How Artificial Intelligence is Matching Drugs to Patients

Considerable direction, following.  

How artificial intelligence is matching drugs to patients  in the BBC

Published,2 days ago, By Natalie Lisbona, Business reporter, Tel Aviv

Dr Talia Cohen Solal sits down at a microscope to look closely at human brain cells grown in a petri dish.   "The brain is very subtle, complex and beautiful," she says.

A neuroscientist, Dr Cohen Solal is the co-founder and chief executive of Israeli health-tech firm Genetika+.  Established in 2018, the company says its technology can best match antidepressants to patients, to avoid unwanted side effects, and make sure that the prescribed drug works as well as possible.

"We can characterise the right medication for each patient the first time," adds Dr Cohen Solal.

Genetika+ does this by combining the latest in stem cell technology - the growing of specific human cells - with artificial intelligence (AI) software.  From a patient's blood sample its technicians can generate brain cells. These are then exposed to several antidepressants, and recorded for cellular changes called "biomarkers".

This information, taken with a patient's medical history and genetic data, is then processed by an AI system to determine the best drug for a doctor to prescribe and the dosage.

Although the technology is currently still in the development stage, Tel Aviv-based Genetika+ intends to launch commercially next year.  .... '

Large Language Models: A Cognitive and Neuroscience Perspective

Irving does an excellent review and links to much work about LLMs, Large Language Models, and oter topics that are now much in the news. Below an intro. I plan to read all the articles pointed to at the link.  A considerable weakness in the current directions?   Implications to all this,  will provide.

A collection of observations, news and resources on the changing nature of innovation, technology, leadership, and other subjects.  By Irving Wladawsky-Berger  March 23, 2023

Large Language Models: A Cognitive and Neuroscience Perspective

Over the past few decades, powerful AI systems have matched or surpassed human levels of performance in a number of tasks such as image and speech recognition, skin cancer classification, breast cancer detection, and highly complex games like Go. These AI breakthroughs have been based on increasingly powerful and inexpensive computing technologies, innovative deep learning (DL) algorithms, and huge amounts of data on almost any subject. More recently, the advent of large language models (LLMs) are taking AI to the next level. And, for many technologists like me, LLMs and their associated chatbots have introduced us to the fascinating world of human language and cognition.

I recently learned the difference between form, communicative intent, meaning, and understanding from “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data,” a 2020 paper by linguistic professors Emiliy Bender and Alexander Koller. These linguistic concepts helped me understand the authors’ argument that “in contrast to some current hype, meaning cannot be learned from form alone. This means that even large language models such as BERT do not learn meaning; they learn some reflection of meaning into the linguistic form which is very useful in applications.”

A few weeks ago, I came across another interesting paper, “Dissociating Language and Thought in Large Language Models: a Cognitive Perspective,” published in January, 2023 by principal authors linguist Kyle Mahowald and cognitive neuroscientist Anna Ivanova and four additional co-authors. The paper nicely explains how the study of human language, cognition and neuroscience sheds light on the potential capabilities of LLMs and chatbots. Let me briefly discuss what I learned.

“Today’s large language models (LLMs) routinely generate coherent, grammatical and seemingly meaningful paragraphs of text,” said the paper’s abstract. “This achievement has led to speculation that these networks are — or will soon become — thinking machines, capable of performing tasks that require abstract knowledge and reasoning. “Here, we review the capabilities of LLMs by considering their performance on two different aspects of language use: formal linguistic competence, which includes knowledge of rules and patterns of a given language, and functional linguistic competence, a host of cognitive abilities required for language understanding and use in the real world.

The authors point out that there’s a tight relationship between language and thought in humans. When we hear or read a sentence, we typically assume that it was produced by a rational person based on their real world knowledge, critical thinking, and reasoning abilities. We generally view other people’s statements not just as a reflection of their linguistic skills, but as a window into their mind. .... '

Detailing Neural Activity

Rare Electrical Recordings of Human Brain Detail Neural Activity

By New York University, October 5, 2022

An international team of neuroscientists used medical data to record human neural activity of visual processing in exceptional detail.

The researchers examined volunteer epilepsy patients who had been implanted with electrodes in order to measure seizure-associated neural activity. Recordings made as patients watched pictures on a laptop showed computational models that were designed to explain neural responses in non-human primates are applicable to human brains. The results indicate that the models can accurately predict changes in human neural activity for various changes in a visually presented image, according to the team's published report.

"We found that both human and animal brains seem to be using a similar 'toolkit' of neural calculations to make sense of the continuous stream of inputs arriving from our senses," says Iris Groen, an assistant professor at the University of Amsterdam, the Netherlands. ... 

A computer model can predict rapid fluctuations in neural activity in the human visual cortex. ... 

From New York University

View Full Article     

Building a Brain Atlas

New effort moves forward: 

NIH’s BRAIN Initiative puts $500 million into creating most detailed ever human brain atlas

Neuroscientists will build on census of mouse brain as massive program moves into new phase

22 SEP 202210:00 AM BYJOCELYN KAISER

The BRAIN Initiative, the 9-year-old, multibillion-dollar U.S. neuroscience effort, today announced its most ambitious challenge yet: compiling the world’s most comprehensive map of cells in the human brain. Scientists say the BRAIN Initiative Cell Atlas Network (BICAN), funded with $500 million over 5 years, will help them understand how the human brain works and how diseases affect it. BICAN “will transform the way we do neuroscience research for generations to come,” says BRAIN Initiative Director John Ngai of the National Institutes of Health (NIH).

BRAIN, or Brain Research Through Advancing Innovative Neurotechnologies, was launched by then-President Barack Obama in 2013. It began with a focus on tools, then developed a program called the BRAIN Initiative Cell Census Network, resulting in a raft of papers in 2021. The studies combined data on the genetic features, shapes, locations, and electrical activity of millions of cells to identify more than 100 cell types across the primary motor cortex—which coordinates movement—in mice, marmosets, and humans. Hundreds of researchers involved in the network are now completing a cell census for the rest of the mouse brain. It is expected to become a widely used, free resource for the neuroscience community.

Now, BICAN will characterize and map neural and nonneuronal cells across the entire human brain, which has 200 billion cells and is 1000 times larger than a mouse brain. “It’s using similar approaches but scaling up,” says Hongkui Zeng, director of the Allen Institute for Brain Science, which won one-third of the BICAN funding. Zeng says the results of the effort will serve as a reference—a kind of Human Genome Project for neuroscience.

Other groups will add data from human brains across a range of ancestries and ages, including fetal development. “We will try to cover the breadth of human development and aging,” says Joseph Ecker of the Salk Institute for Biological Studies, which leads BICAN studies of epigenetics, the study of heritable changes that are passed on without changes to the DNA. Ngai expects BICAN to study several hundred human brains overall, although investigators are just starting to work out details. “The sampling and coverage is going to be a big, big topic of discussion,” Ngai says. ... 

Algorithm Could Help Enable Next-Generation Deep Brain Stimulation

New means to stimulate brains.  As I read it, this may require quite a bit of regulation. 

Algorithm Could Help Enable Next-Generation Deep Brain Stimulation Devices

By News from Brown University, June 8, 2021

Brown University bioengineers have developed a new algorithm that could clear a path to more adaptive deep brain stimulation (DBS) technology.  The algorithm helps DBS systems more easily detect brain signals while concurrently delivering stimulation, by identifying and eliminating electrical artifacts.

The Brown team was able to use the algorithm to stitch fragments of low-resolution data into a high-resolution picture of an artifact waveform, which outperformed other approaches in distinguishing brain signals from artifacts in laboratory experiments and computer simulations.  Brown's Nicole Provenza said this differentiation holds up even when the signal of interest is very similar to simulation artifacts.

The researchers also said the algorithm is computationally inexpensive, which suggests the possibility of real-time artifact-filtering, and simultaneous recording and stimulation.

  (full article) 

Google Reconstructs the Human Cortex

Quite remarkable.  Impressive  build. Images at the link.  A way to connect brain and computer.  Browsing is a way to learn about structure, now take it out to function.    See the Neuroglancer Browser Interface.

A Browsable Petascale Reconstruction of the Human Cortex,  Tuesday, June 1, 2021

Posted by Tim Blakely, Software Engineer and Michał Januszewski, Research Scientist, Connectomics at Google

In January 2020 we released the fly “hemibrain” connectome — an online database providing the morphological structure and synaptic connectivity of roughly half of the brain of a fruit fly (Drosophila melanogaster). This database and its supporting visualization has reframed the way that neural circuits are studied and understood in the fly brain. While the fruit fly brain is small enough to attain a relatively complete map using modern mapping techniques, the insights gained are, at best, only partially informative to understanding the most interesting object in neuroscience — the human brain.

Today, in collaboration with the Lichtman Laboratory at Harvard University, we are releasing the “H01” dataset, a 1.4 petabyte rendering of a small sample of human brain tissue, along with a companion paper, “A connectomic study of a petascale fragment of human cerebral cortex.” The H01 sample was imaged at 4nm-resolution by serial section electron microscopy, reconstructed and annotated by automated computational techniques, and analyzed for preliminary insights into the structure of the human cortex. The dataset comprises imaging data that covers roughly one cubic millimeter of brain tissue, and includes tens of thousands of reconstructed neurons, millions of neuron fragments, 130 million annotated synapses, 104 proofread cells, and many additional subcellular annotations and structures — all easily accessible with the Neuroglancer browser interface. H01 is thus far the largest sample of brain tissue imaged and reconstructed in this level of detail, in any species, and the first large-scale study of synaptic connectivity in the human cortex that spans multiple cell types across all layers of the cortex. The primary goals of this project are to produce a novel resource for studying the human brain and to improve and scale the underlying connectomics technologies.  ... ' 

Quanta Magazine: Brain Rotates Memories to Save Them

 Made me think of how this kind of structure could be used in machine learning to perform a kind of  maintenance and change managemebt as new data is acquired.  Even lead to creativity based on current knowledge. 

The Brain ‘Rotates’ Memories to Save Them From New Sensations

Jordana Cepelewicz   Quanta Mag Staff Writer

Some populations of neurons simultaneously process sensations and memories. New work shows how the brain rotates those representations to prevent interference. 

During every waking moment, we humans and other animals have to balance on the edge of our awareness of past and present. We must absorb new sensory information about the world around us while holding on to short-term memories of earlier observations or events. Our ability to make sense of our surroundings, to learn, to act and to think all depend on constant, nimble interactions between perception and memory.

But to accomplish this, the brain has to keep the two distinct; otherwise, incoming data streams could interfere with representations of previous stimuli and cause us to overwrite or misinterpret important contextual information. Compounding that challenge, a body of research hints that the brain does not neatly partition short-term memory function exclusively into higher cognitive areas like the prefrontal cortex. Instead, the sensory regions and other lower cortical centers that detect and represent experiences may also encode and store memories of them. And yet those memories can’t be allowed to intrude on our perception of the present, or to be randomly rewritten by new experiences.

A paper published recently in Nature Neuroscience may finally explain how the brain’s protective buffer works.  https://www.nature.com/articles/s41593-021-00821-9   .... ' 

Measuring Consciousness

 More brain research addresses complexities of intelligence.

Researchers Find Better Way to Measure Consciousness

University of Wisconsin-Madison News, Chris Barncard, March 16, 2021

Analysis of neural signals in monkeys by University of Wisconsin–Madison (UWM) researchers combined traditional telltales of consciousness with computational metrics describing the signals' complexities and interaction in different brain regions. The authors used machine learning to determine whether the monkeys were conscious or not and the activity levels of their brain areas by processing those signals through a computer. UWM's Mohsen Afrasiabi said the results indicated the back of the brain and the deep brain areas are more predictive of states of consciousness than the front. UWM's Yuri Saalmann said, "We could use what we've learned to optimize electrical patterns through precise brain stimulation and help people who are, say, in a coma maintain a continuous level of consciousness."

Talk Tonight: A Thousand Brains: Jeff Hawkins

  Late to this, but of interest, talked to Jeff in our early AI exploration days.

Neuroscientist and engineer Jeff Hawkins unveils a new biological theory of intelligence that Richard Dawkins calls "exhilarating.".  Presented by Kepler's Literary Foundation.

About this Event, registration.  Registration required. 

Date And Time  Wed, March 10, 2021   9:00 PM – 10:00 PM EST  Online

In a book that biologist Richard Dawkins calls “exhilarating,” author, neuroscientist and engineer Jeff Hawkins unveils a new theory of intelligence with awe-inspiring implications. Get ready for a dynamic and exciting conversation about the brain with Kepler’s.

In a century rife with neuroscientific and biological advances, researchers have made little progress on one very big question: how do the simple cells of the brain create intelligence?

Hawkins, cofounder of the neuroscience research company Numenta, dares to answer with A Thousand Brains. In a compulsively readable book accessible even to the casual science reader, he lays forth a simple and yet mold-breaking theory: that intelligence arises from the interaction of maplike reference frames in the brain, which build hundreds of thousands of interconnected models of everything a person knows. These maplike reference frames can tell you how to achieve goals, how to get from one place to another, who you are and how you’re connected to the world.

Hawkins and his Silicon Valley based research team have studied the neocortex, the part of the brain we associate with everything responsible for intelligence. Now with his “thousand brains” theory of the structure that runs the show, Hawkins proposes answers to some of neurosciences most stubborn questions—including questions about the very nature of consciousness, false beliefs and more.

On March 10th, join us for an online conversation between Jeff Hawkins and award-winning science journalist Anil Ananthaswamy as they share with us A Thousand Brains. Starting with basic information on how the brain works for anyone to understand, they’ll discuss Jeff’s new theory and explore what it could mean not only for advancements in machine intelligence, but also its broader implications for all of us as people. This will be a smart, fun night celebrating a key moment in our understanding of the human brain: don’t miss it!

**Please consider joining with a book or donation to support the production of this event and make it possible for us to continue bringing you great conversations.

Registration will close one hour before the event; please reserve your spot early to guarantee access, as registrations are limited.**   .... 

Brain Background Noise Useful

Recall this being brought up in some work we did in understanding reactions to stimuli.    We theorized that it was always useful to calculate the background, where we later discovered some 'data' that ended up being useful.    Still think its useful to bring along the metadata of background noise.   This is not the same thing, more in the realm of neuro, and only some of our data was neuro.   Nice to see this,  makes me think.

The Brain’s ‘Background Noise’ May Be Meaningful After All

By digging out signals hidden within the brain’s electrical chatter, scientists are getting new insights into sleep, aging, and more.

AT A SLEEP research symposium in January 2020, Janna Lendner presented findings that hint at a way to look at people’s brain activity for signs of the boundary between wakefulness and unconsciousness. For patients who are comatose or under anesthesia, it can be all-important that physicians make that distinction correctly. Doing so is trickier than it might sound, however, because when someone is in the dreaming state of rapid-eye movement (REM) sleep, their brain produces the same familiar, smoothly oscillating brain waves as when they are awake.  ... " 

Replaying Memory to Retain it

 Fascinating play.   Remember this similarity being brought up in a talk about short vs long term memories.   Now being brought into play?

How a Memory Quirk of the Human Brain Can Galvanize AI  By Shelly Fan in SingularityHub

Even as toddlers we’re good at inferences. Take a two-year-old that first learns to recognize a dog and a cat at home, then a horse and a sheep in a petting zoo. The kid will then also be able to tell apart a dog and a sheep, even if he can’t yet articulate their differences.

This ability comes so naturally to us it belies the complexity of the brain’s data-crunching processes under the hood. To make the logical leap, the child first needs to remember distinctions between his family pets. When confronted with new categories—farm animals—his neural circuits call upon those past remembrances, and seamlessly incorporate those memories with new learnings to update his mental model of the world.

Not so simple, eh?

It’s perhaps not surprising that even state-of-the-art machine learning algorithms struggle with this type of continuous learning. Part of the reason is how these algorithms are set up and trained. An artificial neural network learns by adjusting synaptic weights—how strongly one artificial neuron connects to another—which in turn leads to a sort of “memory” of its learnings that’s embedded into the weights. Because retraining the neural network on another task disrupts those weights, the AI is essentially forced to “forget” its previous knowledge as a prerequisite to learn something new. Imagine gluing together a bridge made out of toothpicks, only having to rip apart the glue to build a skyscraper with the same material. The hardware is the same, but the memory of the bridge is now lost.

This Achilles’ heel is so detrimental it’s dubbed “catastrophic forgetting.” An algorithm that isn’t capable of retaining its previous memories is severely kneecapped in its ability to infer or generalize. It’s hardly what we consider intelligent.

But here’s the thing: if the human brain can do it, nature has already figured out a solution. Why not try it on AI?

A recent study by researchers at the University of Massachusetts Amherst   and the Baylor College of Medicine did just that. Drawing inspiration from the mechanics of human memory, the team turbo-charged their algorithm with a powerful capability called “memory replay”—a sort of “rehearsal” of experiences in the brain that cements new learnings into long-lived memories.     ..." 

Nanalyze on Brain Computer Interfaces (BCI)

Had forgotten about Nanalyze, worth a look: 

After I posted about it, they sent me this on Brain Computer interfaces:

Who we are We?     
We are a boutique media and research firm. Our research method involves sifting through large amounts of information in the public domain published by experts, then using our regular meetings and interviews with founders and industry participants to validate and boost our findings. ... 

This year we learned about a mind-blowing advance in brain-computer interface (BCI) technology for recording and interpreting neural signals. Founded by a wealthy and outsized personality, the West Coast startup has raised more than $100 million to advance the boundaries of neuroscience, augment human memory, and perhaps meld mind and machine in mind-bending ways. You know, modest goals like the rest of us. And, no, we’re not talking about painting lipstick on a pig in the way that Elon Musk is doing at Neuralink, a BCI startup that recently demonstrated its hardware hardwired into a poor little piggy. This story is about Kernel, a startup founded by Bryan Johnson that broke stealth mode (again) earlier this year with new mind-reading hardware powered by machine learning. It also unveiled its business model: Neuroscience as a Service (NaaS).

What is Brain-Computer Interface Technology?
Commercial BCI technology falls under the broader category of what’s become known as neurotech, which also includes applications like brain diagnostics and therapeutics, such as neuromodulation and electroceuticals. .... "   (More at the link above)

More Elon Musk on Brain Implants

Some more detailed comments on the Neuralink talk given by Elon Musk last week.  And a link to the talk.  Was an area we looked at carefully, especially how it linked to neuromarketing engagement.

Humans and technology/Brain-computer interface
Elon Musk’s Neuralink is neuroscience theater
Elon Musk’s livestreamed brain implant event made promises that will be hard to keep.  by Antonio  Regaldo in TechnologyReview

Humans and technology/Brain-computer interface
Elon Musk’s Neuralink is neuroscience theater
Elon Musk’s livestreamed brain implant event made promises that will be hard to keep.

Rock-climb without fear. Play a symphony in your head. See radar with superhuman vision. Discover the nature of consciousness. Cure blindness, paralysis, deafness, and mental illness. Those are just a few of the applications that Elon Musk and employees at his four-year-old neuroscience company Neuralink believe electronic brain-computer interfaces will one day bring about.

None of these advances are close at hand , and some are unlikely to ever come about. But in a “product update” streamed over YouTube    on Friday, Musk, also the founder of SpaceX and Tesla Motors, joined staffers wearing black masks to discuss the company’s work toward an affordable, reliable brain implant that Musk believes billions of consumers will clamor for in the future. ... "

Engineers 3D Print Brain Implants

Considerably more at the link.

Engineers 3D-Print Soft, Rubbery Brain Implants
MIT News
Jennifer Chu
March 30, 2020

Engineers at the Massachusetts Institute of Technology (MIT) are developing soft, flexible neural implants that can conform to the brain's contours without irritating the surrounding tissue. The team turned a polymer solution that is normally liquid-like into a more viscous substance that can be fed through a three-dimensional printer to create stable, electrically conductive patterns. These flexible electronics could replace metal-based electrodes used to monitor brain activity and may be useful in brain implants that stimulate neural regions to treat epilepsy, Parkinson's disease, and severe depression. Said MIT's Hyunwoo Yuk, "This process may replace or supplement lithography techniques, as a simpler and cheaper way to make a variety of neurological devices, on demand." ... "  ... 

Improving Neural Models

So many groups working on how to make such models smaller and faster.  Not different from the very long hunt for faster models to solve optimization problems over the years.   Discussion is technical.

A Tool to Simplify Complex Neuron Models
EPFL News (Switzerland)
January 15, 2020

Researchers at the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and Israel's Hebrew University of Jerusalem have developed a computational tool to streamline complex neuron models of any type of cell, while retaining their input/output properties and accelerating the run-times of cell simulations. The Neuron_Reduce tool maps a dendritic computation tree into a simpler multi-cylindrical tree, mapping synapses and ion channels into the reduced model to preserve their transfer impedance to the cell body. EPFL's Pramod Kumbhar said, "Neuron_Reduce ... opens the path for a novel type of reduced models that crucially maintain important details of the model but possibly run 40 to 250 times faster."

Future of Computer Tech?

Points again to the 'Brain as a computing model' conundrum.   We have lots of brains we can study, observe, dissect.   But we still don't know their operational specs.   Algorithms 'work', because their input and output are simple.   Brains are not.    But its good we can get closer to the biomimicry of the brain for any hope of replicating their approach.

Brain-Like Device May Forecast Future of Computer Technology
By The Daily Bruin,  January 14, 2020

Researchers at UCLA and Japan's National Institute for Materials Science developed a device with the ability to mimic certain characteristics of the brain. The work is described in "Emergent Dynamics of Neuromorphic Nanowire Networks," published in Scientific Reports.

James Gimzewski, a chemistry professor at UCLA, and Adam Stieg, associate director of the California NanoSystems Institute at UCLA, helped create the device, which spans 10 square millimeters.

The device's small size is possible because of the number of networks within the device, all of which are made from silver nanowires, which have an average diameter of 360 nanometers.

"Because [the networks] self-assemble on the nanoscale, we can achieve a very high density of synaptic-like connections that wouldn't be achievable using normal computer chip technology," Gimzewski says.

Kelsey Scharnhorst, who assisted in research for this project, says the growing popularity of machine learning has led to a scramble to develop an option that can produce computational outputs in a timely, more-energy-efficient fashion.

"Instead of doing everything with algorithms . . . [computations] can be sped up an insane amount [with machine learning], and that can make a huge difference for a ton of technology," Scharnhorst says.

From The Daily Bruin  Full article at the link.

Difference Between Human Perception and Neural Nets

A long time question,  these are 'artificial' neural nets. They are only very, very crudely modeled after the networks in our brain.     We had this explained to us, at least to the extent it is understood by an expert neuroscientist.   So what is the difference?  And to what degree does it make a difference to our AI aims?

Differences between deep neural networks and human perception
Stimuli that sound or look like gibberish to humans are indistinguishable from naturalistic stimuli to deep networks.

Kenneth I. Blum | MIT Center for Brains, Minds and Machines

When your mother calls your name, you know it’s her voice — no matter the volume, even over a poor cell phone connection. And when you see her face, you know it’s hers — if she is far away, if the lighting is poor, or if you are on a bad FaceTime call. This robustness to variation is a hallmark of human perception. On the other hand, we are susceptible to illusions: We might fail to distinguish between sounds or images that are, in fact, different. Scientists have explained many of these illusions, but we lack a full understanding of the invariances in our auditory and visual systems.

Deep neural networks also have performed speech recognition and image classification tasks with impressive robustness to variations in the auditory or visual stimuli. But are the invariances learned by these models similar to the invariances learned by human perceptual systems? A group of MIT researchers has discovered that they are different. They presented their findings yesterday at the 2019 Conference on Neural Information Processing Systems.

The researchers made a novel generalization of a classical concept: “metamers” — physically distinct stimuli that generate the same perceptual effect. The most famous examples of metamer stimuli arise because most people have three different types of cones in their retinae, which are responsible for color vision. The perceived color of any single wavelength of light can be matched exactly by a particular combination of three lights of different colors — for example, red, green, and blue lights. Nineteenth-century scientists inferred from this observation that humans have three different types of bright-light detectors in our eyes. This is the basis for electronic color displays on all of the screens we stare at every day. Another example in the visual system is that when we fix our gaze on an object, we may perceive surrounding visual scenes that differ at the periphery as identical. In the auditory domain, something analogous can be observed. For example, the “textural” sound of two swarms of insects might be indistinguishable, despite differing in the acoustic details that compose them, because they have similar aggregate statistical properties. In each case, the metamers provide insight into the mechanisms of perception, and constrain models of the human visual or auditory systems.  .... "

Artificial Networks Shed Light on Human Face Recognition

From what we know how the brain works to artificial neural nets (ANN), which we know are very simplistic neural models, then back to the actual operation of the brain?

Artificial Networks Shed Light on Human Face Recognition  By Weizmann Institute of Science

A pair of face images that elicited dissimilar neuronal activation patterns.
Weizmann Institute of Science researchers are gaining new insights into humans' ability to recognize faces, through the use of deep neural networks.

Researchers at the Weizmann Institute of Science in Israel have gained new insights on humans' ability to recognize faces using deep neural networks.

The researchers compared brain activity to these networks by analyzing data from 33 epileptics with implanted brain electrodes as they were shown a series of faces, each of which triggered a unique neuronal activation pattern.

The neural network was shown the same images, to determine whether it would exhibit activation patterns similar to the human brain.  There were striking similarities, especially in the network's middle layers, which represent the actual pictorial appearance of the faces.

Weizmann's Shany Grossman said, "These findings can help advance our understanding of how face perception and recognition are encoded in the human brain [and] ... may also help to further improve the performance of neural networks, by tweaking them so as to bring them closer to the observed brain response patterns."

From Weizmann Institute of Science    View Full Article

Do we Know How the Brain Works?

Have had  conversations of late with people who have said:  Look at neural nets they are modeled after brains.    But the answer is still, no we don't.  And we are still not close.   Artificial neural models are very different even from the way we think we know how biological neurons work.    Not to say the artificial models are not useful,  but its not what your brain actual does.  How much closer are we getting?  See the Neuralink approach, mentioned below, to understand the challenges.  Will we know?  I am always optimistic.

Will It Ever Be Possible to Understand the Human Brain?
Despite technical breakthroughs like Elon Musk’s Neuralink, scientists still have no reliable model of how the brain actually works

By Brian Bergstein in Medium ... 

Deciding When to Trust

Have recently been involved with the concept of  'smart contracts' and trustble agreements.    How might this idea be included in the construction of such things?  Things that we can trust in a neuroscience sense?  Is trust just a way to accurately forecast an agent's future behavior?

How do Our Brains Decide When to Trust?      By Paul J. Sak, in the HBR

Trust is the enabler of global business — without it, most market transactions would be impossible. It is also a hallmark of high-performing organizations. Employees in high-trust companies are more productive, are more satisfied with their jobs, put in greater discretionary effort, are less likely to search for new jobs, and even are healthier than those working in low-trust companies. Businesses that build trust among their customers are rewarded with greater loyalty and higher sales. And negotiators who build trust with each other are more likely to find value-creating deals.

Despite the primacy of trust in commerce, its neurobiological underpinnings were not well understood until recently. Over the past 20 years, research has revealed why we trust strangers, which leadership behaviors lead to the breakdown of trust, and how insights from neuroscience can help colleagues build trust with each other — and help boost a company’s bottom line.

THE BIOLOGY OF TRUST

Human brains have two neurological idiosyncrasies that allow us to trust and collaborate with people outside our immediate social group (something no other animal is capable of doing). The first involves our hypertrophied cortex, the brain’s outer surface, where insight, planning, and abstract thought largely occur. Parts of the cortex let us do an amazing trick: transport ourselves into someone else’s mind. Called theory of mind by psychologists, it’s essentially our ability to think, “If I were her, I would do this.” It lets us forecast others’ actions so that we can coordinate our behavior with theirs.  ..... "   (Details at the ink)