Perceptual Learning via Modification of Cortical Top-Down Signals

Abstract

The primary visual cortex (V1) is pre-wired to facilitate the extraction of behaviorally important visual features. Collinear edge detectors in V1, for instance, mutually enhance each other to improve the perception of lines against a noisy background. The same pre-wiring that facilitates line extraction, however, is detrimental when subjects have to discriminate the brightness of different line segments. How is it possible to improve in one task by unsupervised practicing, without getting worse in the other task? The classical view of perceptual learning is that practicing modulates the feedforward input stream through synaptic modifications onto or within V1. However, any rewiring of V1 would deteriorate other perceptual abilities different from the trained one. We propose a general neuronal model showing that perceptual learning can modulate top-down input to V1 in a task-specific way while feedforward and lateral pathways remain intact. Consistent with biological data, the model explains how context-dependent brightness discrimination is improved by a top-down recruitment of recurrent inhibition and a top-down induced increase of the neuronal gain within V1. Both the top-down modulation of inhibition and of neuronal gain are suggested to be universal features of cortical microcircuits which enable perceptual learning.

Top-down modulation of sensory cortex gates perceptual learning

“…Our hypothesis is consistent with these models but posits that, rather than providing a static modulatory signal, top-down networks change throughout training, thereby contributing to improved ACx sensitivity and PL. This framework is similar to a computational model that has been proposed to explain visual brightness discrimination learning (84) and is consistent with human psychophysical and imaging evidence that training can improve visual attentional modulation (55,85,86) and general cognitive skills (23). Thus, training-induced plasticity in top-down modulatory processes may be a general mechanism that supports PL across sensory modalities.…”

Perceptual Expertise and Top–Down Expectation of Musical Notation Engages the Primary Visual Cortex

“…Instead, the task-dependent tuning of the neural network may be largely implicit, non-verbalizable, highly associated with different parts of the neural network and may occur automatically when the participants know that they are about to perform a well-learned task with a set of highly familiar stimuli. Such flexibility in tuning up the neural network may be important especially when early retinotopic cortex is involved in perceptual learning, because changing the low-level representation in a permanent, hard-wired way may be detrimental to performing other visual tasks (Fahle & Poggio, 2002; Gilbert, Sigman, & Crist, 2001; Schafer, Vasilaki, & Senn, 2007). …”

Contrast-dependence of surround suppression in Macaque V1: Experimental testing of a recurrent network model

“…We have developed a network model for surround suppression in V1, which incorporates feedback connections from extrastriate cortex, and can account for many experimental findings using only one set of parameters (Schwabe et al, 2006). Feedback connections are generally believed to serve attentional (Maunsell and Treue, 2006) and other task-related top-down modulations (Navalpakkam and Itti, 2007; Salinas, 2006; Schwabe and Obermayer, 2005), or to play a role in perceptual learning (Li et al, 2008; Schäfer et al, 2007). However, we (Angelucci and Bressloff, 2006) have recently proposed that these connections, which have a much larger spatial scale (Angelucci et al, 2002) and are much faster-conducting (Girard et al, 2001) than intra-V1 horizontal connections, may also generate surround modulation in V1 (Fig.…”