Pieter R. Roelfsema

One of the biggest challenges for neurotechnology is the design of devices that are tolerated well by brain tissue, without sacrificing functionality and implantability. This study examined which design choices mitigate tissue damage and improve longevity by varying probe features implanted in the cerebral cortex of mice. We report on a systematic, quantitative analysis of neuronal and inflammation markers across cortical depth. We implanted a total of 103 stiff silicon or flexible polyimide probes in 32 mice, varying their thicknesses and widths, and either attaching them to the skull or not. A new, automated workflow to quantify immunohistochemical data examines: 1) the tissue loss caused by the implant, 2) the cortical neuronal density, and 3) the immune response expressed by astrocytic and microglial reaction. Flexible polyimide probes exhibited a clear advantage, causing fewer lesions and weaker immune responses than stiff silicon probes. Furthermore, we observed a weak influence of the shank cross‐section. A cortical depth profile of immune reactivity revealed focal reactions at the device entry points in the superficial cortex and at the cortex‐white matter boundary. This study gives important insights on optimizing device design parameters as well as surgical insights for improved tissue integration of intracortical electrode arrays.

Natural scenes usually contain many objects that need to be segregated from each other and the background. Object-based attention is the process that groups image fragments belonging to the same objects. Curve-tracing tasks provide a special case, testing our ability to group image elements of an elongated curve. In the brain, curve-tracing is associated with the gradual spread of enhanced neuronal activity over the representation of the traced curve. Previous studies demonstrated that the tracing speed is higher if curves are far apart than if they are nearby. One hypothesis is that a larger distance between curves permits activity propagation in higher visual cortical areas. In these higher areas receptive fields are larger and connections exist between neurons representing image regions that are farther apart (Pooresmaeili et al., 2014). We propose a recurrent architecture for the scale-invariant tracing of curves and objects. The architecture is composed of a feedforward pathway that dynamically selects the appropriate scale for tracing, and a recurrent pathway for propagating enhanced neuronal activity through horizontal and feedback connections, enabled by a disinhibitory loop involving VIP and SOM interneurons. We trained the network using a biologically plausible reinforcement learning scheme and observed that training on short curves allowed the networks to generalize to longer curves and 2D-objects. The network chose the scale based on the distance between curves and the width of objects, just as in human psychophysics and the visual cortex of monkeys. The results provide a mechanistic account of the learning and execution of multiscale perceptual grouping in the brain.

The Gestalt laws of perceptual organization determine which image regions in a visual scene are perceived as figures or background. Convex, enclosed, and symmetric regions are more likely to be perceived as figures, but the underlying neural mechanisms are not well understood. Here, we studied the influence of Gestalt cues on visual perception and on neuronal activity in early visual cortex. We found that, in both humans and macaques, the perceived contrast of Gabor probes was generally higher when placed on Gestalt-defined figures than on background regions, although individuals differed. Neural activity in areas V1 and V4 of the visual cortex of monkeys performing a contrast discrimination task was higher for image elements belonging to Gestalt-defined figures than image-elements of the background, except for symmetry-defined figures. We observed this figure-background modulation in both V1 and V4 even when the distinction between figure and background was irrelevant for the task of the animal. The influence of Gestalt cues on neural activity was stronger in V4 than in V1, suggesting that Gestalt organization arises through modulation of neural activity in higher visual areas, which then modulates neural activity in early visual areas through feedback.

One of the biggest challenges for neurotechnology is the design of devices that are tolerated well by brain tissue, without sacrificing functionality and implantability. This study examined which design choices mitigate tissue damage and improve longevity, by varying probe features implanted in the cerebral cortex of mice. We report on a systematic, quantitative analysis of neuronal and inflammation markers across cortical depth. We implanted a total of 103 stiff silicon or flexible polyimide probes in 32 mice, varying their thicknesses and widths, which were either attached to the skull or not. A new, automated workflow to quantify immunohistochemical data examines: 1) the tissue loss caused by the implant, 2) the cortical neuronal density, and 3) the immune response expressed by astrocytic and microglial reaction. Flexible polyimide probes exhibited a clear advantage, with fewer lesions and weaker immune responses than stiff silicon probes. Furthermore, we observed a weaker influence of the shank cross-section. A cortical depth profile of immune reactivity revealed focal reactions at the device entry points in the superficial cortex and at the cortex-white matter boundary. This study gives important insights on optimizing device design parameters as well as surgical insights for improved tissue integration of intracortical electrode arrays.

Studies in neuroscience inspired progress in the design of artificial neural networks (ANNs), and, vice versa, ANNs provide new insights into the functioning of brain circuits. So far, the focus has been on how ANNs can help to explain the tuning of neurons at various stages of the visual cortical hierarchy. However, the role of modulatory feedback connections, which play a role in attention and perceptual organization, has not yet been resolved. The present study presents a biologically plausible neural network that performs scene segmentation and can shift attention using modulatory feedback connections from higher to lower cortical brain areas. The model replicates several neurophysiological signatures of recurrent processing. Specifically, figural regions elicit more activity in model units than background regions. The modulation of activity by figure and ground occurs at a delay after the first feedforward response, because it depends on a loop through the higher model areas. Importantly, the figural response enhancement is amplified by object-based attention, which stays focused on the figural regions and does not spill over to the adjacent background, just as is observed in the visual cortex. Our results indicate how progress in artificial intelligence can be used to garner insight into the recurrent cortical processing for scene segmentation and object-based attention.

The lateral geniculate nucleus (LGN) of the thalamus is a key link between the retina and visual cortex but our understanding of the properties of neurons in the human LGN is based on recordings in animal models. Here we recorded spiking activity of cells in the LGN of two patients who had electrodes implanted in the LGN as part of their treatment for epilepsy. Human LGN cells responded to strong visual stimulation with high-frequency bursts of spikes. The cells had receptive-field properties resembling those of monkeys with circular ON-OFF sub-fields, red-green opponency in the dorsal layers and preferences for high temporal frequencies in the ventral layers. Responses were largely monocular and the closure of one eye decreased the spontaneous activity of broad-spiking neurons preferring this eye while increasing the activity of neurons with narrower spikes, suggesting that interneurons might gate LGN activity during eye closure.