The First International Workshop on Computational Models of the Visual Cortex: Hierarchies, Layers, Sparsity, Saliency and Attention

Research Article

A Study of the Role of the Maintained-Discharge Parameter in the Divisive Normalization Model of V1 Neurons

  • @INPROCEEDINGS{10.4108/eai.3-12-2015.2262400,
        author={Tadamasa Sawada and Alexander Petrov},
        title={A Study of the Role of the Maintained-Discharge Parameter in the Divisive Normalization Model of V1 Neurons},
        proceedings={The First International Workshop on Computational Models of the Visual Cortex: Hierarchies, Layers, Sparsity, Saliency and Attention},
        publisher={ACM},
        proceedings_a={CMVC},
        year={2016},
        month={5},
        keywords={primary visual cortex single-cell modeling divisive normalization},
        doi={10.4108/eai.3-12-2015.2262400}
    }
    
  • Tadamasa Sawada
    Alexander Petrov
    Year: 2016
    A Study of the Role of the Maintained-Discharge Parameter in the Divisive Normalization Model of V1 Neurons
    CMVC
    ACM
    DOI: 10.4108/eai.3-12-2015.2262400
Tadamasa Sawada1, Alexander Petrov2,*
  • 1: Higher School of Economics, Moscow, Russia
  • 2: Ohio State University, USA
*Contact email: apetrov1969@alexpetrov.com

Abstract

The divisive normalization model [Heeger, 1992] accounts successfully for a wide range of phenomena observed in single-cell physiological recordings from neurons in primary visual cortex (V1). Using mathematical analyses and simulation experiments, we investigated the role of the maintained-discharge (base firing rate) parameter in this model. We developed an implementation that can take grayscale images as inputs and applied it to the types of visual stimuli used in a comprehensive suite of published physiological studies. We found that three empirical phenomena are closely associated with the maintained-discharge parameter: (A) the existence of inhibitory regions in the receptive fields of simple cells in V1, (B) the supersaturation effect in the contrast sensitivity curves, and (C) the narrowing/widening of the spatial-frequency tuning curves when the stimulus contrast decreases. The model predicts two patterns of these phenomena: One possibility is that a neuron can show A, B, and widening (C); the other possibility is to show not-A, not-B, and narrowing (C). This interdependence is a potentially falsifiable prediction of the divisive normalization model.