal parameters. Further, adding this nonlinearity to the predictive coding circuit still allows the input to be compressed losslessly, allowing for additional downstream manipulations. Finally, we demonstrate that the nonlinear circuit dynamics match responses in both auditory and visual neurons. Therefore, we believe that this nonlinear circuit may be a general circuit motif that can be applied in different neural circuits, whenever it is necessary to provide an automatic improvement in the quality of the transmitted signal, for a fast varying input distribution.