Memory improvement via stimulation of temporal cortex

Stimulation of temporal cortex with electrodes at memory encoding time boosted recall by 15%, in humans.

In the first phase, researchers listened to brain activity while subjects were memorizing nouns. They trained a model to try to predict, based on the brain activity at encoding time, if that word would be remembered or not. In the second phase, researchers ran the model while subjects were memorizing words, and if the model predicted that the word was more than 50% likely to be forgotten, they zapped the brain for 0.5 seconds (through a single pair of adjacent electrodes in the lateral temporal cortex, at amplitudes ranging from 0.5 mA to 1.5 mA (for electrodes deep in the cortex) or 3.5 mA (for the cortical surface) (amplitude was the maximum within this range such that stimulation didn’t appear to cause afterdischarges). Stimulation in this fashion improved recall by 15%. After stimulation, the classifier was more likely to say that the subject would remember the word, which might suggest that the stimulation improved recall by sometimes nudging the brain into a state that the classifier recognized as good for memory encoding.

As an aside, imo one should keep in mind that this doesn’t necessarily mean that this would be a good thing to do every time you are learning something. The way i like to think about this experiment is to imagine that you have some big machine that you don’t know how it works. The machine sometimes makes humming noises and other times it makes sputtering noises. You notice that when it makes the sputtering noise, this correlates somewhat with it not doing its job so well. So, whenever you hear a sputtering noise, you kick it really hard. Sometimes when you kick it, it makes it hum again. You record data and find out that if you kick it when it sputters, that improves output by 15%. That’s very interesting, but does it mean that it’s a good idea to kick the machine whenever it sputters? No — maybe kicking the machine damages it a little (or has some small probability of damaging it sometimes), or maybe the sputtering was something (such as a self-cleaning cycle) that the machine needs to do for its long-term health even at the cost of short-term performance. In other words, there is a clear gain to kicking the machine when it sputters, but it is unknown if there is also a subtle cost.


Youssef Ezzyat, Paul A. Wanda, Deborah F. Levy, Allison Kadel, Ada Aka, Isaac Pedisich, Michael R. Sperling, Ashwini D. Sharan, Bradley C. Lega, Alexis Burks, Robert E. Gross, Cory S. Inman, Barbara C. Jobst, Mark A. Gorenstein, Kathryn A. Davis, Gregory A. Worrell, Michal T. Kucewicz, Joel M. Stein, Richard Gorniak, Sandhitsu R. Das, Daniel S. Rizzuto & Michael J. Kahana. Closed-loop stimulation of temporal cortex rescues functional networks and improves memory.


Topological analysis of population activity in visual cortex

Singh, G., Memoli, F., Ishkhanov, T., Sapiro, G., Carlsson, G., & Ringach, D. L. (2008). Topological analysis of population activity in visual cortex. Journal of Vision, 8(8):11, 1–18,, doi:10.1167/8.8.11

From sparsely sampled data, we can attempt to estimate some of topological structure of the data.

Toplogical structure is here represented by Betti numbers. The paper explains this best:

Consider a world where objects are made of elastic rubber. Two objects are considered equivalent if they can be deformed into each other without tearing the material. If such a transformation between X and Y exists, we say they are topologically equivalent……it is evident that a possible reason for two objects not to be equivalent is that they differ in the number of holes. Thus, simply counting holes can provide a signature for the object at hand. Holes can exist in different dimensions. A one-dimensional hole is exposed when a one-dimensional loop (a closed curve) on the object cannot be deformed into a single point without tearing the loop. If two such loops can be deformed into one another they define the same hole, which should be counted only once. Analogous definitions can be invoked in higher dimensions. For example, a two-dimensional hole is revealed when a closed two-dimensional oriented surface on the object cannot be deformed into a single point.

This notion of counting holes of different dimensions is formalized by the definition of Betti numbers. The Betti numbers of an object X can be arranged in a sequence, b ( X )=( b 0 , b 1 , b 2 , I ), where b 0 represents the number of connected components, b 1 represents the number of one- dimensional holes, b 2 the number of two-dimensional holes, and so forth. An important property of Betti sequences is that if two objects are topologically equiv- alent (they can be deformed into each other) they share the same Betti sequence. One must note, as we will shortly illustrate, that the reverse is not always true: two objects can be different but have the same Betti sequence.

A technique is presented for estimating the Betti numbers of sampled data using “Rips complexes” and “barcodes”. To put this technique to use on neural data, the spiking of 5 cells (mostly “complex cells in the superficial layers”) with high spontaineous rate in V1 in Macaques were recorded from. The spikes were binned and a point cloud in 5D was constructed (so i think the coordinates of the point cloud representing the spike rate in each of the 5 dimensions).

This was done in two experimental conditions, when a stimulus was being presented, and when the eyes were occluded. In both cases, the topological structure varied between a circle and a sphere, although the circle structure was found with higher probability in the stimulus condition. The authors present a model of circular structure generated “if cortical activity is dominated by neuronal responses to stimulus orientation”, and a model of toroidal structure generated “A toroidal representation may arise from a neuronal population responding to two circular variables, such as orientation and color hue”. Note that a torus wasn’t actually observed in the data; a circle and a sphere was. In the conclusions the authors speculate what could have caused the sphere.

The authors conclude that the topology of spiking patterns for “both the data for spontaneous and driven conditions have similar topological structures, with the signatures of the circle and the sphere dominating the results”.

Local sleep in awake rats

this experiment claims to show that

(1) when rats are sleep-deprived, small populations of rat brain neurons can fall asleep while the rest of the rat is awake, and
(2) this may correspond to performance degradation



i haven’t read the actual article yet…

IBM Cat Brain Simulation Scuffle: Symbolic?

You’ve probably read by now about the announcement by IBM’s Cognitive Computing group that they had created a “computer system that simulates and emulates the brain’s abilities for sensation, perception, action, interaction and cognition” at the “scale of a cat cortex”.    For their work, the IBM team led by Dharmendra Modha was awarded the ACM Gordon Bell prize, which recognizes “outstanding achievement in high-performance computing”.

A few days later, Henry Markram, leader of the Blue Brain Project at EPFL, sent off an e-mail to IBM CTO Bernard Meyerson harshly criticizing the IBM press release, and cc’ed several reporters. This brought a spate of shock media into the usually placid arena of computational neuroscience reporting, with headlines such as “IBM’s cat-brain sim a ‘scam,’ says Swiss boffin: Neuroscientist hairs on end”, and “Meow! IBM cat brain simulation dissed as ‘hoax’ by rival scientist”.  One reporter chose to highlight the rivalry as cat versus rat, using the different animal model choice of the two researchers as a theme.  Since then, additional criticisms from Markram have appeared online.

Find out more after the jump.

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Henry Markram on TED – video online

We had read that Dr. Henry Markram of the Blue Brain project had given a talk at TED (technology, entertainment, design), but the video wasn’t released until this month.  This talk is geared towards a general audience, rather than getting into the specific details of the Blue Brain project, as he has before.  It is engaging and includes many suggestions towards the future of neuroscience and AI.

Watch it online at the TED website.