Lifeboat Foundation

How far would you go to keep your mind from failing? Would you go so far as to let a doctor drill a hole in your skull and stick a microchip in your brain?

It’s not an idle question. In recent years neuroscientists have made major advances in cracking the code of memory, figuring out exactly how the human brain stores information and learning to reverse-engineer the process. Now they’ve reached the stage where they’re starting to put all of that theory into practice.

Last month two research teams reported success at using electrical signals, carried into the brain via implanted wires, to boost memory in small groups of test patients. “It’s a major milestone in demonstrating the ability to restore memory function in humans,” says Dr. Robert Hampson, a neuroscientist at Wake Forest School of Medicine and the leader of one of the teams.

As fun as brain-computer interfaces (BCI) are, for the best results they tend to come with the major asterisk of requiring the cutting and lifting of a section of the skull in order to implant a Utah array or similar electrode system. A non-invasive alternative consists out of electrodes which are placed on the skin, yet at a reduced resolution. These electrodes are the subject of a recent experiment by [Shaikh Nayeem Faisal] and colleagues in ACS Applied NanoMaterials employing graphene-coated electrodes in an attempt to optimize their performance.

Although external electrodes can be acceptable for basic tasks, such as registering a response to a specific (visual) impulse or for EEG recordings, they can be impractical in general use. Much of this is due to the disadvantages of the ‘wet’ and ‘dry’ varieties, which as the name suggests involve an electrically conductive gel with the former.

This gel ensures solid contact and a resistance of no more than 5 – 30 kΩ at 50 Hz, whereas dry sensors perform rather poorly at 200 kΩ at 50 Hz with worse signal-to-noise characteristics, even before adding in issues such as using the sensor on a hairy scalp, as tends to be the case for most human subjects.

Cameras for machine vision and robotics are essentially bionic devices mimicking human eyes. These applications require advanced color imaging systems to possess a number of attributes such as high resolution, large FoV, compact design, light-weight and low energy consumption, etc¹. Conventional imaging systems based on CCD/CMOS image sensors suffer from relatively low FoV, bulkiness, high complexity, and power consumption issues, especially with mechanically tunable optics. Recently, spherical bionic eyes with curved image sensor retinas have triggered enormous research interest^{1,2,3,4,5,6,7}. This type of devices possess several appealing features such as simplified lens design, low image aberration, wide FoV, and appearance similar to that of the biological eyes rendering them suitable for humanoid robots^{8,9,10,11,12,13}. However, the existing spherical bionic eyes with curved retinas typically only have fixed lens and can only acquire mono color images. Fixed lenses cannot image objects with varying distances. On the other hand, conventional color imaging function of CCD/CMOS image sensors are achieved by using color filter arrays, which add complexity to the device fabrication and cause optical loss^{14,15,16,17,18,19}. Typical absorptive organic dye filters suffer from poor UV and high-temperature stabilities, and plasmonic color filters suffer from low transmission^20,21,22. And it is even more challenging to fabricate color filter arrays on hemispherical geometry where most traditional microelectronic fabrication methods are not applicable.

Herein, we demonstrate a novel bionic eye design that possesses adaptive optics and a hemispherical nanowire array retina with filter-free color imaging and neuromorphic preprocessing abilities. The primary optical sensing function of the artificial retina is realized by using a hemispherical all-inorganic CsPbI₃ nanowire array that can produce photocurrent without external bias leading to a self-powered working mode. Intriguingly, an electrolyte-assisted color-dependent bidirectional synaptic photo-response is discovered in a well-engineered hybrid nanostructure. Inspired by the vertical alignment of a color-sensitive cone cell and following neurons, the device structure vertically integrates a SnO₂/NiO double-shell nanotube filled with ionic liquid in the core on top of a CsPbI₃/NiO core-shell nanowire. It is found that the positive surrounding gate effect of NiO due to photo hole injection can be partially or fully balanced by electrolyte under shorter (blue) or longer (green and red) wavelength illuminations, respectively. Thus, the device can yield either positive or negative photocurrent under shorter or longer wavelength illumination, respectively. The carriers can be accumulated in SnO₂/NiO structure, giving rise to the bidirectional synaptic photo-response. This color-sensitive bidirectional photo-response instills a unique filter-free color imaging function to the retina. The synaptic behavior-based neuromorphic preprocessing ability, along with the self-powered feature, effectively reduce the energy consumption of the system^{23,24,25,26,27,28}. Moreover, the color selectivity of each pixel can be tuned by a small external bias to detect more accurate color information. We demonstrate that the device can reconstruct color images with high fidelity for convolutional neural network (CNN) classifications. In addition, our bionic eye integrates adaptive optics in the device, by integrating an artificial crystalline lens and an electronic iris based on liquid crystals. The artificial crystalline lens can switch focal length to detect objects from different distances, and the electronic iris can control the amount of light reaching the retina which enhances the dynamic range. Both of the optical components can be easily tuned by the electric field, which are fast, compact, and much more energy efficient compared to the conventional mechanically controlled optics reported hitherto. (Supplementary Table 1 compares our system with some commercial zoom lenses.) The combination of all these unique features makes the bionic eye structurally and functionally equivalent to its biological counterpart.

Researchers show how Stable Diffusion can read minds. The method reconstructs images from fMRI scans with amazing accuracy.

Researchers have been using AI models to decode information from the human brain for years. At their core, most methods involve using pre-recorded fMRI images as input to a generative AI model for text or images.

In early 2018, for example, a group of researchers from Japan demonstrated how a neural network reconstructed images from fMRI recordings. In 2019, a group reconstructed images from monkey neurons, and Meta’s research group, led by Jean-Remi King, has published new work that derives text from fMRI data, for example.

Neuralace™ is a glimpse of what’s possible in the future of BCI.

This patent pending concept technology is the start of Blackrock’s journey toward whole-brain data capture–with transformative potential for the way neurological disorders are treated. With over 10,000 channels and a flexible lace structure that seamlessly conforms to the brain, Neuralace has potential applications in vision and memory restoration, performance prediction, and the treatment of mental health disorders like depression.

Continue reading “Neuralace™ | The next-generation of BCI and whole-brain data capture” »

Describes and demonstrates the MR technique of Diffusion Tensor Imaging and reviews some of the basic mathematics of Tensors including matrix multiplication, eigenvalues and eigenvectors.

Blackrock’s long-tested NeuroPort® Array, widely considered the gold standard of high-channel neural interfacing, has been used in human BCIs since 2004 and powered many of the field’s most significant milestones. In clinical trials, patients using Blackrock’s BCI have regained tactile function, movement of their own limbs and prosthetics, and the ability to control digital devices, despite diagnoses of paralysis and other neurological disorders.

While Blackrock’s BCI enables patients to execute sophisticated functions without reliance on assistive technologies, next-generation BCIs for areas such as vision and memory restoration, performance prediction, and treatment of mental health disorders like depression will need to interface with more neurons.

Neuralace is designed to capitalize on this need; with 10,000+ channels and the entire scalable system integrated on an extremely flexible lace-structured chip, it could capture data that is orders of magnitude greater than existing electrodes, allowing for an exponential increase in capability and intuitiveness.

Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and computational biology. In this Primer, Hellinger et al. describe the techniques and workflows required for peptide discovery and characterization and give an overview of biological and clinical applications of peptidomics.

A book talk by:

Robert Stickgold, PhD

Continue reading “When Brains Dream: Exploring the Science and Mystery of Sleep” »