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Full-Text Articles in Life Sciences
Characterizing Receptive Field Selectivity In Area V2, Corey M. Ziemba, Robbe Lt Goris, J Anthony Movshon, Eero P. Simoncelli
Characterizing Receptive Field Selectivity In Area V2, Corey M. Ziemba, Robbe Lt Goris, J Anthony Movshon, Eero P. Simoncelli
MODVIS Workshop
The computations performed by neurons in area V1 are reasonably well understood, but computation in subsequent areas such as V2 have been more difficult to characterize. When stimulated with visual stimuli traditionally used to investigate V1, such as sinusoidal gratings, V2 neurons exhibit similar selectivity (but with larger receptive fields, and weaker responses) relative to V1 neurons. However, we find that V2 responses to synthetic stimuli designed to produce naturalistic patterns of joint activity in a model V1 population are more vigorous than responses to control stimuli that lacked this naturalistic structure (Freeman, et. al. 2013). Armed with this signature …
A Recurrent Multilayer Model With Hebbian Learning And Intrinsic Plasticity Leads To Invariant Object Recognition And Biologically Plausible Receptive Fields, Michael Teichmann, Fred H. Hamker
A Recurrent Multilayer Model With Hebbian Learning And Intrinsic Plasticity Leads To Invariant Object Recognition And Biologically Plausible Receptive Fields, Michael Teichmann, Fred H. Hamker
MODVIS Workshop
Much effort has been spent to develop biologically plausible models for different aspects of the processing in the visual cortex. However, most of these models are not investigated with respect to the functionality of the neural code for the purpose of object recognition comparable to the framework of deep learning in the machine learning community.
We developed a model of V1 and V2 based on anatomical evidence of the layered architecture, using excitatory and inhibitory neurons where the connectivity to each neuron is learned in parallel. We address learning by three different mechanisms of plasticity: intrinsic plasticity, Hebbian learning with …