A Structured Model of Video Reproduces Primary Visual Cortical Organisation

By Pietro Berkes, Richard E. Turner, Maneesh Sahani. 2009

44 citations recorded by the source corpus.

The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation (2016, positive): “The computational insight enabling depth-rotation tolerant representations to be learned from experience is that, due to properties of how objects move in the world, temporally adjacent frames (the giw) almost always depict the same object [22, 48, 15, 58, 10, 26].”
Discovering Hidden Factors of Variation in Deep Networks (2014, methodology): “motion [4, 5, 6, 7, 8, 9], have relied upon a bilinear model architecture in which the units representing different factors are combined multiplicatively.”
Emergence of Complex-Like Cells in a Temporal Product Network with Local Receptive Fields (2010, neutral): “Another structured model of video is Berkes et al. (2009).”
Transforming Auto-Encoders (2011, methodology): “A model proposed in [1] uses a very different learning procedure to learn a similar representation to the transforming auto-encoder.”
Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex (2012, unknown): “…to phase shifts with large oscillations as measured by the ratio of the modulation response (F1) to the mean ﬁring rate (F0) given a grating with optimal frequency and orientation (Skottun et al., 1991; Einh¨auser et al., 2002; Johnson, Hawken, & Shapley, 2008; Berkes, Turner, & Sahani, 2009).”
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics (2014, background): “(Conversely, inference of z may provide an account of complex-cell activations [41, 42, 43], which we did not study in further detail here.”
Slowness and Sparseness Have Diverging Effects on Complex Cell Learning (2014, background): “While such an experiment has already been done for sparseness by [8] who interestingly found that sparseness decreases throughout development, we are not aware of the equivalent evaluation of any change in neuronal slowness.”