A neural model for V1 that incorporates dendritic nonlinearities and back-propagating action potentials

Abstract

The work of Hubel and Wiesel has been instrumental in shaping our understanding of V1, leading to modeling neural responses as cascades of linear and nonlinear processes in what is known as the "standard model" of vision. Under this formulation, however, some dendritic properties cannot be represented in a practical manner, while evidence from both experimental and theoretical work indicates that dendritic processes are an indispensable element of key neural behaviors. As a result, current V1 models fail to explain neural responses in a number of scenarios. In this work, we propose an implicit model for V1 that considers nonlinear dendritic integration and backpropagation of action potentials from the soma to the dendrites. Our model can be viewed as an extension of the standard model that minimizes an energy function, allows for a better conceptual understanding of neural processes, and explains several neurophysiological phenomena that have challenged classical approaches. Significance statementMost current approaches for modeling neural activity in V1 are data driven; their main goal is to obtain better predictions and are formally equivalent to a deep neural network (DNN). Aside from behaving like a black-box these models ignore a key property of biological neurons, namely, that they integrate their input via their dendrites in a highly nonlinear fashion that includes backpropagating action potentials (bAPs). Here, we propose a model based on dendritic mechanisms, which facilitates conceptual analysis and can explain a number of physiological results that challenge standard approaches. Our results suggest that the proposed model may provide a better understanding of neural processes and be considered as a contribution in the search of a consensus model for V1.