Learning and Adaptation in the Primary Visual Cortex

这个项目旨在使用计算神经科学来理解初级视觉皮质(V1)如何适应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质中，某些细胞是“方向选择性的”，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白条纹做出最佳反应(Hubel和Wiseel 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发射速率的动态平衡(Benucci等人。2013)。这种适应使一阶和二阶放电统计基本保持不变，因此不能用单个神经元水平上的机制来解释。这一领域的最新进展以描述这一皮质活动的模型的形式出现(Westrick等人。2016)。该模型旨在理解归一化方面的调整，归一化是V1执行的一种拟议的规范计算(Carandini和Heeger，2011)。然而，对于这些动态，目前还没有一个机械论层面的解释。以前已经证明，稳定的超线性网络(SSN)模型能够执行归一化计算(Ahmadian等人)。2013)。因此，我们将研究通过用适当的塑性规则来补充SSN的可能性，我们可以制定一个适应的机制模型。参考文献：[1]D.H.Hubel和T.N.威塞尔，《Single Neurons in the cat‘s striatate皮质》，J Physiol，Vol.148，No.3，pp.574-591,1959年10月。[2]A.Benucci，A.B.Saleem和M.Carandini，《Adaption Maintain Popular Homomomalin Prial Visualear》，Nat Neurosci，Vol.16，No.6，2013年6月，第724-729页，DOI：10.1038/nn.3382.[3]Z.M.Westrick，D.J.Heeger和M.S.Landy，《模式适应和归一化重新加权》，《神经科学杂志》，第36卷，第38期，第9805-9816页，2016年9月，DOI：10.1523/JNEUROSCI.1067-16.2016。[4]M.Carandini和D.J.Heeger，《标准化为一种规范的神经计算》，NAT Rev Neurosci，第13卷，第1期，第51-62页，2012年1月，DOI：10.1038/NECO_a_00472.[5]Y.Ahmadian，D.B.Rubin和K.D.Miller，《稳定化超线性网络的分析》，《神经计算》，第25卷，第8期，第1994-2037页，2013年8月，DOI：10.1162/Neco_a_00472.[6]D.Ganguli和E.P.Simoncell，《神经计算》，第26卷，第10期，第2103-2134页，2014年10月，DOI：10.1162/Neco_a_3136.

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.