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Ibbotson, M. R.

Publications and source records attributed to Ibbotson, M. R..

3 recordsLinked to original sources

Carbon Cybernetics Array: a miniaturized carbon-based microelectrode array for intracortical recording

Implantable brain-machine interfaces, capable of capturing high-resolution neural signals, hold great promise for treating neurological conditions and advancing neuroscience research. As the field moves toward chronic, fully implantable systems, ensuring the long-term safety and stability of the microelectrode arrays, the core component of these interfaces, remains a significant challenge. Hermetic sealing, which prevents moisture and contaminants from entering the device, is a critical yet often overlooked factor in array designs. This study reports the Carbon Cybernetics Array, a hermetically sealed, carbon-based microelectrode array for neural interfacing. Carbon fiber electrodes are anchored on a diamond substrate via a hermetic feedthrough structure, with hermeticity validated through helium leak testing. These ultrathin and flexible carbon fibers require minimal insertion force, cause little tissue damage after six months of implantation in rats and enable single-unit neural recordings. Furthermore, a low-cost, manually controllable insertion toolset is developed for precise array implantation in the sheep brain through a small burr hole, demonstrating a step toward clinical translation. Finally, the scalability of the fabrication approach is exemplified by arrays with over 1000 fibers. Together, this hermetically sealed feedthrough structure will lay the foundation for a future fully implantable device with wireless capabilities.

bioengineering↗

How Stimulus Statistics Affect The Receptive Fields of V1 Cells

We studied the changes that neuronal RF models undergo when the statistics of the stimulus are changed from those of white Gaussian noise (WGN) to those of natural scenes (NS). Fitting the model to data estimates both a cascade of linear filters on the stimulus, as wells as the static nonlinearities that map the output of the filters to the neuronal spike rates. We found that cells respond differently to these two classes of stimuli, with mostly higher spike rates and shorter response latencies to NS than to WGN. The most striking finding was that NS resulted in RFs that had additional uncovered filters than did WGN. This finding was not an artefact of the higher spike rates but rather related to a change in coding. Our results reveal a greater extent of nonlinear processing in V1 neurons when stimulated using NS compared to WGN. Our findings indicate the existence of nonlinear mechanisms that endow V1 neurons with context-dependent transmission of visual information.

neuroscience↗

Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex

Visual object identification requires both selectivity for specific visual features that are important to the objects identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognised as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we show that the balance between selectivity and invariance in complex cells is more diverse than thought. Phase invariance is frequently partial, thus retaining sensitivity to brightness polarity, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.

neuroscience↗