Neuro Chip Records Brain Cell Activity At Higher Resolution

im putting together a couple of scottish folk mixes bc that’s what i do and im honestly curious if anyone in my country has ever been unequivocally happy about anything ever

Back when I was a studying biology, I noticed that a lot of anatomical terms sound like they come straight from Middle Earth. So, to celebrate the release of the last Hobbit film, I’ve created this INCREDIBLY nerdy quiz.

Do these words and phrases refer to parts of the human body, or reference people and places from J. R. R. Tolkien’s work?

Antrum of Highmore

Crypt of Morgagni

Caves of Androth

Lobelia

Loop of Henle

Scapha

Great Vein of Galen

Halls of Mandos

Groin

Gap of Calenardhon

Macewen’s Triangle

Canal of Schlemm

Gerontius

Islets of Langerhans

Meckel’s Cave

Chamber of Mazarbul

You shall not pass.

Your pocket Constitution

The amazing Khizr Khan was onto something with his pocket U.S. Constitution - and our Labs team went ahead and made an app for that. Understanding the U.S. Constitution is an app that allows you to view the articles and amendments of the Constitution, and then links you to scholarship relating to each specific section. It’s free for iOS and Android. Keep fighting the good fight against “alternative facts.” 

More here: http://labs.jstor.org/constitution/

Sailor’s Eyeballs (Valonia ventricosa)

Known as bubble algae or sailor’s eyeballs, Valonia ventricosa are one of the world’s largest single-celled organisms. They’re found in almost every ocean in the world, mostly in tropical and sub-tropical regions among coral rubble.

These tough, shiny multi-nucleic cells, a kind of green algae, usually grow to be 0.4 to 1.5 inches in diameter but sometimes reach up to 2 inches across. By comparison, most human cells are so small they’re invisible to the naked eye; Valonia ventricosa are larger than your fingernail!

photograph by Alexander Vasenin | Wikipedia

via: American Museum of Natural History

Molecule of the Day - DDT

Dichlorodiphenyltrichloroethane (C14H9Cl5), more commonly known as DDT, is a colourless, tasteless solid under room conditions. It was used as an insecticide during the 1940s-1970s, and gained notoriety after Rachel Carson’s 1962 book, Silent Spring, which highlighted the health and environmental effects of DDT.

DDT acts by binding to voltage-gated sodium ion channels of neurons (as seen on the left of the diagram below), causing these channels to be permanently open instead of opening only upon the arrival of an action potential. Consequently, there is a continuous influx of Na+ ions into the neuron, which triggers a series of rapid action potentials and hence neuronal impulses. This leads to rapid muscle contractions, spasms, and death.

While this effect does not occur in humans and other non-insects, it is still moderately toxic, and as been shown to be an endocrine disruptor. Therefore, chronic exposure to it can lead to tumour formation, developmental problems, and birth defects. DDT is also considered to be a possible carcinogen.

Due to the hydrophobicity of DDT, it tends to accumulate in the lipids of living organisms rather than in the environment. This results in biomagnification, in which its concentration increases upon going up the food chain, as each organism of a rung of the chain consumes multiple prey. Consequently, the usage of DDT affected the populations of many birds of prey, such as the bald eagle.

In 1962, Rachel Carson published the book Silent Spring, which highlighted the negative effects of the usage of DDT and other pesticides on the environment and biodiversity. This book was revolutionary; it sparked a heated debate on pesticides and contributed to the 1972 US ban on DDT. The world followed suit; most countries around the world now prohibit the use of DDT, except for limited disease vector control purposes, such as for malaria.

DDT is synthesised by the condensation of a molecule of chloral and 2 molecules of chlorobenzene via an electrophilic substitution reaction, producing water as a by-product.

Could you explain this tfw no ZF joke? I really dont get it... :D

Get ready for a long explanation! For everyone’s reference, the joke (supplied by @awesomepus​) was:

Q: What did the mathematician say when he encountered the paradoxes of naive set theory?A: tfw no ZF

You probably already know the ‘tfw no gf’ (that feel when no girlfriend) meme, which dates to 2010. I’m assuming you’re asking about the ZF part.

Mathematically, ZF is a reference to Zermelo-Fraenkel set theory, which is a set of axioms commonly accepted by mathematicians as the foundation of modern mathematics. As you probably know if you’ve taken geometry, axioms are super important: they are basic assumptions we make about the world we’re working in, and they have serious implications for what we can and can’t do in that world. 

For example, if you don’t assume the Parallel Postulate (that consecutive interior angle measures between two parallel lines and a transversal sum to 180°, or twice the size of a right angle), you can’t prove the Triangle Angle Sum Theorem (that the sum of the angle measures in any triangle is also 180°). It’s not that the Triangle Angle Sum Theorem theorem is not true without the Parallel Postulate — simply that it is unprovable, or put differently, neither true nor false, without that Postulate. Asking whether the Triangle Angle Sum Theorem is true without the Parallel Postulate is really a meaningless question, mathematically. But we understand that, in Euclidean geometry (not in curved geometries), both the postulate and the theorem are “true” in the sense that we have good reason to believe them (e.g., measuring lots of angles in physical parallel lines and triangles). Clearly, the axioms we choose are important.

Now, in the late 19th and early 20th century, mathematicians and logicians were interested in understanding the underpinnings of the basic structures we use in math — sets, or “collections,” being one of them, and arithmetic being another. In short, they were trying to come up with an axiomatic set theory. Cantor and Frege were doing a lot of this work, and made good progress using everyday language. They said that a set is any definable collection of elements, where “definable” means to provide a comprehension (a term you’re familiar with if you program in Python), or rule by which the set is constructed.

But along came Bertrand Russell. He pointed out a big problem in Cantor and Frege’s work, which is now called Russell’s paradox. Essentially, he made the following argument:

Y’all are saying any definable collection is a set. Well, how about this set: R, the set of all sets not contained within themselves. This is, according to you, a valid set, because I gave that comprehension. Now, R is not contained within itself, naturally: if it is contained within itself, then it being an element is a violation of my construction of R in the first place. But R must be contained within itself: if it’s not an element of itself, then it is a set that does not contain itself, and therefore it is an element of itself. So we have that R ∈ R and also R ∉ R. This is a contradiction! Obviously, your theory is seriously messed up.

This paradox is inherently a part of Cantor and Frege’s set theory — it shows that their system was inconsistent (with itself). As Qiaochu Yuan explains over at Quora, the problem is exactly what Russell pointed out: unrestricted comprehension — the idea that you can get away with defining any set you like simply by giving a comprehension. Zermelo and Fraenkel then came along and offered up a system of axioms that formalizes Cantor and Frege’s work logically, and restricts comprehension. This is called Zermelo-Fraenkel set theory (or ZF), and it is consistent (with itself). Cantor and Frege’s work was then retroactively called naive set theory, because it was, of course, pretty childish:

There are two more things worth knowing about axiomatic systems in mathematics. First, some people combine Zermelo-Fraenkel set theory with the Axiom of Choice¹, resulting in a set theory called ZFC. This is widely used as a standard by mathematicians today. Second, Gödel proved in 1931 that no system of axioms for arithmetic can be both consistent and complete — in every consistent axiomatization, there are “true” statements that are unprovable. Or put another way: in every consistent axiomatic system, there are statements which you can neither prove nor disprove.For example, in ZF, the Axiom of Choice is unprovable — you can’t prove it from the axioms in ZF. And in both ZF and ZFC, the continuum hypothesis² is unprovable.³ Gödel’s result is called the incompleteness theorem, and it’s a little depressing, because it means you can’t have any good logical basis for all of mathematics (but don’t tell anyone that, or we might all be out of a job). Luckily, ZF or ZFC has been good enough for virtually all of the mathematics we as a species have done so far!

The joke is that, when confronted with Russell’s paradox in naive set theory, the mathematician despairs, and wishes he could use Zermelo-Fraenkel set theory instead — ‘that feel when no ZF.’

I thought the joke was incredibly funny, specifically because of the reference to ‘tfw no gf’ and the implication that mathematicians romanticize ZF (which we totally do). I’ve definitely borrowed the joke to impress friends and faculty in the math department…a sort of fringe benefit of having a math blog.

– CJH

Keep reading

Awesome things you can do (or learn) through TensorFlow. From the site:

A Neural Network Playground

Um, What Is a Neural Network?

It’s a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other. Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure. For a more detailed introduction to neural networks, Michael Nielsen’s Neural Networks and Deep Learning is a good place to start. For more a more technical overview, try Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

GitHub

h-t FlowingData

I am not a native Japanese speaker but the first word that comes to mind is 懐かしい (natsukashii), which is that warm fuzzy feeling you have when you think upon a fond memory or experience. Or that feeling you are having when you say, "sure brings back memories." Depending on context it gets translated to nostalgic, or longing, or dear, but by themselves they all feel somewhat inadequate.

For Chinese mandarin, I can think of 骗我的感情 (pian wo de gan qing) (there should be tone markers, but I don't know how to put them in, sorry!), which is literally "trick/bluff my feelings", which I am now finding quite to explain! Hmm... it's that disappointment you feel when someone sets your expectations up for something and then fails to deliver. I suppose like feeling cheated.

Hope that helps and good luck!

bobbies

YOU SPEAK A LANGUAGE AND I NEED YOUR HELP PLEASE I BEG YOU

hi. sorry about that catchy title, but you have something i need. you speak a language, maybe even multiple languages. you use emotions words everyday. i’m sure you know that languages have their own emotion words that are very hard to translate to other languages, for example, the word ‘anxiety’ doesn’t really exist in Polish, it is always a challenge to translate it in such way to convey its true meaning. Polish people don’t really feel anxiety, because they don’t have the word for it. i need your help with something: tell me an emotion word that is unique to your language or hard to translate. i’ll ask you a few questions and maybe i’ll write an essay about it using the natural semantic metalanguage (NSM). it’s a linguistic theory, whatever. please help a linguist out. i need an A. i promise i won’t get an F on your precious word. 

i am interested in emotion words from every language except for Polish and English.

you can reply under this post, you can message me privately, i can give you my e-mail, whatever works for you. it would really help me if you reblogged this post, but no pressure

help education.. pretty please?

Entanglement Made Simple, a divulgative article of theoretical physicist and Nobel laureate Frank Wilczek, in Quanta Magazine.

Image by James O'Brien for Quanta Magazine

(Fig.1. Neuron connections in biological neural networks. Source: MIPT press office)

Physicists build “electronic synapses” for neural networks

A team of scientists from the Moscow Institute of Physics and Technology(MIPT) have created prototypes of “electronic synapses” based on ultra-thin films of hafnium oxide (HfO2). These prototypes could potentially be used in fundamentally new computing systems. The paper has been published in the journal Nanoscale Research Letters.

The group of researchers from MIPT have made HfO2-based memristors measuring just 40x40 nm2. The nanostructures they built exhibit properties similar to biological synapses. Using newly developed technology, the memristors were integrated in matrices: in the future this technology may be used to design computers that function similar to biological neural networks.

Memristors (resistors with memory) are devices that are able to change their state (conductivity) depending on the charge passing through them, and they therefore have a memory of their “history”. In this study, the scientists used devices based on thin-film hafnium oxide, a material that is already used in the production of modern processors. This means that this new lab technology could, if required, easily be used in industrial processes.

“In a simpler version, memristors are promising binary non-volatile memory cells, in which information is written by switching the electric resistance – from high to low and back again. What we are trying to demonstrate are much more complex functions of memristors – that they behave similar to biological synapses,” said Yury Matveyev, the corresponding author of the paper, and senior researcher of MIPT’s Laboratory of Functional Materials and Devices for Nanoelectronics, commenting on the study.

Synapses – the key to learning and memory

A synapse is point of connection between neurons, the main function of which is to transmit a signal (a spike – a particular type of signal, see fig. 2) from one neuron to another. Each neuron may have thousands of synapses, i.e. connect with a large number of other neurons. This means that information can be processed in parallel, rather than sequentially (as in modern computers). This is the reason why “living” neural networks are so immensely effective both in terms of speed and energy consumption in solving large range of tasks, such as image / voice recognition, etc.

(Fig.2 The type of electrical signal transmitted by neurons (a “spike”). The red lines are various other biological signals, the black line is the averaged signal. Source: MIPT press office)

Over time, synapses may change their “weight”, i.e. their ability to transmit a signal. This property is believed to be the key to understanding the learning and memory functions of thebrain.

From the physical point of view, synaptic “memory” and “learning” in the brain can be interpreted as follows: the neural connection possesses a certain “conductivity”, which is determined by the previous “history” of signals that have passed through the connection. If a synapse transmits a signal from one neuron to another, we can say that it has high “conductivity”, and if it does not, we say it has low “conductivity”. However, synapses do not simply function in on/off mode; they can have any intermediate “weight” (intermediate conductivity value). Accordingly, if we want to simulate them using certain devices, these devices will also have to have analogous characteristics.

The memristor as an analogue of the synapse

As in a biological synapse, the value of the electrical conductivity of a memristor is the result of its previous “life” – from the moment it was made.

There is a number of physical effects that can be exploited to design memristors. In this study, the authors used devices based on ultrathin-film hafnium oxide, which exhibit the effect of soft (reversible) electrical breakdown under an applied external electric field. Most often, these devices use only two different states encoding logic zero and one. However, in order to simulate biological synapses, a continuous spectrum of conductivities had to be used in the devices.

“The detailed physical mechanism behind the function of the memristors in question is still debated. However, the qualitative model is as follows: in the metal–ultrathin oxide–metal structure, charged point defects, such as vacancies of oxygen atoms, are formed and move around in the oxide layer when exposed to an electric field. It is these defects that are responsible for the reversible change in the conductivity of the oxide layer,” says the co-author of the paper and researcher of MIPT’s Laboratory of Functional Materials and Devices for Nanoelectronics, Sergey Zakharchenko.

The authors used the newly developed “analogue” memristors to model various learning mechanisms (“plasticity”) of biological synapses. In particular, this involved functions such as long-term potentiation (LTP) or long-term depression (LTD) of a connection between two neurons. It is generally accepted that these functions are the underlying mechanisms of  memory in the brain.

The authors also succeeded in demonstrating a more complex mechanism – spike-timing-dependent plasticity, i.e. the dependence of the value of the connection between neurons on the relative time taken for them to be “triggered”. It had previously been shown that this mechanism is responsible for associative learning – the ability of the brain to find connections between different events.

To demonstrate this function in their memristor devices, the authors purposefully used an electric signal which reproduced, as far as possible, the signals in living neurons, and they obtained a dependency very similar to those observed in living synapses (see fig. 3).

(Fig.3. The change in conductivity of memristors depending on the temporal separation between “spikes”(rigth) and the change in potential of the neuron connections in biological neural networks. Source: MIPT press office)

These results allowed the authors to confirm that the elements that they had developed could be considered a prototype of the “electronic synapse”, which could be used as a basis for the hardware implementation of artificial neural networks.

“We have created a baseline matrix of nanoscale memristors demonstrating the properties of biological synapses. Thanks to this research, we are now one step closer to building an artificial neural network. It may only be the very simplest of networks, but it is nevertheless a hardware prototype,” said the head of MIPT’s Laboratory of Functional Materials and Devices for Nanoelectronics, Andrey Zenkevich.