Learning and Adaptation in the Primary Visual Cortex

该项目旨在使用计算神经科学来了解初级视觉皮层（V1）如何适应其环境中不断变化的视觉统计数据，以及为什么。它属于EPSRC的生物信息学研究领域。在哺乳动物的视觉皮层中，某些细胞具有"方向选择性"，这意味着（在它们敏感的视野区域内），它们对特定方向上交替的黑色和白色条纹做出最佳反应（Hubel和Wibel 1959）。当观察自然场景时，方向由V1统一表示;然而，当特定方向的频率增加时，该表示被调整以保持放电率的稳态（Benucci等人，2013）。这种适应使一阶和二阶放电统计量保持近似恒定，因此不能用单个神经元水平的机制来解释。这一领域的最新进展是以这种皮层活动的描述性模型的形式出现的（Westrick et al. 2016）。该模型旨在理解归一化方面的调整，这是V1执行的一种建议的规范计算（Carandini和Heeger 2011）。然而，目前还没有一个机制层面的解释，这些动态。之前已经证明，稳定超线性网络（SSN）模型能够执行归一化计算（Ahmadian et al. 2013）。因此，我们将调查的可能性，通过补充SSN与适当的可塑性规则，我们可以制定一个机械模型的适应。我们的目标还在于利用最近的有效表征理论（Ganguli and Simoncelli 2014）来理解这种适应的计算动机（如果有的话）。参考文献：[1] D. H. Hubel和T. n. Wigerland，“猫纹状体皮层中单个神经元的感受野”，J Physiol，第148卷，第3期，第148页。574 - 591，Oct. 1959. [2]a. Benucci，A. B。Saleem和M. Carandini，“适应性维持初级视觉皮层的群体动态平衡”，Nat Neurosci，第16卷，第6期，第16页。724 - 729，2013年6月，doi：10.1038/nn.3382。[3]z. M. Westrick，D. J. Heeger和M. S. Landy，“模式适应和标准化重新加权”，神经科学杂志，第36卷，第38页。9805 - 9816，2016年9月，doi：10.1523/JNEUROSCI.1067 - 16.2016。[4]M. Carandini和D. J. Heeger，“规范化作为一种典型的神经计算”，Nat Rev Neurosci，第13卷，第1期，第13页。51 - 62，2012年1月，doi：10.1038/nrn3136。[5]y. Ahmadian，D. B。Rubin和K. D.米勒，"稳定的超线性网络的分析"，神经计算，第25卷，第8期，第10页。1994 - 2037，2013年8月，doi：10.1162/NECO_a_00472。[6]D. Ganguli和E. P. Simoncelli，"Efficient Sensory Encoding and Bayesian Inference with Heterogeneous Neural Populations"，Neural Computation，vol. 26，no. 10，pp. 2103 - 2134，2014年10月，doi：10.1162/NECO_a_00638。

This project aims to use computational neuroscience to understand how the primary visual cortex (V1) adapts to the changing visual statistics of its environment, and why. It is within the EPSRC's Biological Informatics research area. In the mammalian visual cortex, certain cells are "orientation selective", meaning that (within the region of the visual field they are sensitive to), they respond optimally to alternating black and white stripes at a particular orientation (Hubel and Wiesel 1959). When observing natural scenes, the orientations are represented uniformly by V1; however, when the frequency of a particular orientation is increased, the representation is adjusted to maintain a homeostasis in firing rates (Benucci et al. 2013). This adaptation keeps both first and second order firing statistics approximately constant, and therefore cannot be explained by a mechanism at the level of individual neurons. Recent progress in this area has come in the form of a descriptive model of this cortical activity (Westrick et al. 2016). This model aims to understand the adjustment in terms of normalisation, a proposed canonical computation performed by V1 (Carandini and Heeger 2011). However, there is not yet a mechanistic level explanation for these dynamics. It has previously been demonstrated that the Stabilized Supralinear Network (SSN) model is able to perform normalisation computations (Ahmadian et al. 2013). We will therefore investigate the possibility that by supplementing the SSN with appropriate plasticity rules we can formulate a mechanistic model of adaptation. We also aim to understand what (if any) computational motivation there is for this adaptation, using recent theories of efficient representation (Ganguli and Simoncelli 2014).References: [1] D. H. Hubel and T. N. Wiesel, 'Receptive fields of single neurones in the cat's striate cortex', J Physiol, vol. 148, no. 3, pp. 574-591, Oct. 1959.[2] A. Benucci, A. B. Saleem, and M. Carandini, 'Adaptation maintains population homeostasis in primary visual cortex', Nat Neurosci, vol. 16, no. 6, pp. 724-729, Jun. 2013, doi: 10.1038/nn.3382.[3] Z. M. Westrick, D. J. Heeger, and M. S. Landy, 'Pattern Adaptation and Normalization Reweighting', Journal of Neuroscience, vol. 36, no. 38, pp. 9805-9816, Sep. 2016, doi: 10.1523/JNEUROSCI.1067-16.2016.[4] M. Carandini and D. J. Heeger, 'Normalization as a canonical neural computation', Nat Rev Neurosci, vol. 13, no. 1, pp. 51-62, Jan. 2012, doi: 10.1038/nrn3136.[5] Y. Ahmadian, D. B. Rubin, and K. D. Miller, 'Analysis of the stabilized supralinear network', Neural Comput, vol. 25, no. 8, pp. 1994-2037, Aug. 2013, doi: 10.1162/NECO_a_00472.[6] D. Ganguli and E. P. Simoncelli, 'Efficient Sensory Encoding and Bayesian Inference with Heterogeneous Neural Populations', Neural Computation, vol. 26, no. 10, pp. 2103-2134, Oct. 2014, doi: 10.1162/NECO_a_00638.