A 62-year-old Australian man paralyzed following his diagnosis with amyotrophic lateral sclerosis (ALS) has become the first individual to send out a message on social media using a brain-computer interface, RT reported. Brain-computer interfaces (BCI) are the next big thing in technology. While some people like Elon Musk want to use it to enhance human experiences as early as next year, others such as Synchron, whose interface helped Australian Philip O'Keefe send out his first tweet, want to develop it as a prosthesis for paralysis and treat other neurological diseases such as Parkinson's disease in the future, the company said in a press release. Synchron's BCI works through its brain implant called Stentrode that does not require any brain surgery to be installed. Instead, the company leverages the intentional techniques that are commonly used to treat stroke to implant the Stentrode via the jugular vein, the press release said.
In this work we propose a new biophysical computational model of brain regions relevant to Parkinson's Disease based on local field potential data collected from the brain of marmoset monkeys. Parkinson's disease is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled single-neuron mean firing rates and spectral signatures of local field potentials from healthy and parkinsonian marmoset brain data. As far as we are concerned, this is the first computational model of Parkinson's Disease based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results show that the proposed model could facilitate the investigation of the mechanisms of PD and support the development of techniques that can indicate new therapies. It could also be applied to other computational neuroscience problems in which biological data could be used to fit multi-scale models of brain circuits.
This paper proposes a framework for the biological learning mechanism as a general learning system. The proposal is as follows. The bursting and tonic modes of firing patterns found in many neuron types in the brain correspond to two separate modes of information processing, with one mode resulting in awareness, and another mode being subliminal. In such a coding scheme, a neuron in bursting state codes for the highest level of perceptual abstraction representing a pattern of sensory stimuli, or volitional abstraction representing a pattern of muscle contraction sequences. Within the 50-250 ms minimum integration time of experience, the bursting neurons form synchrony ensembles to allow for binding of related percepts. The degree which different bursting neurons can be merged into the same synchrony ensemble depends on the underlying cortical connections that represent the degree of perceptual similarity. These synchrony ensembles compete for selective attention to remain active. The dominant synchrony ensemble triggers episodic memory recall in the hippocampus, while forming new episodic memory with current sensory stimuli, resulting in a stream of thoughts. Neuromodulation modulates both top-down selection of synchrony ensembles, and memory formation. Episodic memory stored in the hippocampus is transferred to semantic and procedural memory in the cortex during rapid eye movement sleep, by updating cortical neuron synaptic weights with spike timing dependent plasticity. With the update of synaptic weights, new neurons become bursting while previous bursting neurons become tonic, allowing bursting neurons to move up to a higher level of perceptual abstraction. Finally, the proposed learning mechanism is compared with the back-propagation algorithm used in deep neural networks, and a proposal of how the credit assignment problem can be addressed by the current proposal is presented.
I am incredibly proud and excited to present the very first public product of Peptone, the Database of Structural Propensities of Proteins. Database of Structural Propensities of Proteins (dSPP) is the world's first interactive repository of structural and dynamic features of proteins with seamless integration for leading Machine Learning frameworks, Keras and Tensorflow. IDPs are implicated in numerous debilitating human pathologies, including Alzheimer's, Parkinson's, prion diseases, molecular basis of cancer, HIV, HSV, HVC, ZIKVR, and many others. MOAG-4 is known to enhance the process of protein aggregation in animal brain models, thus accelerating an early onset of Parkinson's disease. As opposed to binary (logits) secondary structure assignments available in other protein datasets for experimentalists and the machine learning community, dSPP data report on protein structure and local dynamics at the residue level with atomic resolution, as gauged from continuous structural propensity assignment in a range -1.0 to 1.0.
When you repair electronics, you frequently test individual parts to see how they affect the whole. Why not try that with the brain? Stanford is doing just that. It developed a technique that fires specific kinds of neurons to map the brain and identify problems caused by Parkinson's and other diseases. The approach first uses optogenetics to make neurons activate in response to light, and follows up with a functional MRI scan to look for the increased blood flow that indicates activity in other brain regions.
Want to understand what happens to the brain as it ages, or figure out how people learn to recognize faces? Neurologists asking such questions, or struggling to deal with brain degeneration caused by Parkinson's and Alzheimer's, might get some insight from detailed observations of the brain's circuitry over time. But so far, such information has been hard to come by. Now researchers at Harvard have shown that they can track brain activity, at the level of individual neurons, for months at a time, using a tiny electronic mesh that can be injected directly into the brain. A group led by Charles Lieber, a chemistry professor at Harvard, reports in this week's Nature Methods that they were able to record the neural activity of mice over eight months, long enough to see how the animals' brains changed as they entered the mouse version of middle age.
Ian Burkhart has been a cyborg for two years now. In 2014, scientists at Ohio State's Neurological Institute implanted a pea-sized microchip into the 24-year-old quadriplegic's motor cortex. Its goal: to bypass his damaged spinal cord and, with the help of a signal decoder and electrode-packed sleeve, control his right arm with his thoughts. Neuroengineers have been developing these so-called brain-computer interfaces for more than a decade. They've used readings from brain implants to help paralyzed patients play Pong on computer screens and control robotic arms.