Neuromorphic Computing: What Is It And Where Are We At?

For the last hundred or so years, collectively as humanity, we’ve been dreaming, thinking, writing, singing, and producing movies about a machine that could think, reason, and be intelligent in a similar way to us. The stories beginning with “Erewhon” published in 1872 by Sam Butler, Edgar Allan Poe’s “Maelzel’s Chess Player,” and the 1927 film “Metropolis” showed the idea that a machine could think and reason like a person. Not in magic or fantastical way. They drew from the automata of ancient Greece and Egypt and combined notions of philosophers such as Aristotle, Ramon Llull, Hobbes, and thousands of others.

Their notions of the human mind led them to believe that all rational thought could be expressed as algebra or logic. Later the arrival of circuits, computers, and Moore’s law led to continual speculation that human-level intelligence was just around the corner. Some have heralded it as the savior of humanity, where others portray a calamity as a second intelligent entity rises to crush the first (humans).

The flame of computerized artificial intelligence has brightly burned a few times before, such as in the 1950s, 1980s, and 2010s. Unfortunately, both prior AI booms have been followed by an “AI winter” that falls out of fashion for failing to deliver on expectations. This winter is often blamed on a lack of computer power, inadequate understanding of the brain, or hype and over-speculation. In the midst of our current AI summer, most AI researchers focus on using the steadily increasing computer power available to increase the depth of their neural nets. Despite their name, neural nets are inspired by the neurons in the brain and share only surface-level similarities.

Some researchers believe that human-level general intelligence can be achieved by simply adding more and more layers to these simplified convolutional systems fed by an ever-increasing trove of data. This point is backed up by the incredible things these networks can produce, and it gets a little better every year. However, despite what wonders deep neural nets produce, they still specialize and excel at just one thing. A superhuman Atari playing AI cannot make music or think about weather patterns without a human adding those capabilities. Furthermore, the quality of the input data dramatically impacts the quality of the net, and the ability to make an inference is limited, producing disappointing results in some domains. Some think that recurrent neural nets will never gain the sort of general intelligence and flexibility that our brains offer.

However, some researchers are trying to creating something more brainlike by, you guessed it, more closely emulates a brain. Given that we are in a golden age of computer architecture, now seems the time to create new hardware. This type of hardware is known as Neuromorphic hardware.

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DIY Neuroscience Hack Chat

Join us on Wednesday, February 24 at noon Pacific for the DIY Neuroscience Hack Chat with Timothy Marzullo!

Watch a film about a mad scientist from the golden age of Hollywood and chances are good that among the other set pieces, you’ll see human brains floating in jars of cloudy fluid wired up to electrodes and fancy machines. It’s all made up, of course, but tropes work because they’re based on a kernel of truth, and we in the audience know that our brains and the other parts of our nervous system do indeed work on electricity. Or more precisely, excitable tissues in our nervous systems pass electrochemical signals between themselves as waves of potential across cell membranes.

Studying this electrical world locked away inside our heads is a challenging, but by no means impossible, pursuit. Usable signals can be picked up, amplified, digitized, and recorded to help us understand what’s going on when we think, feel, move, sleep, wake, or just be. Neuroscience has made tremendous strides looking at these signals, but the equipment to do so has largely remained the province of large universities and teaching hospitals with ample budgets, leaving the amateur neuroscientist out of luck.

Tim Marzullo, co-founder of Backyard Brains, is looking to change all that. While working on his Ph.D. in neuroscience at the University of Michigan, he and Greg Gage looked for ways to make the tools of neuroscience research affordable to everyone. The result is the Neuron SpikerBox, a low-cost bioamplifier that can tap into the “spikes” of action potential in live neurons. Open-source tools like these have helped educators bring neuroscience experiments to STEM students, and even helped other scientists set up novel, low-cost experiments.

Tim will join us on the Hack Chat to talk about doing DIY neuroscience and designing the instruments that make it possible. Bring your “mad scientist” questions as we push back the veil of ignorance on how our brains work, one neuron at a time.

join-hack-chatOur Hack Chats are live community events in the Hackaday.io Hack Chat group messaging. This week we’ll be sitting down on Wednesday, February 24 at 12:00 PM Pacific time (UTC-8). If time zones have you tied up, we have a handy time zone converter.

Click that speech bubble to the right, and you’ll be taken directly to the Hack Chat group on Hackaday.io. You don’t have to wait until Wednesday; join whenever you want and you can see what the community is talking about.

 

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Open-Source Neuroscience Hardware Hack Chat

Join us on Wednesday, February 19 at noon Pacific for the Open-Source Neuroscience Hardware Hack Chat with Dr. Alexxai Kravitz and Dr. Mark Laubach!

There was a time when our planet still held mysteries, and pith-helmeted or fur-wrapped explorers could sally forth and boldly explore strange places for what they were convinced was the first time. But with every mountain climbed, every depth plunged, and every desert crossed, fewer and fewer places remained to be explored, until today there’s really nothing left to discover.

Unless, of course, you look inward to the most wonderfully complex structure ever found: the brain. In humans, the 86 billion neurons contained within our skulls make trillions of connections with each other, weaving the unfathomably intricate pattern of electrochemical circuits that make you, you. Wonders abound there, and anyone seeing something new in the space between our ears really is laying eyes on it for the first time.

But the brain is a difficult place to explore, and specialized tools are needed to learn its secrets. Lex Kravitz, from Washington University, and Mark Laubach, from American University, are neuroscientists who’ve learned that sometimes you have to invent the tools of the trade on the fly. While exploring topics as wide-ranging as obesity, addiction, executive control, and decision making, they’ve come up with everything from simple jigs for brain sectioning to full feeding systems for rodent cages. They incorporate microcontrollers, IoT, and tons of 3D-printing to build what they need to get the job done, and they share these designs on OpenBehavior, a collaborative space for the open-source neuroscience community.

Join us for the Open-Source Neuroscience Hardware Hack Chat this week where we’ll discuss the exploration of the real final frontier, and find out what it takes to invent the tools before you get to use them.

join-hack-chatOur Hack Chats are live community events in the Hackaday.io Hack Chat group messaging. This week we’ll be sitting down on Wednesday, February 19 at 12:00 PM Pacific time. If time zones have got you down, we have a handy time zone converter.

Click that speech bubble to the right, and you’ll be taken directly to the Hack Chat group on Hackaday.io. You don’t have to wait until Wednesday; join whenever you want and you can see what the community is talking about. Continue reading “Open-Source Neuroscience Hardware Hack Chat”

Reverse-Engineering Brains, One Neuron At A Time

Most posts here are electrical or mechanical, with a few scattered hacks from other fields. Those who also keep up with advances in biomedical research may have noticed certain areas are starting to parallel the electronics we know. [Dr. Rajib Shubert] is in one such field, and picked up on the commonality as well. He thought it’d be interesting to bridge the two worlds by explaining his research using analogies familiar to the Hackaday audience. (Video also embedded below.)

He laid the foundation with a little background, establishing that we’ve been able to see individual static neurons for a while via microscope slides and such, and we’ve been able to see activity of the whole living brain via functional MRI. These methods gradually improved our understanding of neurons, and advances within the past few years have reached an intersection of those two points: [Dr. Shubert] and colleagues now have tools to peer inside a functional brain, teasing out how it works one neuron at a time.

[Dr. Shubert]’s talk makes analogies to electronics hardware, but we can also make a software analogy treating the brain as a highly optimized (and/or obfuscated) piece of code. Virus stamping a single cell under this analogy is like isolating a single function, seeing who calls it, and who it calls. This pairs well with optogenetics techniques, which can be seen as like modifying a function to see how it affects results in real time. It certainly puts a different meaning on the phrase “working with live code”!

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Hackaday Prize Entry: Grasshopper Neurons

A plague of locusts descends on your garden, and suddenly you realize grasshoppers are very hard to catch. Grasshoppers are nature’s perfect collision avoidance system, and this is due to a unique visual system that includes neurons extending directly from the eye to the animal’s legs. For this Hackaday Prize entry – and as a research project for this summer at Backyard Brains, [Dieu My Nguyen] is studying the neuroscience of grasshopper vision with stabs and shocks.

We visited Backyard Brains about two years ago, and found three very interesting projects. The first was a project on optogenetics, or rewiring neurons so flies taste something sweet when they’re exposed to red light. The second was remote-controlled cockroaches. Number three will shock you: a device that allowed me to expand my megalomania by shocking people with the power of my mind. It’s not all fun and games, though. This grasshopper neuron probe will use the Backyard Brains SpikerBox to investigate when those neurons are activated in response to a stimuli.

The utility of looking at the common grasshopper to learn about collision and object avoidance may not be very apparent at first. The more you learn about neuroscience, the more apparent the biological connection to common computer vision tasks becomes. That makes this a great research project and an excellent entry into the Hackaday Prize.

Mouse Brain with neurons exhibiting GFP expression

UC Davis Researchers Use Light To Erase Memories In Genetically Altered Mice

Much like using UV light to erase data from an EPROM, researchers from UC Davis have used light to erase specific memories in mice. [Kazumasa Tanaka, Brian Wiltgen and colleagues] used optogenetic techniques to test current ideas about memory retrieval. Optogenetics has been featured on Hackaday before. It is the use of light to control specific neurons (nerve cells) that have been genetically sensitized to light.  By doing so, the effects can be seen in real-time.

For their research, [Kazumasa Tanaka, Brian Wiltgen and colleagues] created genetically altered mice whose activated neurons expressed GFP, a protein that fluoresces green. This allowed neurons to be easily located and track which ones responded to learning and memory stimuli. The neurons produced an additional protein that made it possible to “switch them off” in response to light.  This enabled the researchers to determine which specific neurons are involved in the learning and memory pathways as well as study the behavior of the mouse when certain neurons were active or not.

Animal lovers may want to refrain from the following paragraph. The mice were subjected to mild electric shocks after being placed in a cage. They were trained so that when they were put in the cage again, they remembered the previous shock and would freeze in fear. However, when specific neurons in the hippocampus (a structure in the brain) were exposed to light transmitted through fiber optics (likely through a hole in each mouse’s skull), the mice happily scampered around the cage, no memory of the earlier shock to terrify them. The neurons that stored the memory of the shock had been “turned off” after the light exposure.

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