# Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1

## TL;DR

This paper presents a neurodynamic model of V1 that simulates lateral interactions and subcortical processes to predict visual attention, outperforming existing models in saliency prediction and saccade sequence forecasting.

## Contribution

It introduces a biologically-inspired neural network model incorporating cortical magnification and novel top-down inhibition mechanisms for attention prediction.

## Key findings

- Outperforms other biologically-inspired saliency models.
- Accurately predicts saccade sequences during scene viewing.
- Demonstrates the impact of inhibition of return on attention prediction.

## Abstract

Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically-inpired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts.