Since Dolly the sheep was first cloned, humans have had a love-hate relationship with machines. Ok, maybe even before we asked a machine to make a living thing. In a variety of industries, machine learning systems, AI, and robotics are taking on the routine, mundane tasks once reserved for humans. But they’re doing this not to take away from humans, but to give them an opportunity to operate at a higher, creative level.
So, when you’re modelling the mind using Machine Learning and Computational Data in Neuroscience
for mind blowing breakthroughs, we sit up and pay attention.
When it comes neuroscience, the benefits far outweigh the pitfalls. Just ask the researchers in China, who’ve developed a way to spot whether or not a child has autism from imaging the back of their eye
. Other neurological orders such as dementia and Alzheimer’s falls under the computational neuroscience spectrum as well.
From the 1970s to today, computational biology, using analytical, mathematical modelling, and simulation techniques to study behavioral and biological systems has evolved into a variety of subgenres. And it's within these subgenres we get a sneak peek into the mind of man that creates computers that can understand the mind of man. Can you wrap your head around it?
Engineering the Mind – Mathematical Relationships
The Life Sciences, Biostatistics, and Computational Biology all play a role in physical and mental health care. In seeking to understand the makings of the human mind, to study its syntactic rules, and to help explain how we think, human and machine have come together again. This time in the form of Computational Psychiatry.
It’s here we realize our computational theories have often mirrored what we hoped to accomplish in building computers that could think with reason and logic. By understanding how we think, how the brain performs, and how it solves problems, can also help us to identify what we see as abnormalities of the mind – autism, schizophrenia, Alzheimer’s, dementia, and Parkinson’s disease just to name a few.
At its heart, the fundamental message
is that the brain’s way solving of inferred problems can be useful in determining hypotheses around neurological disorders.
Even within these subgenres there are varying degrees of theoretical concepts and with the data Computational Biologists and Computational Psychiatrists are able to conduct to navigate the complex inner workings of the brain. But much like the gathering, collecting, and analyzing of the data for the pandemic, the same can be done for in the mental health arena.
Not the least of these theorems newly determined comes from a new theoretical model
in the journal Medical Hypotheses. In it, T.A. Meridian McDonald, PhD, a research instructor in Neurology at Vanderbilt University Medical Center describes the positive traits of autism.
These positive traits she puts forth include but are not limited to increased attention, increased memory, increase differences in sensory and perception.
Building Computational Relationships
Building relationships between neurobiology, environment, and mental signals in computational terms provides a cognitive model to understand the current state of one’s environment.
It’s this building of relationships upon which human minds and the inner workings of the machine come together for the common good. There are positives in the negative. Mindset shifts aren’t just for learning how to work online or be more mindful, but are how best to present, and put your best foot forward.
If you’re interested in Life Science Analytics, Computational Biology, Decision Science, Machine Learning, or Robotics just to name a few, Harnham may have a role for you. Check out our current vacancies
or contact one of our expert consultants to learn more.
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