Are dogs people?

Boston Review asks the most pressing question:

 “Do dogs have some concept of humans as something more than food dispensers?” he asks.

Simply knowing that human feelings toward dogs are reciprocated in some way, even if only partially, changes everything. It would mean that dog-human relationships belong on the same plane as human-human relationships.

The stakes are clear: his brain scans show that dogs think and therefore are sentient persons. Consequently they should be granted rights of personhood…But since no animal welfare act has yet “elevated the rights of dogs to the same level as those of our human subjects,” Berns’s goal is understandable. To say that dogs are persons is to attribute to them the kind of conscious intentionality that defines subjectivity as we understand it.

…[W]hat if we summoned instead a kind of remote and uncertain reservoir on which all creatures might draw but from which most humans have learned to cut themselves off completely? Instead of opposing humans to dogs, we need to question the boundaries of humanity.

I work with tiny nematodes with 302 neurons. These guys, quite clearly, think. If you spend any time watching them you will see their nose flick to the left, then to the right, then slowly move back to the left as if considering before they run off in that direction. Yet I will unequivocally state that they are not people.

But where is the boundary between humanity and other animals? Now that’s a much more interesting question.

What is popular in top neuroscience?

As a reminder, here is the list of the top 10 most cited neuroscience papers from the last ten years:

1. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

2. Epigenetic programming by maternal behavior.

3. Expanded GGGGCC Hexanucleotide Repeat in Noncoding Region of C9ORF72 Causes Chromosome 9p-Linked FTD and ALS.

4. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse.

5. Suppression of basal autophagy in neural cells causes neurodegenerative disease in mice.

6. Mutations in FUS, an RNA processing protein, cause familial amyotrophic lateral sclerosis type 6.

7. Microglia: active sensor and versatile effector cells in the normal and pathologic brain.

8. Separate neural systems value immediate and delayed monetary rewards.

9. The neural basis of economic decision-making in the ultimatum game.

10. TDP-43 mutations in familial and sporadic amyotrophic lateral sclerosis.

You can also go back to my previous post and see the longer (and more detailed) list of most-cited papers from the last ten years.

I made some word clouds! I took my database and removed words like “neuroscience”, “neuron”, “cells”, etc. Here are the most common words from the top 100 most cited papers of the last ten years:

Here are the next 100:

and then 200-500:

therest_neuroscienceI don’t trust the data to go down any further. The lesson here is if you want an incredibly well-cited paper, work on human cognition! If you just want a really well-cited one, you can work on mechanisms, circuitry, or stem cells.  And unless you are working on the hippocampus, definitely don’t refer to what region of the brain you are studying unless you just want a pretty well-cited paper.

So much for making explicit that you work on rats and mice; people only want to know if you are working on humans or AIs!

The Neuroscience Top 20 (Updated)

Update 1: I made a mistake when scraping the data. I’ve regenerated the list of research articles but will have to wait on the list including books & reviews for when I get home this evening.

Update 2: I made word clouds! Go see what are the most popular words used in the most-cited papers.

I had originally made this list using Web of Science. However, when poking through the results I realized there were tons of papers that it was not classifying as Neuroscience. Why would a paper dissecting a neural circuit be labeled “Techniques/Other” and be excluded from “Neuroscience”? So I did some digging and found this cool tool called Publish Or Perish that will scrape Google Scholar search results. I’m going to order these in terms of citations/year. It’s interesting that, for all the hustle and bustle, the first Deisseroth paper isn’t until #13 #18 and that systems neuroscience is totally unrepresented. Ladies and Gentlemen, your Neuroscience Top 20 for the last ten years:

1. 310 citations/year, 3101 total citations. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis, Delorme et al. (2004)

2. 263 citations/year, 2633 total citations. Epigenetic programming by maternal behavior, Weaver et al (2004)

3. 234 citations/year, 703 total citations. Expanded GGGGCC Hexanucleotide Repeat in Noncoding Region of C9ORF72 Causes Chromosome 9p-Linked FTD and ALS. DeJesus-Hernandez et al (2011)

4. 218 citations/year, 1094 total citations. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. McGowan et al (2009)

5. 188 citations/year, 1511 total citations. Suppression of basal autophagy in neural cells causes neurodegenerative disease in mice. Hara et al (2006)

6. 165 citations/year, 826 total citations. Mutations in FUS, an RNA processing protein, cause familial amyotrophic lateral sclerosis type 6. Vance et al (2009)

7. 163 citations/year, 1145 total citations. Microglia: active sensor and versatile effector cells in the normal and pathologic brain. Hanisch et al (2007)

8. 159 citations/year, 1598 total citations. Separate neural systems value immediate and delayed monetary rewards. McClure et al (2004)

9. 159 citations/year, 1752 total citations. The neural basis of economic decision-making in the ultimatum game. Sanfrey et al (2004).

10. 159 citations/year, 955 total citations. TDP-43 mutations in familial and sporadic amyotrophic lateral sclerosis. Sreedharan (2008)

11. 147 citations/year, 1179 total citations. Reducing the dimensionality of data with neural networks. Hinton et al (2006)

12. 143 citations/year, 1289 total citations. 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression. Pezawas et al (2005)

13. 142 citations/year, 997 total citations. Dissociable intrinsic connectivity networks for salience processing and executive control. Seeley et al (2007)

14. 139 citations/year, 696 total citations. Circular analysis in systems neuroscience: the dangers of double dipping. Kriegeskorte et al (2009)

15. 133 citations/year, 801 total citations. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. Cahoy et al (2008)

16. 125.9 citations/year, 1259 total citations. Neural systems supporting interoceptive awareness. Critchley et al (2004)

17. 125 citations/year, 628 total citations. Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Chambers et al (2009)

18. 124 citations/year, 1122 total citations. Millisecond-timescale, genetically targeted optical control of neural activity. Boyden et al (2005)

19. 123 citations/year, 741 total citations. Pluripotent stem cells induced from adult neural stem cells by reprogramming with two factors. Kim et al (2008)

20. 121 citations/year, 608 total citations. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. Buckner et al (2009)

21. 120 citations/year, 1204 total citations. Cloning of the Gene Containing Mutations that Cause PARK8-Linked Parkinson’s Disease.

22. 118 citations/year, 1308 total citations. Both of Us Disgusted in My Insula: The Common Neural Basis of Seeing and Feeling Disgust. Wicker et al (2003).

23. 117 citations/year, 1297 total citations. Normalization of cDNA microarray data. Smyth et al (2003)

24. 117 citations/year, 588 total citations. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Sohal et al (2009).

25. 115 citations/year, 1271 total citations. Neural mechanisms of empathy in humans: a relay from neural systems for imitation to limbic areas. Carr et al (2003).

26. 113 citations/year, 1017 total citations. Understanding emotions in others: mirror neuron dysfunction in children with autism spectrum disorders. Dapretto et al (2005).

27. 112 citations/year, 1123 total citations. The neural basis of altruistic punishment. De Quervain (2004).

And here are the most cited articles if you include books and reviews.

1.894 citations/year, 5346 total citations. The prefrontal cortex, J Fuster (2008)

2. 798 citations/year, 5591 total citations. Pattern recognition and neural networks. Ripley (2007)

3. 472 citations/year, 3778 total citations. Fundamentals of Neural Networks: Arquitectures, Algorithms, and Applications. Fausett (2006)

4. 451 citations/year, 451 total citations. Right hemisphere language comprehension: Perspectives from cognitive neuroscience, Beeman et al. (2013)

5. 328 citations/year, 1641 total citations. Neural networks and learning machines. Haykin (2009)

6. 310 citations/year, 3101 total citations. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis, Delorme et al. (2004)

7. 303 citations/year, 910 total citations. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups, Albert et al. (2011)

8. 295 citations/year, 2655 total citations. Developmental neurobiology, Rao et al (2005)

9. 286 citations/year, 3154 total citations. The endophenotype concept in psychiatry: etymology and strategic intentions. Gottesman et al (2003).

10. 274 citations/year, 1099 total citations. Neural Computing-an introduction. Beale et al (2010)

11. 263 citations/year, 2633 total citations. Epigenetic programming by maternal behavior, Weaver et al (2004)

12. 250 citations/year, 1750 total citations. Neuroscience, Bear et al (2007)

13. 234 citations/year, 703 total citations. Expanded GGGGCC Hexanucleotide Repeat in Noncoding Region of C9ORF72 Causes Chromosome 9p-Linked FTD and ALS. DeJesus-Hernandez et al (2011)

14. 222 citations/year, 1336 total citations. Effective treatments for PTSD: practice guidelines from the International Society for Traumatic Stress Studies. Foa et al. (2008)

15. 221 citations/year, 1550 total citations. Dynamical systems in neuroscience. Izhikevich (2007)

16. 218 citations/year, 1094 total citations. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. McGowan et al (2009)

17. 217 citations/year, 1306 total citations. Mechanisms and functional implications of adult neurogenesis. Zhao et al (2008)

18. 213 citations/year, 427 total citations. Fundamental neuroscience. Squire et al (2012)

19. 198 citations/year, 1189 total citations. The human central nervous system. Nieuwenhuys et al (2008).

20. 194 citations/year, 779 total citations. Handbook of emotions. Lewis et al (2010).

Here is all the data in CSV form.

Lessons learned? Uh, be famous and write reviews or books? But more seriously, make a technique that everyone needs to use. Or do fMRI on something that will grab general interest.

The Connectionists

Labrigger (via Carson Chow) pointed to a sprawling debate on the Connectionist mailing list concerning Big Data and theory in neuroscience. See the list here (“Brain-like computing fanfare and big data fanfare”). There seem to be three main threads of debate:

(1) Is Big Data what we need to understand the brain?

(2) What is the correct level of detail?

(3) Can we fruitfully do neuroscience in the absence of models? Do we have clearly-posed problems?

Here are some key comments you can read.

There are few points that need to be made. First, one of the ongoing criticisms through the thread concerns the utility of Deep Learning models. It is repeatedly asserted that one beauty of the brain is that it doesn’t necessarily needs gobs of data to be able to perform many important behaviors. This is actually not true in the slightest: the data has been collected through many generations of evolution. In fact, Deep Learning ‘assembles’ its network through successive training of layers in a manner vaguely reminiscent of the development of the nervous system.

In terms of the correct level of detail, James Bower is ardent in promoting the idea that we need to go down to the nitty-gritty. In cerebellum, for instance, you need to understand the composition of ion channels on the dendrites to understand the function of the cells. Otherwise, you miss the compartmentalized computations being performed there. And someone else points out that, in fact, from another view this is not even reduced enough; why aren’t they considering transcription? James Bower responds with:

One of the straw men raised when talking about realistic models is always: “at what level do you stop, quantum mechanics?”. The answer is really quite simple, you need to model biological systems at the level that evolution (selection) operates and not lower. In some sense, what all biological investigation is about, is how evolution has patterned matter. Therefore, if evolution doesn’t manipulate at a particular level, it is not necessary to understand how the machine works.

…although genes are clearly a level that selection operates on…

But I think the underlying questions here really are:

(1) What level of detail do we need to understand in order to predict behavior of [neurons/networks/organisms]?

(2) Do we understand enough of the nervous system – or general organismal biology – to make theoretical predictions that we can test experimentally?

I think Geoff Hinton’s comment is a good answer:

A lot of the discussion is about telling other people what they should NOT be doing. I think people should just get on and do whatever they think might work. Obviously they will focus on approaches that make use of their particular skills. We won’t know until afterwards which approaches led to major progress and which were dead ends. Maybe a fruitful approach is to model every connection in a piece of retina in order to distinguish between detailed theories of how cells get to be direction selective. Maybe its building huge and very artificial neural nets that are much better than other approaches at some difficult task. Probably its both of these and many others too. The way to really slow down the expected rate of progress in understanding how the brain works is to insist that there is one right approach and nearly all the money should go to that approach.

Communication among animals (aka, I wasn’t droppin’ no eaves sir, honest.)

cocktail party

I have terrible hearing. I’m not hearing-impaired in any actual way, but whenever there is a lot of background noise – terrible music at a bar, the burbling of friends at a big party – I just cannot understand what people are saying even when they’re right nearby. I honestly spend most of time responding to what I guess they’re talking about. But this ability to separate what a friend is signaling from the background noise is not just a problem most of us are able to solve at “cocktail parties” but is also something that ubiquitous technology like cell phones have been developed to cope with.

A less understood problem is not how to detect and understand these signals, but how to convey them. Should you speak really loudly? Have a particularly distinctive voice? This is something that animals in the wild have to deal with all the time. Among the cacophony that is multiple species trying to chatter at each other, they have to decide how to send messages to each other that are both detectable and understandable from the background noise.

The traditional view has been that animals will act like to channels: partition the space so that they don’t interfere with each other too much. This bird over here will squawk loudly, this dove will coo softly, and so on. That would be the most informative way if each species were acting on their own. But of course there are other things to consider. Two birds may occupy the same ecological niche, worried about the same predators and needing to warn off other animals that are battling for the same food. If that was the driving evolutionary pressure, signals might end up more clustered than you’d otherwise expect.

In fact, the latter possibility is exactly what happens. Tobias et al. visited the Amazon and recorded the dense vocalizations of more than 300 animals throughout the day. Taking the principal components, they found that the three most relevant ways to describe the data are in pitch, duration, and pace of the signal. In fact, there is much more clustering than you’d expect from animals partitioning their signal. Although they are not able to test it directly, this suggests that there could be a lot of communication between different species. This interspecies communication shouldn’t be too shocking: we all understand a growl when we hear it, right?

Informationally-optimal filters for natural sounds (left) and experimentally measured cochlear filters (right)

Informationally-optimal filters for natural sounds (left) and experimentally measured cochlear filters (right)

One of the fundamental questions in neuroscience is how our sensory neurons are able to represent the world. An extremely fruitful line of research has been to study how neurons respond to natural stimuli. It makes sense, then, that sensory neurons have evolved to represent as much information as possible about the natural world – after all, why would you throw away information right away? An influential paper by Michael Lewicki proposed an answer for audition by finding the independent components of natural sounds. But no one has thought about this in an ecological context! Natural sounds have to compete – or cooperate – with vocalizations from other animals. Hopefully we will see evidence of that in the future.

References

Tobias JA, Planqué R, Cram DL, & Seddon N (2014). Species interactions and the structure of complex communication networks. Proceedings of the National Academy of Sciences of the United States of America, 111 (3), 1020-5 PMID: 24395769

M Lewicki (2002). Efficient coding of natural sounds Nature Neuroscience DOI: 10.1038/nn831

Photo from

 

What are the most cited neuroscience papers in the last 10 years?

Can you guess which are the most cited neuroscience papers from the last ten years? Without cheating, mind you! I have seen this done for economics and ecology already, so look at those if you are interested.

Typically, reviews garner more citations than non-reviews so feel free to guess either one (is the most-cited paper a review? hmmm….).

I will post the top 20 on Wednesday, January 29th.

Update: Here is your Neuroscience Top 20

The utility of biomimicry

Pearce found inspiration in the termite mounds that dotted the savannas across his country. The largest mounds could reach several meters in height, dwarfing the legions of termites who built them just as a modern skyscraper towers over an individual construction worker. Each funneled air underground through networks of channels into a spherical nest that housed termites by the millions, and even larger numbers of fungi and bacteria. In all, a typical nest contained about a small cow’s worth of hot, breathing biomass. Based on the ideas of the Swiss entomologist Martin Lüscher, many researchers believed the mounds acted as air conditioners, maintaining a nest’s pleasant temperature, humidity, and oxygenation by continuously exchanging hot air rising from deep inside a colony with cooler drafts diffusing down from the surface…

Around the same time that the Eastgate Centre opened its doors, the American scientist Scott Turner was using propane pumps and arrays of tiny electronic sensors to painstakingly measure gas exchange throughout nearly 50 South African termite mounds. He found that the mounds didn’t regulate temperature so much as push oxygen and carbon dioxide into and out of the nest…The mounds weren’t crude air conditioners so much as a wildly complicated external respiratory system…

This hasn’t stopped classical biomimicry from its myriad successes. Japanese bullet trains blast through tunnels with barely a whisper thanks to aerodynamic shells inspired by the beaks of diving birds. Olympic swimmers shatter world records by wearing suits coated with a drag-reducing texture resembling sharkskin. Rising energy and materials costs have led to a new generation of skyscrapers and
“smart buildings” in cities around the world with bio-inspired passive cooling systems and lightweight structural supports.

The Termite and the Architect. How useful is biomimicry? And how much of the utility is just because it makes you think?

A whole crowd of dreamers like himself

These observations woke new currents in Wokulski’s soul, of which he had not thought before, or only imprecisely. And so the great city, like a plant or beast, had its own anatomy and physiology. And so the work of millions of people who proclaimed their free will so loudly produced the same results as bees building regular honeycombs, ants raising rounded mounds, or chemical compounds forming regular crystals.

Thus there was nothing accidental in society, but an inflexible law which, as if in irony at human pride, manifested itself so clearly in the life of the most capricious of nations, the French!

…And he imagined how it would have been if he’d been born in Paris instead of Warsaw. In the first place, he would have been enabled to learn more as a child because of the many schools and colleges. Then, even if he had gone into trade, he would have experienced less unpleasantness and more help in his studies. Further, he wouldn’t have worked on a perpetual motion machine for he’d have known that many similar machines which never worked were to be found in the museums here. Had he tried to construct guided balloons, he would have found models, a whole crowd of dreamers like himself, and even help if his ideas were practical.

Thoughts on cities, life, and determinism by Stanisław Wokulski (The Doll, Bolesław Prus, 1890.)

I’m finally back from my physical and mental holiday vacation. I read The Doll during this time and am in awe at the collection of insights into capitalism, industry, technology, social status and structures, human life and more. It’s shockingly contemporary and readable, and has to be one of the best major novels of the 19th century – yet is almost entirely unread.