Scientists in California tried to study Alzheimer’s disease from a different perspective and the results may have led them to the cause of the disease.
Researchers at the University of California-Riverside (UCR) recently published results from a study that looked at a protein called tau. By studying the different forms tau proteins take, researchers discovered the difference between people who developed dementia and those who didn’t.
The tau protein was critical for researchers because they wanted to understand what the protein could reveal about the mechanism behind plaques and tangles, two critical indicators doctors look for when diagnosing people with Alzheimer’s.
Researchers of Sechenov University and University of Pittsburgh described the most promising strategies in applying genetic engineering for studying and treating Parkinson’s disease. This method can help evaluate the role of various cellular processes in pathology progression, develop new drugs and therapies, and estimate their efficacy using animal disease models. The study was published in Free Radical Biology and Medicine.
Parkinson’s disease is a neurodegenerative disorder accompanied by a wide array of motor and cognitive impairments. It develops mostly among elderly people (after the age of 55–60). Parkinson’s symptoms usually begin gradually and get worse over time. As the disease progresses, people may have difficulty controlling their movements, walking and talking and, more importantly, taking care of themselves. Although there is no cure for Parkinson’s disease, medicines, surgical treatment, and other therapies can often relieve some symptoms.
The disease is characterized by significant (up to 50–70%) loss of dopaminergic neurons, i.e. nerve cells that synthesize neurotransmitter dopamine which enables communication between the neurons. Another hallmark is the presence of Lewy bodies — oligomeric deposits of a protein called alpha-synuclein inside the neurons.
New BCI improves mental functioning, cognitive control, and relieves anxiety!
Hey it’s Han from WrySci HX presenting you with 5 awesome brain computer interface developments over the past year. Truly amazing stuff by all the researchers and am excited for what’s in store for the future. More below ↓↓↓
Summary: Findings shed new light on how brain states are regulated and how the brain can switch between them.
Source: University of Oregon
Even when at rest, the brain is never truly quiet.
New research in mice sheds light on the seemingly random brain signals that hum in the background of brains. These signals might help the brain switch between states of inattention or disengagement and states of optimal performance, UO researchers reported Oct. 14 in the journal Neuron.
James Somers writes about researchers in the fields of neuroscience and A.I. pursuing age-old questions about the nature of thoughts—and learning how to read them.
The current talk addresses a crucial problem on how compositionality can be naturally developed in cognitive agents by having iterative sensory-motor interactions with the environment.
The talk highlights a dynamic neural network model, so-called the multiple timescales recurrent neural network (MTRNN) model, which has been applied to a set of experiments on developmental learning of compositional actions performed by a humanoid robot made by Sony. The experimental results showed that a set of reusable behavior primitives were developed in the lower level network that is characterized by its fast timescale dynamics while sequential combinations of these primitives were learned in the higher level, which is characterized by its slow timescale dynamics.
This result suggests that adequate functional hierarchy necessary of generating compositional actions can be developed by utilizing timescale differences imposed at different levels of the network. The talk will also introduce our recent results on applications of an extended model of MTRNN to the problem of learning to recognize dynamic visual patterns on a pixel level. The experimental results indicated that dynamic visual images of compositional human actions can be recognized by self-organizing functional hierarchy when both spatial and temporal constraints are adequately imposed on the network activity. The dynamical systems’ mechanisms for development of the higher-order cognition will be discussed upon reviewing the aforementioned research results.
Jun Tani — Professor, Department of Electrical Engineering, KAIST
Prof. Jun Tani received his doctorate degree in electrical engineering from Sophia University in 1995. He worked at Sony Computer Science Lab in Tokyo as a researcher for 8 years and then started his lab as a PI in Riken Brain Science Inst. 12 years ago. He was appointed a visiting associate professor at the Univ. of Tokyo and a visiting researcher in Sony Intelligent Dynamic Lab. He moved to KAIST as a full professor in May, 2012.
He has been interested in neuro-robotics, theoretical problems in cognitive neuroscience, and complex systems. He has authored around 70 journal papers and 90 conference papers. He has been invited for his plenary talks in various international conferences including IEEE ICRA in 2005 and ICANN in 2014. He has served on editorial boards in IEEE Trans. Autonomous Mental Development, Adaptive Behavior, and Frontier in Neurorobotics.
Summary: A newly developed system dubbed Opto-vTrap can temporarily trap vesicles from being released from brain cells.
Source: Institute for Basic Science.
Controlling signal transmission and reception within the brain circuits is necessary for neuroscientists to achieve a better understanding of the brain’s functions. Communication among neuron and glial cells is mediated by various neurotransmitters being released from the vesicles through exocytosis. Thus, regulating vesicular exocytosis can be a possible strategy to control and understand brain circuits.
Our hunter-gatherer ancestors are huddled around a campfire when they suddenly hear the nearby bushes rustling. They have two options: investigate if the movement was caused by small prey such as a rabbit, or flee, assuming there was a predator such as a saber-tooth tiger. The former could lead to a nutritious meal, while the latter could ensure survival. What call do you think our ancestors would have made?
Evolution ensured the survival of those who fled the scene on the margin of safety rather than those who made the best decision by analyzing all possible scenarios. For thousands of years, humans have made snap decisions in fight-or-flight situations. In many ways, the human race learned to survive by jumping to conclusions.
“In modern context, such survival heuristics become myriad cognitive biases,” said Eric Colson, Chief Algorithms Officer at Stitch Fix. Let’s look at the most common biases or shortcut decisions that influence organizational leaders and how decision intelligence can come to their rescue.
