V1 microcircuit dynamics: altered signal propagation suggests intracortical origins for adaptation in response to visual repetition

Abstract

Repetitive visual stimulation profoundly changes sensory processing in the primary visual cortex (V1). We show how the associated adaptive changes are linked to an altered flow of synaptic activation across the V1 laminar microcircuit. Using repeated visual stimulation, we recorded layer-specific responses in V1 of two fixating monkeys. We found that repetition-related spiking suppression was most pronounced outside granular V1 layers that receive the main retinogeniculate input. This repetition-related response suppression was robust to alternating stimuli between the eyes, in line with the notion that repetition-related adaptation is predominantly of cortical origin. Most importantly, current source density (CSD) analysis, which provides an estimate of local net depolarization, revealed that synaptic processing during repeated stimulation was most profoundly affected within supragranular layers, which harbor the bulk of cortico-cortical connections. Direct comparison of the temporal evolution of laminar CSD and spiking activity showed that stimulus repetition first affected supragranular synaptic currents, which translated into a reduction of stimulus-evoked spiking across layers. Together, these results suggest that repetition induces an altered state of intracortical processing that underpins visual adaptation. NEW & NOTEWORTHY Our survival depends on our brains rapidly adapting to ever changing environments. A well-studied form of adaptation occurs whenever we encounter the same or similar stimuli repeatedly. We show that this repetition-related adaptation is supported by systematic changes in the flow of sensory activation across the laminar cortical microcircuitry of primary visual cortex. These results demonstrate how adaptation impacts neuronal interactions across cortical circuits.

Fine-scale computations for adaptive processing in the human brain

“…Consistent with this interpretation, previous neurophysiological studies have shown stronger decrease in neural population responses due to stimulus repetition in superficial layers of V1, while delayed adaptation effects in middle and deeper levels (Westerberg, Cox, Dougherty, & Maier, 2019).…”

Fine-scale computations for adaptive processing in the human brain

“…Consistent with this interpretation, previous neurophysiological studies have shown stronger decrease in neural population responses due to stimulus repetition in superficial layers of V1, while delayed adaptation effects in middle and deeper levels (Westerberg, Cox, Dougherty, & Maier, 2019). The neural connectivity within superficial V1 layers (layer 2/3) has been also shown to be rapidly and dynamically modulated by sensory adaptation (Hansen & Dragoi, 2011).…”

Priming of Attentional Selection in Macaque Visual Cortex: Feature-Based Facilitation and Location-Based Inhibition of Return

“…The size of the items in the array scaled with eccentricity at 0.3 dva per dva eccentricity so that they were smaller than the estimated overall receptive field size (Freeman and Simoncelli 2011). To determine the orientation and eccentricity of the array, each day online multiunit activity was measured during a receptive field mapping task (Cox et al 2013(Cox et al , 2019Dougherty et al 2019;Westerberg et al 2019), where the monkey fixated while a series of stimuli were presented across the visual field. The array was then oriented so that its eccentricity coincided with the location of the receptive field (3-10 dva eccentricity) and a single array item was placed at the center of the receptive field.…”

“…Target selection time was measured using previously reported methods (Bichot and Schall 2002, see also Bradley et al 1987;Britten et al 1992;Westerberg et al 2019), rooted in signal detection theory (Green and Swets 1966;Macmillan and Creelman 2005). We performed this analysis at the population level: Using 1 ms increments, we compared the activity between the target and distractor conditions at each timepoint from 50 ms prior to array onset to 250 ms post array onset by calculating receiver operating characteristic curves (ROC).…”