# Papers for the week, 1/1 edition

Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs. Ming Wu, Aljoscha Nern, W. Ryan Williamson, Mai M Morimoto, Michael B Reiser, Gwyneth M Card, Gerald M Rubin.

The hippocampus as a predictive map. Kimberly Lauren Stachenfeld, Matthew M Botvinick, Samuel J Gershman.

Searching for Signatures of Brain Maturity: What Are We Searching For? Leah H. Somerville.

The misleading narrative of the canonical faculty productivity trajectory. Samuel F. Way, Allison C. Morgan, Aaron Clauset, Daniel B. Larremore.

Contribution of Head Shadow and Pinna Cues to Chronic Monaural Sound Localization. Marc M. Van Wanrooij and A. John Van Opstal.

The influence of pinnae‐based spectral cues on sound localization. Alan D. Musicant and Robert A. Butler.

Everyday bat vocalizations contain information about emitter, addressee, context, and behavior. Yosef Prat, Mor Taub & Yossi Yovel.

A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing.Haruo Hosoya, Aapo Hyvärinen.

cGAL, a temperature-robust GAL4–UAS system for Caenorhabditis elegans. Han Wang, Jonathan Liu, Shahla Gharib, Cynthia M Chai, Erich M Schwarz, Navin Pokala & Paul W Sternberg.

Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Min-Tzu Lo, David A Hinds, Joyce Y Tung, Carol Franz, Chun-Chieh Fan, Yunpeng Wang, Olav B Smeland, Andrew Schork, Dominic Holland, Karolina Kauppi, Nilotpal Sanyal, Valentina Escott-Price, Daniel J Smith, Michael O’Donovan, Hreinn Stefansson, Gyda Bjornsdottir, Thorgeir E Thorgeirsson, Kari Stefansson, Linda K McEvoy, Anders M Dale, Ole A Andreassen & Chi-Hua Chen.

# 5 things I learned on Sunday at #sfn16

Just some irrelevant facts.

1. Toadfish can sing by vibrating their swim bladder, and can vocalize independent of breathing.

2. Scientists working in Drosophila are starting to see reafferent signals (signals representing motor commands such as walking or singing) in tons of places, from sensory neurons on downward. This is one of the areas where Drosophila neuroscientists are way ahead of mammalian neuroscientists.

3. The computations that visual neurons perform can fundamentally alter in conditions that seem like they should be similar, such as light levels (this is not just adaptation to statistics).

4. There are conditions in which retinal neurons can be synergistic and not redundant. This is a bit of a controversy in the field, with the common consensus that ganglion cells have ~10% redundancy. Apparently this is not always the case!

5. Drosophila (fruit flies) have a spatial short term memory that has been located in the central complex (actually, I am not clear exactly where the anatomical structure is located: it may be just outside of the central complex.)

# Papers for the week, 10/30 edition

Recurrent Switching Linear Dynamical Systems. Scott W. Linderman, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski, Matthew J. Johnson. 2016.

The Serotonergic System Tracks the Outcomes of Actions to Mediate Short-Term Motor Learning. Takashi Kawashima, Maarten F. Zwart, Chao-Tsung Yang, Brett D. Mensh, Misha B. Ahrens. 2016.

A visual circuit uses complementary mechanisms to support transient and sustained pupil constriction. William Thomas Keenan, Alan C Rupp, Rachel A Ross, Preethi Somasundaram, Suja Hiriyanna, Zhijian Wu, Tudor C Badea, Phyllis R Robinson, Bradford B Lowell, Samer S Hattar. 2016.

Numerical analysis of homogeneous and inhomogeneous intermittent search strategies. Karsten Schwarz, Yannick Schröder, and Heiko Rieger. 2016.

Unexpected arousal modulates the influence of sensory noise on confidence. Micah Allen, Darya Frank, D Samuel Schwarzkopf, Francesca Fardo, Joel S Winston, Tobias U Hauser, Geraint Rees. 2016.

Vision Drives Accurate Approach Behavior during Prey Capture in Laboratory Mice.
Jennifer L. Hoy, Iryna Yavorska, Michael Wehr, Cristopher M. Niell. 2016.

Amygdala and Ventral Striatum Make Distinct Contributions to Reinforcement Learning. Vincent D. Costa,Olga Dal Monte, Daniel R. Lucas, Elisabeth A. Murray, Bruno B. Averbeck. 2016.

Potent optogenetic inhibition of behavior with anion channelrhodopsins.  Farhan Mohammad, James Stewart, Stanislav Ott, Katarina Chlebikova, Jia Yi Chua, Tong-Wey Koh, Joses Ho, Adam Claridge-Chang. 2016.

# Papers for the week, 8/21 edition

Coordinating long-latency stretch responses across the shoulder, elbow and wrist during goal-directed reaching. Jeffrey Weiler, James Saravanamuttu, Paul L Gribble, J. Andrew Pruszynski.

Internal states drive nutrient homeostasis by modulating exploration-exploitation trade-off. Veronica Maria Corrales-Carvajal, Aldo A Faisal, Carlos Ribeiro.

Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks. Noah J. Apthorpe, Alexander J. Riordan, Rob E. Aguilar, Jan Homann, Yi Gu, David W. Tank, H. Sebastian Seung.

Wake-sleep transition as a noisy bifurcation. Dong-Ping Yang, Lauren McKenzie-Sell, Angela Karanjai, and P. A. Robinson.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework. Vijay Mohan, K. Namboodiria, Joshua M. Levy, Stefan Mihalas, David W. Sims, and Marshall G. Hussain Shuler.

Optimality of Spatially Inhomogeneous Search Strategies. Karsten Schwarz, Yannick Schröder, Bin Qu, Markus Hoth, and Heiko Rieger.

Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness. Leeat Keren, Jean Hausser, Maya Lotan-Pompan, Ilya Vainberg Slutskin, Hadas Alisar, Sivan Kaminski, Adina Weinberger, Uri Alon, Ron Milo, Eran Segal.

Loom-Sensitive Neurons Link Computation to Action in the Drosophila Visual System. Saskia E.J. de Vries, Thomas R. Clandinin.

A spike-timing mechanism for action selection. Catherine R von Reyn, Patrick Breads, Martin Y Peek, Grace Zhiyu Zheng, W Ryan Williamson, Alyson L Yee, Anthony Leonardo & Gwyneth M Card.

Automatic Segmentation of Drosophila Neural Compartments Using GAL4 Expression Data Reveals Novel Visual Pathways. Karin Panser, Laszlo Tirian, Florian Schulze, Santiago Villalba, Gregory S.X.E. Jefferis, Katja Bühler, Andrew D. Straw.

# Papers for the week, 8/14 edition

A circuit motif in the zebrafish hindbrain for a two alternative behavioral choice to turn left or right, Minoru Koyama, Francesca Minale, Jennifer Shum, Nozomi Nishimura, Chris B Schaffer, Joseph R Fetcho

Coordination of Brain Wide Activity Dynamics by Dopaminergic Neurons, Heather K Decot, Vijay MK Namboodiri, Wei Gao, Jenna A McHenry, Joshua H Jennings, Sung-Ho Lee, Pranish A Kantak, Yu-ChiehJill Kao, Manasmita Das, Ilana B Witten, Karl Deisseroth, Yen-YuIan Shih, and Garret D Stuber

Convergence of visual and whisker responses in the primary somatosensory thalamus (ventral posterior medial region) of the mouse, Annette E Allen, Christopher A Procyk, Timothy M Brown, Robert J Lucas

Generating Natural Language Descriptions for Semantic Representations
of Human Brain Activity, Eri Matsuo, Ichiro Kobayashi, Shinji Nishimoto, Satoshi Nishida, Hideki Asoh

Complementary mechanisms create direction selectivity in the fly, Juergen Haag, Alexander Arenz, Etienne Serbe, Fabrizio Gabbiani, Alexander Borst

Statistical mechanics of ecological systems: Neutral theory and beyond, Sandro Azaele, Samir Suweis, Jacopo Grilli, Igor Volkov, Jayanth R. Banavar, and Amos Maritan

Human inferences about sequences: A minimal transition probability model, Florent Meyniel, Maxime Maheu, Stanislas Dehaene

Endocannabinoid signaling enhances visual responses through modulation of intracellular chloride levels in retinal ganglion cells, Loïs S Miraucourt, Jennifer Tsui, Delphine Gobert, Jean-François Desjardins, Anne Schohl, Mari Sild, Perry Spratt, Annie Castonguay, Yves De Koninck, Nicholas Marsh-Armstrong, Paul W Wiseman, Edward S Ruthazer

Human collective intelligence as distributed Bayesian inference, Peter M. Krafft, Julia Zheng, Wei Pan, Nicolás Della Penna, Yaniv Altshuler, Erez Shmueli, Joshua B. Tenenbaum, Alex Pentland

Temporal Horizons and Decision-Making: A Big Data Approach, Robert Thorstad, Phillip Wolff

# Papers for the week, 8/7 edition

Vision Guides Selection of Freeze or Flight Defense Strategies in Mice,
Gioia De Franceschi, Tipok Vivattanasarn, Aman B. Saleem, Samuel G. Solomon.

How Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience,
Sonia Kéfi, Vincent Miele, Evie A. Wieters, Sergio A. Navarrete, Eric L. Berlow.

Wireless Recording in the Peripheral Nervous System with Ultrasonic Neural Dust, Dongjin Seo, Ryan M. Neely, Konlin Shen, Utkarsh Singhal, Elad Alon, Jan M. Rabaey, Jose M. Carmena, Michel M. Maharbiz.

Thirst neurons anticipate the homeostatic consequences of eating and drinking, Christopher A. Zimmerman, Yen-Chu Lin, David E. Leib, Ling Guo, Erica L. Huey, Gwendolyn E. Daly, Yiming Chen & Zachary A. Knight.

Operation of a homeostatic sleep switch, Diogo Pimentel, Jeffrey M. Donlea, Clifford B. Talbot, Seoho M. Song, Alexander J. F. Thurston & Gero Miesenböck.

The Basis of Food Texture Sensation in Drosophila, Yali V. Zhang, Timothy J. Aikin, Zhengzheng Li, Craig Montell.

On the Encoding of Panoramic Visual Scenes in Navigating Wood Ants, Cornelia Buehlmann, Joseph L. Woodgate, Thomas S.

A rodent model of social rejection,  Haozhe Shan, Inbal Ben-Ami Bartal, Peggy Mason.

Posterior parietal cortex guides visual decisions in rats, Angela M. Licata, Matthew T. Kaufman, David Raposo, Michael B. Ryan, John P. Sheppard, Anne K. Churchland.

A Probability Distribution over Latent Causes, in the Orbitofrontal Cortex, Stephanie C. Y. Chan, Yael Niv, and Kenneth A. Norman.

Neurons in Macaque Area V4 Are Tuned for Complex Spatio-Temporal Patterns, Anirvan S. Nandy, Jude F. Mitchell, Monika P. Jadi, John H. Reynolds.

# Unrelated to all that, 11/8 edition

The Beauty of the Network in the Brain and the Origin of the Mind in the Control of Behavior

Behavior is not adequately described as a stimulus-response process. It is initiated by the animal and is generated because of its expected outcome in the future. The outcome can be good or bad for the animal. The brain is in charge of the selection process. This is the basic function of the brain. Taking Drosophila as a study case, this paper discusses initiating activity, several examples of outcome expectations, trying out (the internal search for a suitable behavior), chaining of actions, and the functional roles of chance in action selection. It takes mental processes and states such as goals, intentions, feelings, memories, cognition, and attention as higher levels of behavioral control that have their origin in biological evolution.

Why female ants battle for foreign sperm

Yet males that hook up with the wrong queens don’t realize that they’ve made a bad choice until they begin to copulate, say the scientists who studied these pairings in a series of closely controlled experiments. The males try to rectify their error by reducing the rate at which they transfer sperm, but the queens respond by holding on. The longer copulation forces the male to continue releasing his sperm, until he’s given the same amount as he would to a gal of his own kind. Sexual bondage may seem a strange evolutionary tactic, but if the female ants weren’t “sperm parasites,” the scientists say, the harvester ant colonies would collapse.

A Better Way to Think About the Genre Debate

Last month, when the fiction finalists for the National Book Awards were announced, one stood out from the rest: “Station Eleven,” by Emily St. John Mandel. While the other nominated books are what, nowadays, we call “literary fiction,” “Station Eleven” is set in a familiar genre universe, in which a pandemic has destroyed civilization. The twist—the thing that makes “Station Eleven” National Book Award material—is that the survivors are artists…

“Station Eleven,” in other words, turns out not to be a genre novel so much as a novel about genre. Unlike Cormac McCarthy’s “The Road,” which asked what would remain after the collapse of culture, “Station Eleven” asks how culture gets put together again. It imagines a future in which art, shorn of the distractions of celebrity, pedigree, and class, might find a new equilibrium. The old distinctions could be forgotten; a comic book could be as influential as Shakespeare. It’s hard to imagine a novel more perfectly suited, in both form and content, to this literary moment. For a while now, it’s looked as though we might be headed toward a total collapse of the genre system. We’ve already been contemplating the genre apocalypse that “Station Eleven” imagines.

So it’s okay to be genre, so long as it is about literature… The genre debate is really just about pretension: what is “okay” to like.

Women Having A Terrible Time At Parties In Western Art History

Undercover Robot Baby Penguins Are the Future of Ecology

Both the adult and baby replicants roving around the colony are expected to produce a wealth of observations, with a much reduced impact on the quality of life of the birds. “Our next project is to use rovers to understand how penguins are located into their colony according to their own individual history,” Le Maho told me. That will include “the role of vocalizations in this structuring.”
Le Maho’s study is the latest of a number of experiments involving camouflaged reconnaissance robots. Earlier this year, for example, I reported on a Carnegie Mellon University study that sent cameras disguised as crocodiles into Kenya’s Mara River to study hippopotamus dung. In addition to extravagantly spraying their poop around with their tails, hippos apparently were dropping so many deuces that they were causing mass die-offs of fish downstream.

Bow before your new robopenguin overlords

Today’s World

10. Don’t advertise you’ve got something, that you’re any better than the hoi polloi, then the hoi polloi will judge you for having it. Don’t say you went to Harvard, they were the son of a single mother who couldn’t go to college. Don’t say you took a vacation, they haven’t been on one in years. Don’t say you have any money, otherwise the unwashed masses will excoriate you. Don’t confuse this with the ignorant rich who believe they’re better than everybody else. Just call it poor on poor crime, or middle class on middle class crime. People don’t want you movin’ on up, because that means they’ve got to look at their situation, and they don’t want to. The end result is those with wealth and power seal their lives off from those who don’t have these things. They live behind gates, fly private and vacation in places you’ve never heard of and they don’t talk about it. If someone is talking about their wealth, bragging about their lifestyle, you know they’re nouveau riche and not accepted by the upper class and might not have money for long.

Europeans Mutate Differently

How did English become the language of science?

“The first major shock to the system of basically having a third of science published in English, a third in French, and a third in German — although it fluctuated based on field and Latin still held out in some places — was World War I, which had two major impacts,” Gordin said. After World War I, Belgian, French and British scientists organized a boycott of scientists from Germany and Austria. They were blocked from conferences and weren’t able to publish in Western European journals…

The second effect of World War I took places across the Atlantic in the United States. Starting in 1917 when the US entered the war, there was a wave of anti-German hysteria that swept the country. “At this moment something that’s often hard to keep in mind is that large portions of the US still speak German,” Gordin said. In Ohio, Wisconsin and Minnesota there were many, many German speakers. World War I changed all that. “German is criminalized in 23 states. You’re not allowed to speak it in public, you’re not allowed to use it in the radio, you’re not allowed to teach it to a child under the age 10,” Gordin explained. The Supreme Court overturned those anti-German laws in 1923, but for years that was the law of the land. What that effectively did, according to Gordin, was decimate foreign language learning in the US.

“In 1915, Americans were teaching foreign languages and learning foreign languages about the same level as Europeans were,” Gordin said. “After these laws go into effect, foreign language education drops massively. Isolationism kicks in in the 1920s, even after the laws are overturned and that means people don’t think they need to pay attention to what happens in French or in German.”

Life is quantum

What does this have to do with life? Well, Schrödinger was particularly interested in the question of heredity. In 1944, a decade before James Watson and Francis Crick, the physical nature of genes was still mysterious. Even so, it was known that they must be passed down the generations with an extraordinary high degree of fidelity: less than one error in a billion. This was a puzzle, because one of the few other known facts about genes was that they were very small – far too small, Schrödinger insisted, for the accuracy of their copying to depend on the order-from-disorder rules of the classical world. He proposed that they must instead involve a ‘more complicated organic molecule’, one in which ‘every atom, and every group of atoms, plays an individual role’.

# Unrelated to all that, 2/7 edition

I have returned from Cosyne; apologies for the cessation in updates but the wifi during the second half of the conference was entirely nonfunctional. I’ll have a post about the rest of the conference in a day or two.

On the blogs

The scientific case for P≠NP. Basically it boils down to, it’s likely (aka Bayesian inference.)

The Tarsier (via Noah Gray)

The future is here, and it is weird

If correlation doesn’t equal causation, then what does? A reminder that we have Bayes networks

What neuroscience is learning: an array of links on free will, neurophilosophy, consciousness, and more

Terrance Tao and the Navier-Stokes. Thinking of fluids like a computer

The trouble with Oxytocin. Extracting it is more complicated than you think

Matlab Information Theoretical Estimators toolbox

Science takes on a silent invader. Mussels!

# #cosyne14 day 3: Genes, behavior, and decisions

For other days (as they appear): 1, 2, 4

How do genes contribute to complex behavior?

Cosyne seems to have a fondness for inviting an ecogically-related researcher to remind us computational scientists that we’re actually studying animals that exist in, you know, an environment. Last year it was ants, this year deer mice.

Hopi Hoekstra gave an absolutely killer talk on a fairly complex behavior that is seen in deer mice: house building! Or rather, nest building. These mice will burrow to make a stereotyped nest with an entrance tunnel, a small nest, and an escape hatch that doesn’t quite make it to the surface (see below). But not every species of deer mouse builds their nest in precisely the same way. Only one (peromyscus) will build escape tunnels. Most will only make small little entrance tunnels (and possibly no nest?). Some don’t seem to dig at all. What causes this difference?

They crossed the species that makes long entrance tunnels and escape tunnels with a recently-diverged species (polionatus) that makes short entrance tunnels. These little guys will make tunnels that span the range from tiny to long, which suggests a multigenic trait. They did QTL on these crosses and found that only five genes are required for controlling nest building! One gene controls the construction of the escape tunnel, and four (three?) genes control the length of the entrance-tunnel length in an additive manner. One of the genes that is controlling tunnel length is an acetylcholine receptor in the basal ganglia (read: neuromodulator receptor in the ‘motivating’ part of the brain) that has been linked to addiction in other animals.

How many different behaviors do we have?

One of the themes that seemed to pop up this year was how to quantify animal behavior. It’s really not that obvious: is a reach for a coffee mug the same as a reach for my cell phone? Maybe, maybe not. Gordon Berman took their analytic tools to fly behavior in an attempt to map their ‘behavioral space’.

And okay, they were able to extract what look like unique behaviors: abdomen movements and wing movements and such. Okay, but that’s pretty hard for me to have an opinion on; what really sold me is when they decomposed a video of someone doing the hokey pokey. That gave them a hokey-pokey space which really corresponded to putting the left foot in, and also the left foot out, not to mention shaking things all about. It’s a shame that image is not up on the arXiv…

You know a talk is good when you start off incredibly skeptical and end up nodding along fervently by the end.

How do dopamine neurons signal prediction error?

Dopamine neurons are known to signal what is called ‘prediction error’: the difference between the expected reward and the received reward. How exactly are they doing it? Neir Eshel recorded from dopamine neurons (I missed where exactly) to expected and unexpected rewards. If you look at the reward vs. spike rate curve, they fit very well to a Hill function. In fact, every neuron they record from looks the same up to some multiplicative scaling factor. That’s a bit surprising to me because I thought there was much more heterogeneity in how, exactly, dopamine neurons respond to rewards…??

But they also find that the response to expected reward for any given neuron is the same Hill function as for the unexpected reward with some constant subtracted. They claim that this is beneficial because it allows even slowly responding neurons to contribute to prediction error without hitting the zero lower bound; I missed the logic of this when scribbling notes, though.

References
Gordon J. Berman, Daniel M. Choi, William Bialek, & Joshua W. Shaevitz (2013). Mapping the structure of drosophilid behavior arXiv arXiv: 1310.4249v1

Weber JN, Peterson BK, & Hoekstra HE (2013). Discrete genetic modules are responsible for complex burrow evolution in Peromyscus mice. Nature, 493 (7432), 402-5 PMID: 23325221
Photo from

# #cosyne14 day 2: what underlies our neural representation of the world?

Now that I’ve been armed with a tiny notepad, I’m being a bit more successful at remembering what I’ve seen. For other days (as they appear): 1, 3, 4

Connectivity and computations

The second day started with a talk by Thomas Mrsic-Flogel motivated by the question of, how does the organization of the cortex give rise to computations? He focused on connectivity between excitatory neurons in layer 2/3 of V1 in mice. Traditionally when we think of these neurons, we think of how they respond to visual stimulation: what patterns of light activity, what shapes or edges are they responding to? This ‘receptive field’ has a characteristic shape and (tends to) respond to certain orientations of edges [see left]. They receive input from more primary visual neurons, but you still want to know: what type of input do they receive from other neurons in the same layer?

By imaging the neurons during behavior and then making posthumous brain slices, they are able to match the direct connectivity with the visual responses. It turns out that the neurons they connect to are most likely to be neurons that respond in a similar way. Yet despite our fetish for connectivityomics, it is not the fact of connections that matter but the strength of those connections. And if you look at the excitatory neurons that are providing input to another postsynaptic neuron, the weighted sum of their response is exactly what the postsynaptic neuron responds to!

So theoretically, if you cut off all the external input to that neuron, it would still respond to the same visual input as before. In fact, if you only use the strongest 12% of the connections, that’s enough to maintain the visual representation. Of course, L2/3 neurons do receive external input so this mechanism is probably for denoising?

Everything’s non-linear Hebb

A long-standing question with a thousand answers is why primary visual neurons respond in the manner that they do (first image above). There have been several theories (most notably from Olshausen (1996) and Bell & Sejnowski (1996)) dealing with sparsity of responses or the fact that these are the optimal independent components of natural images. But the strange fact is that almost everything you do gives these same receptive fields! Why is that? Carlos Stein Naves de Brito (whew) dug into it and found that the commonality to all these algorithms is that they are essentially implementing a non-linear Hebbian learning rule ($\delta w \prop x f(wx)$). One result from ICA is that it doesn’t matter which nonlinearity $f$ you use, because if it doesn’t work you can just use $-f$ and it will… so this is a very nice result. The paper will be well worth reading.

Maximum entropy, minimal assumptions

Elad Schneidman gave his normal talk about using maximum entropy models to understand the neural code. Briefly, there is a class of statistical relationships between observed data that uses as few assumptions about the organization of that data as possible (see also: Ising models). If you use a model that only looks at first-order correlations, ie correlations between pairs of neurons, that’s enough to describe how populations of neurons will respond to white noise.

But it turns out that it’s not enough to describe their response natural stimuli! The correlations induced by these stimuli must trigger fundamentally different computations than the white noise. The model that does work is something they call the reliable interaction model (RIM). It uses few parameters and fits using only the most common patterns (instead of trying to find all orders of correlation, ie correlations between triplets of neurons etc). This fits extremely well which suggests that a high-order interaction network underlies a highly structured neural code.

If you then examine the population responses to stimuli, you’ll find that the brain responds to the same stimulus with different population responses. They’re using this to construct a ‘thesaurus’ of words, in which they find high structure when using the Jensen-Shannon divergence D(p(s|r1),p(s|r2)). What I think they are missing (and are going to miss with their analyses) is a focus on the dynamics. What is the context that in which each synonymous word arises? Why are there so many synonymous words? etc. But it promises to be pretty interesting.

Why so many neurons?

When we measure the response of a population of neurons to something that stimulates them, it often seems like the dimensionality of the stimulus (velocity, orientation, etc) is much, much lower than the number of neurons being used to represent it. So why do we need so many neurons? Is it not more efficient to just use one neuron per dimension?

I didn’t entirely follow the logic of Peiran Gao’s talk (I got distracted by twitter…) but they relate it to the complexity of the task and say that random projection theory predicts how many neurons are needed, which is much  more than the dimensionality of the task.

References

Ganmor E, Segev R, & Schneidman E (2011). Sparse low-order interaction network underlies a highly correlated and learnable neural population code. Proceedings of the National Academy of Sciences of the United States of America, 108 (23), 9679-84 PMID: 21602497