healthcare technology

The human brain is the most ridiculously complex computer that’s ever existed, and mapping this dense tangle of neurons, synapses and other cells is nigh on impossible. But engineers at Google and Harvard have given it the best shot yet, producing a browsable, searchable 3D map of a small section of human cerebral cortex.

 

A browsable 3D map of just one millionth of the cerebral cortex has been created using 225 million images and a whopping 1.4 petabytes of data, illustrating the immense complexity of the human brain.

 

With about 86 billion neurons connecting via 100 trillion synapses, it’s a Herculean task to figure out exactly what each of them does and how those connections form the basis of thought, emotion, memory, behavior and consciousness. Daunting as it may be, though, teams of scientists around the world are rolling up their sleeves and trying to build a wiring diagram for the human brain – a so-called “connectome.”

 

he researchers started with a sample taken from the temporal lobe of a human cerebral cortex, measuring just 1 mm3. This was stained for visual clarity, coated in resin to preserve it, and then cut into about 5,300 slices each about 30 nanometers (nm) thick. These were then imaged using a scanning electron microscope, with a resolution down to 4 nm. That created 225 million two-dimensional images, which were then stitched back together into one 3D volume.

 

Machine learning algorithms scanned the sample to identify the different cells and structures within. After a few passes by different automated systems, human eyes “proofread” some of the cells to ensure the algorithms were correctly identifying them.

 

The end result, which Google calls the H01 dataset, is one of the most comprehensive maps of the human brain ever compiled. It contains 50,000 cells and 130 million synapses, as well as smaller segments of the cells such axons, dendrites, myelin and cilia. But perhaps the most stunning statistic is that the whole thing takes up 1.4 petabytes of data – that’s more than a million gigabytes.

 

read more at https://newatlas.com/biology/google-harvard-human-brain-connectome/

 

Since the early years of artificial intelligence, scientists have dreamed of creating computers that can “see” the world. As vision plays a key role in many things we do every day, cracking the code of computer vision seemed to be one of the major steps toward developing artificial general intelligence.

 

But like many other goals in AI, computer vision has proven to be easier said than done. In the past decades, advances in machine learning and neuroscience have helped make great strides in computer vision. But we still have a long way to go before we can build AI systems that see the world as we do.

 

Biological and Computer Vision, a book by Harvard Medical University Professor Gabriel Kreiman, provides an accessible account of how humans and animals process visual data and how far we’ve come toward replicating these functions in computers.

 

Kreiman’s book helps understand the differences between biological and computer vision. The book details how billions of years of evolution have equipped us with a complicated visual processing system, and how studying it has helped inspire better computer vision algorithms.

 

Kreiman also discusses what separates contemporary computer vision systems from their biological counterpart.

Hardware differences

Biological vision is the product of millions of years of evolution. There is no reason to reinvent the wheel when developing computational models. We can learn from how biology solves vision problems and use the solutions as inspiration to build better algorithms.

 

Before being able to digitize vision, scientists had to overcome the huge hardware gap between biological and computer vision. Biological vision runs on an interconnected network of cortical cells and organic neurons. Computer vision, on the other hand, runs on electronic chips composed of transistors

 

Architecture differences

There’s a mismatch between the high-level architecture of artificial neural networks and what we know about the mammal visual cortex.

Goal differences

Several studies have shown that our visual system can dynamically tune its sensitivities to the common. Creating computer vision systems that have this kind of flexibility remains a major challenge, however.

Current computer vision systems are designed to accomplish a single task.

Integration differences

In humans and animals, vision is closely related to smell, touch, and hearing senses. The visual, auditory, somatosensory, and olfactory cortices interact and pick up cues from each other to adjust their inferences of the world. In AI systems, on the other hand, each of these things exists separately.

 

read more at https://venturebeat.com/2021/05/15/understanding-the-differences-between-biological-and-computer-vision/