Biology subjects

Poort, J.

Publications and source records attributed to Poort, J..

7 recordsLinked to original sources

bioRxiv2025

Distinct response selectivity changes in primary visual and parietal cortex during visual discrimination learning

When animals learn the behavioral relevance of sensory features, response selectivity in primary sensory areas increases for those features. However, the effect of learning on neuronal activity in higher-level areas associated with decision-making in the parietal cortex, compared to primary sensory cortex, is not yet known. We used two-photon calcium imaging to determine how learning modifies neural representations in primary visual cortex (V1) and posterior parietal cortex (PPC) in a visual go/no-go orientation discrimination task. We found that behavior improvements after learning were associated with increased neuronal selectivity in both V1 and PPC. The increased selectivity in PPC was mainly driven by neurons preferring the rewarded go stimulus, while, in V1, neurons increased their selectivity for both the rewarded go and unrewarded no-go stimulus. Furthermore, feature preference was robust in V1 neurons but reorganized in PPC after learning. Finally, feature preference was preserved across contexts in V1 but not in PPC after learning, where many neurons switched their preference to the rewarded feature during active task engagement. Our results demonstrate that learning a visually-guided discrimination task increased information about relevant sensory features through distinct changes in the bottom and top levels of the visual cortical hierarchy. Visual cortex neurons encode visual features with increased reliability but with preserved feature preferences, while parietal neurons reorganize their feature preferences in a task-specific manner. HighlightsO_LIV1 and PPC response selectivity increases after visual discrimination learning. C_LIO_LISelectivity increase in PPC for rewarded features, but in V1 for both rewarded and unrewarded features. C_LIO_LIFeature preference robust in V1 but altered in PPC across learning. C_LIO_LIFeature preference after learning preserved across contexts in V1 but not PPC. C_LI

neuroscience↗

bioRxiv2025

Contributions of superior colliculus and primary visual cortex to visual spatial detection in freely moving mice

Visual spatial detection is a crucial first step in vision to guide decision-making and action. However, its neural basis in freely behaving animals remains unclear due to challenges in controlling visual input and monitoring eye and head position. Studies in head-fixed mice have shown that both the superior colliculus (SC) and primary visual cortex (V1), two key visual processing hubs in mammals, contribute to visual spatial detection. Yet, their relative roles in freely moving animals are poorly understood. Here, we developed a novel approach to study the neural mechanisms of visual spatial detection in unrestrained mice. We combined closed-loop presentation of visual stimuli with neural recordings, optogenetic manipulation, and simultaneous monitoring of eye and head position. Our results show that SC cells are more predictive of reaction times than V1 cells. Furthermore, SC neurons exhibit more sustained activity than V1 neurons during visual spatial detection. Optogenetic SC and V1 silencing causes pervasive and remarkably localized perturbations of visual detection performance. SC silencing has a stronger impact on visual detection than V1 silencing. These results highlight the distinct activity patterns in two principal early visual processing centers, and establish their relative causal contributions to visual spatial detection.

neuroscience↗

bioRxiv2024

The impact of parvalbumin interneurons on visual discrimination depends on strength and timing of activation and task difficulty

Parvalbumin-expressing (PV) cells are the most common class of inhibitory interneurons in the visual cortex. They are densely connected to excitatory cells and play important roles in balancing cortical circuit activity and learning. PV cell activation is a tool to inactivate cortical regions to establish their role in visual processing. However, it is not established how moderate activation affects behaviour and how effects depend on activation strength, timing and task difficulty. We therefore investigated how these three major factors affect performance of mice in a go/no-go orientation discrimination task. We tested discrimination performance with different strength and timing of PV cell activation in V1 and with different task difficulty levels. We found that PV cell activation improved performance in easy discriminations when stimulating with moderate laser powers only during the initial 120 ms from stimulus onset, corresponding to the initial feedforward processing sweep across the cortical hierarchy. In the same animals, PV cell activation did not aid performance in difficult discriminations. However, in both easy and difficult discriminations, optimal behavioural performance required undisturbed late phase activity beyond 120 ms, highlighting the importance of sustained activity in V1. Combining the optogenetic activation of PV cells with two-photon imaging showed that behavioural changes were associated with changes in stimulus response selectivity in V1. Thus, our results demonstrate that early and sustained activity in V1 is crucial for perceptual discrimination and delineate specific conditions when PV cell activation shapes neuronal selectivity to improve behaviour. O_LIEffects of moderate optogenetic PV cell activation on behaviour are time window specific C_LIO_LIBenefits of increased PV cell-mediated inhibition depend on task difficulty C_LIO_LIOptimal behavioural performance requires sustained V1 activity in both easy and difficult discriminations C_LIO_LIBehavioural changes with PV cell activation are reflected by changes in the selectivity of V1 neurons C_LI

neuroscience↗

bioRxiv2021

Learning shapes cortical dynamics to enhance integration of relevant sensory input

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity amongst neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input. HighlightsO_LIA new theoretical principle links recurrent circuit dynamics to optimal sensory coding C_LIO_LIPredicts that high-SNR input dimensions activate slowly decaying modes of dynamics C_LIO_LIPopulation dynamics in primary visual cortex realign during learning as predicted C_LIO_LIStimulus-specific changes in E-I connectivity in recurrent circuits explain realignment C_LI

neuroscience↗

bioRxiv2021

Learning and attention increase visual response selectivity through distinct mechanisms

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance, and across seconds when animals switch attention. While both phenomena are expressed in the same cortical circuit, it is unknown whether they rely on similar mechanisms. We imaged activity of the same neuronal populations in primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the task relevant stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM and VIP inhibitory cell classes. Circuit modelling revealed that cell class-specific top-down inputs best explained attentional modulation, while the reorganization of local functional connectivity accounted for learning related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter time scales.

neuroscience↗

bioRxiv2020

Two distinct types of eye-head coupling in freely moving mice

Animals actively interact with their environment to gather sensory information. There is conflicting evidence about how mice use vision to sample their environment. During head restraint, mice make rapid eye movements strongly coupled between the eyes, similar to conjugate saccadic eye movements in humans. However, when mice are free to move their heads, eye movement patterns are more complex and often non-conjugate, with the eyes moving in opposite directions. Here, we combined eye tracking with head motion measurements in freely moving mice and found that both observations can be explained by the existence of two distinct types of coupling between eye and head movements. The first type comprised non-conjugate eye movements which systematically compensated for changes in head tilt to maintain approximately the same visual field relative to the horizontal ground plane. The second type of eye movements were conjugate and coupled to head yaw rotation to produce a "saccade and fixate" gaze pattern. During head initiated saccades, the eyes moved together in the same direction as the head, but during subsequent fixation moved in the opposite direction to the head to compensate for head rotation. This "saccade and fixate" pattern is similar to that seen in humans who use eye movements (with or without head movement) to rapidly shift gaze but in mice relies on combined eye and head movements. Indeed, the two types of eye movements very rarely occurred in the absence of head movements. Even in head-restrained mice, eye movements were invariably associated with attempted head motion. Both types of eye-head coupling were seen in freely moving mice during social interactions and a visually-guided object tracking task. Our results reveal that mice use a combination of head and eye movements to sample their environment and highlight the similarities and differences between eye movements in mice and humans. HighlightsO_LITracking of eyes and head in freely moving mice reveals two types of eye-head coupling C_LIO_LIEye/head tilt coupling aligns gaze to horizontal plane C_LIO_LIRotational eye and head coupling produces a "saccade and fixate" gaze pattern with head leading the eye C_LIO_LIBoth types of eye-head coupling are maintained during visually-guided behaviors C_LIO_LIEye movements in head-restrained mice are related to attempted head movements C_LI

animal behavior and cognition↗

bioRxiv2018

An ultralight head-mounted camera system integrates detailed behavioral monitoring with multichannel electrophysiology in freely moving mice

Breakthroughs in understanding the neural basis of natural behavior require neural recording and intervention to be paired with high-fidelity multimodal behavioral monitoring. An extensive genetic toolkit for neural circuit dissection, and well-developed neural recording technology, make the mouse a powerful model organism for systems neuroscience. However, methods for high-bandwidth acquisition of behavioral signals in mice remain limited to fixed-position cameras and other off-animal devices, complicating the monitoring of animals freely engaged in natural behaviors. Here, we report the development of an ultralight head-mounted camera system combined with head-movement sensors to simultaneously monitor eye position, pupil dilation, whisking, and pinna movements along with head motion in unrestrained, freely behaving mice. The power of the combined technology is demonstrated by observations linking eye position to head orientation; whisking to non-tactile stimulation; and, in electrophysiological experiments, visual cortical activity to volitional head movements.

neuroscience↗