Adaptive Gain Modulation in V1 Explains Contextual Modifications during Bisection Learning

Abstract

The neuronal processing of visual stimuli in primary visual cortex (V1) can be modified by perceptual training. Training in bisection discrimination, for instance, changes the contextual interactions in V1 elicited by parallel lines. Before training, two parallel lines inhibit their individual V1-responses. After bisection training, inhibition turns into non-symmetric excitation while performing the bisection task. Yet, the receptive field of the V1 neurons evaluated by a single line does not change during task performance. We present a model of recurrent processing in V1 where the neuronal gain can be modulated by a global attentional signal. Perceptual learning mainly consists in strengthening this attentional signal, leading to a more effective gain modulation. The model reproduces both the psychophysical results on bisection learning and the modified contextual interactions observed in V1 during task performance. It makes several predictions, for instance that imagery training should improve the performance, or that a slight stimulus wiggling can strongly affect the representation in V1 while performing the task. We conclude that strengthening a top-down induced gain increase can explain perceptual learning, and that this top-down signal can modify lateral interactions within V1, without significantly changing the classical receptive field of V1 neurons.

Phasic and Tonic Patterns of Locus Coeruleus Output Differentially Modulate Sensory Network Function in the Awake Rat

“…The adaptive gain model proposed by Aston-Jones and Cohen (2005b) predicts that tonic LC activity increases the "gain" or global responsivity of cells in noradrenergic terminal fields to any excitatory input, whereas phasic LC output increases the "gain" of all units in a manner that produces an "all or nothing" response profile to excitatory input. A further understanding of the impact of adaptive gain modulation on discharge properties of target neurons is provided by recent studies in primary visual cortex suggesting that under high gain conditions (as would occur following phasic bursts of LC discharge), weakly active neurons are suppressed concordant with a competitive enhancement of strongly active neurons (Schafer et al 2009). Although the LC adaptive gain theory was originally proposed for associational cortices and decision making networks, our current data support this hypothesis by demonstrating that phasic LC output generally increases the responsiveness of neurons in sensory thalamus and cortical regions to excitatory afferent stimuli and alters stimulus input-response output function of cortical neuron responses to increasing afferent input strength, whereas tonic LC output facilitates cortical responses of individual neurons to all levels of afferent input.…”

Dichotomy in perceptual learning of interval timing: calibration of mean accuracy and precision differ in specificity and time course

“…A high specificity has been interpreted as adaptive changes at a relatively early, low-tier processing stage (Adini et al 2002;Gilbert et al 2001;Tsodyks and Gilbert 2004), whereas a nonspecific transfer of learning was taken as evidence for changes in top-down signals such as selective attention during PL (Ahissar and Hochstein 1997;Ahissar et al 2009;Schäfer et al 2009;Yu et al 2004). We examined learning specificity on a fine scale for a wide range of ⌬Ts.…”

Failure to learn during roving, analysing the unsupervised bias hypothesis

“…Furthermore, since we are mostly interested in the failure to learn during roving the linear representation can be considered a more interesting and challenging case. Previous attempts to explain bisection-task perceptual learning, have relied on physiologically known connectivity to lead to transfer/non-transfer of learning effects [34,27]. In our case, we are testing only the effects of the unsupervised bias, allowing us to vastly simplify the encoding and thus the analysis.…”

“…Feedback leads to learning, which leads to an improvement in the decision process, except in the case of stimulus roving. A number of other models of perceptual learning have been proposed [34,27,17,30]. We will summarise them briefly here and evaluate their capacity to explain failure to learn during task roving.…”

“…Artificial neural network models have been used to isolate the premises of perceptual learning and check their logical consistency [14]. Meanwhile, models of the early visual processing system have attempted to draw links from bio-logically inspired models to results of perceptual learning experiments [34,27]. A review of modelling approaches can be found in [31].…”