I have just returned from a week-long Workshop on the Dynamic Brain, led by Adrienne Fairhall (University of Washington) and Christof Koch (Allen Institute for Brain Science). The course brought in students from around the country including graduate programs at the University of Washington, UCSD and the University of Michigan. Lectures brought students up to speed on emerging approaches for understanding brain function, especially those using tools developed by the Allen Institute. For my part, I gave 2 lectures and then took advantage of the opportunity to learn about the ins and outs of the tools from some of the local experts, most notably Lydia Ng who is my new hero.

I already had some experience with the Allen Brain Connectivity Atlas, but its new developments really blew me away. The atlas is based on systematic injections of AAV across cortical and subcortical structures. One feature I particularly liked is the ability to visualize injections not just based on the injection location, but based on a target location for which the user wants to know all the inputs (this is called a “spatial search”). I did such a search for the posterior parietal region. Because this approach allowed me to see all the areas into which injections led to parietal label, it is kind of equivalent to seeing a retrograde injection from the posterior parietal cortex. As the image below shows, there is clear label in visual areas (the blue dots at the posterior region of the gray brain, one of which is shown in more detail at the right). Injections to secondary motor and orbital areas (the green dots at the anterior region) likewise innervate the posterior parietal area. Being able to easily visualize many injections from many different vantage points gives a much clearer pictures of the overall connectivity, and the tools are really fun to play around with.


leilaLeila Elabbady (Wellesley College) worked in Josh Dubnau’s lab where they are using fruit flies to understand neurodegeneration. An emerging hypothesis is that increased transposon activity may play a role in neurodegeneration. Transposons are repetitive strands of DNA that can copy themselves and insert themselves elsewhere in the genome. These were first discovered in corn, here at Cold Spring harbor by Barbara McClintock who later got the nobel prize for her work. Transposons can be dangerous: they can alter the transcription of other, potentially important genes. Leila’s work this summer focussed on TDP-43, a DNA/RNA binding protein that might keep transposon activity in check. She tested whether manipulating the fly homolog of TDP-43 affected transposon activity in flies. A key part of this approach, and also what makes it challenging, is that Leila monitored transposon activity at multiple stages in the flies’ lives. Identifying how TDP-43 affects this progression will be key for testing the hypothesis about its role in neurodegeneration.

nikaelaNikalea Bryan (University of Maryland, Baltimore County) also worked in my lab. She was likewise interested in the timing of decision formation in the cortex, but wanted to get at the issue by manipulating inhibitory interneurons. These neurons are plentiful in the cortex and the sub-type she was interested in, parvalbumin positive (PV) interneurons, strongly innervate excitatory pyramidal cells. Upregulating the PV neurons therefore can shut down the ability of the cortex to communicate information to downstream areas, a powerful tool. Nikaela also thought deeply about training procedures and how to tweak them to get the best performance possible.

IMG_9161Today marked the last meeting of our summer long “Gilbert Club”, a group that gathers weekly to discuss the Gilbert Strang lectures available on MIT Open Courseware. Most folks who have taken linear algebra know and love the Strang textbook- his lectures will also not disappoint. We gathered after watching each lecture in the hopes of using it as a jupming-off point for developing new techniques to interpret neural data from both electrophysiological recordings and imaging. The challenge is that the temporal precision and noise can differ greatly across these two methods for measuring neural activity, such as electrophysiology and imaging. Techniques discussed include regression, dimensionality reduction and image processing. The club was led by Matt Kaufman, a postdoc in my lab who has been a major player in bringing new analysis techniques to neural data. Attendees included students and postdocs from my lab as well as the Albeanu, Koulakov and Zador labs.

One clear outcome of the club is that scientists here working on different problems now have a common language for discussing the data. My hope is that this could constitute a first step in generating not just a common language, but a common data format and a common set of analysis tools as well. The opportunity to share data and analyses easily would make each of our individual efforts go much further, and could help to unify broad approaches here at CSHL.

Being able to navigate in the world requires a stable representation of space. A key part of the neural substrate supporting this ability is the entorhinal cortex, where individual cells’ responses constitute a grid that tiles the space being explored. ilafiete

The existence of such cells has been known for a while, and certainly they seem reasonable for the task at hand, but it has been an ongoing challenge to understand what kind of neural machinery would give rise to them. Ila Fiete, from UT Austin, has been tackling this problem from a theoretical point of view. I heard her give a talk at a recent meeting organized by the McKnight Foundation, which funds systems neuroscientists working at the molecular, cellular, systems and theoretical level.

Ila’s idea is that the grid cells reflect a stable 2-dimensional manifold driven by continuous attractors (see left panel of figure, below). The gist is that short-range excitatory and long-range inhibitory connections give rise to stable “bumps” of activity. This kind of mechanism has been put forth previously in the visual and oculomotor systems. Here, Ila proposes that the same continuous attractors might be used by entorhinal cortex, there to drive the individual nodes of activity of the grid cells. This model predicts some specific features of the resulting population: for example, the model predicts that even though the absolute phase of individual neurons might change a bit over time the relative phase of the neurons to each other should be fixed. This prediction is born out by real measurements of grid cells: their phase can change over time, but their relative phase is extremely stable. A second prediction is that tuning curves of all the neurons will be stereotyped, a prediction that is again born out by the data.

A continuous attractor, some grid cells, and some measurements about them

A continuous attractor, some grid cells, and some measurements about them

This work presents a challenge to alternative explanations for grid cells, such as that they are driven by oscillations in the cortex. To my mind, a key next step will be to manipulate the circuit, perhaps by suppressing the activity of interneurons which play a key role, and examining the effects on the phase of grid cells.

image description We hosted a micro-conference today about mouse visual areas and how to understand them. It is an exciting time to be thinking about this because recent tools make it possible to visualize lots of visual areas at the same time: for example, using intrinsic optical imaging and the right stimulus, you can identify, in an awake animal, multiple visual areas and use this information to tell you where to place your electrode or where to point your microscope. A more traditional way of figuring that out is to go by the published coordinates that define an area, sometimes garnered by cytoarchitecture or inputs. The problem with this traditional approach is that there can be variability across animals; being able to pinpoint an area in the unique brain of an individual animal is a huge advantage. We wanted to take the classic literature, which often defines areas based on thalamic input, with the emerging literature, defining areas based on functional responses. And most of all, we wanted to get in register the literature about the posterior parietal cortex and the literature about secondary visual areas. IMG_8609The functional approach generates beautiful maps that clearly show the existence of multiple areas (see figure above; it’s from a recent paper by Manavu Tohmi in Current Biology). The basic functional properties of these areas are beginning to be defined, but much is still mysterious about how they relate to primate visual areas, and how they guide behavior.

We were happy to be joined by two colleagues from Columbia: Mehdi Sanayei and Naomi Odean, both from Mike Shadlen’s lab. Like us, they are interested in thinking not just about the flow of information through cortical areas, but also in understanding the role of subcortical structures, especially the thalamus.


photo 1

I brought together a team of CSHL scientists this week to visit a local elementary school in honor of Brain Awareness Week. We taught 50 second graders and 50 fourth graders about the structure and function of the nervous system. Each scientist talked about their work, highlighted a particular structure and described its function. The students heard from Francesca Anselmi, Brittany Cazakoff, Lital Chartarifsky and Balazs Hangya.

One aspect of our presentation that intrigued students was how photo 2-1many mysteries there still are about the brain. For instance, why is the occipital lobe at the back of the brain when the eyes are at the front? Why do we dream when we sleep? Why is it so hard to treat brain disorders like stroke and epilepsy? Their curiosity was inspiring and their enthusiasm was infectious. Communicating science to a broad audience always renews my curiosity about the brain, and makes me feel lucky to have a lab with the tools to dig deep and address some of the many still unanswered questions.

hasana It is exciting and a little sad to see Watson school students leave for new endeavors. A graduate student from Tony Zador’s lab, Hasana Oyibo, is heading to the FMI in Switzerland to work in Georg Keller’s lab. The Keller Lab has highlighted “mismatch cells” in the visual cortex that respond when a visual stimulus doesn’t match an expected motor output. The existence of these cells is now well-documented, but much remains mysterious about how they acquire their properties. They don’t seem to be innate so something about their visual and movement experiences must enable the circuit that drives their responses. Hasana, an expert in neural circuits, is well-positioned to weigh in on this problem. We will miss her in the Marks Building where her intellectual insights and awesome molecular tools have been of great value to the community.

I am writing this week from the Cosyne conference in Salt Lake City. I gave a talk this morning about mixed selectivity in parietal cortex and have also heard great talks from a number of other labs.
Our brains are wired to detect auditory stimuli that are important and might be relevant for behavior. A signature of this is an effect called “Stimulus specific adaptation” or, SSA, a phenomenon in which neural responses to unusual or “deviant” stimuli are larger compared to repeating stimuli (think: beep beep book beep). SSA has been established for some time, but the underlying neural circuits that drive it have remained mysterious. Recent work from Ryan Natan in Maria Geffen’s lab at the University of Pennsylvania tackles this issue. Maria and Ryan took advantage of new tools that make it possible to specifically up and photo-9 down regulate inhibitory neurons and look at the effect on firing rates of excitatory cells known to show SSA. They used this approach to evaluate the role of two classes of inhibitory neurons: PV and SOM interneurons.They found that inhibitory either class of neurons interfered with the SSA: following their manipulation, deviant auditory stimuli no longer “popped out” the way they normally do. By carefully comparing the effect of each manipulation on responses to both standard and deviant tones, they revealed that both interneuron classes drive the affect, but in complementary ways.

I blogged repeatedly during the recent Society for Neuroscience Meeting about posters and presentations from other labs. This was great fun as there was a lot of terrific science presented. However, this post will take a different angle: I’ll highlight what my lab presented at the meeting.

David Raposo, John Sheppard and Matt Kaufman:

Data baseball card

Data baseball card

The three posters collectively made the point that our use of multisensory stimuli exposed an unexpected computational strategy for neurons in the posterior parietal cortex. Despite the fact that the 3 posters made this point together, they were stationed in separate sessions! Undaunted by this problem, the guys manufactured “data baseball cards” (see right) that briefly outlined each poster. Each presenter could hand out the baseball cards of the other presenter as needed; for example, if a poster attendee wondered about an issue that was presented in a different session. Although we designed the cards to ease the burden of connected posters in different sessions, they became a huge hit! The guys’ collections were depleted almost immediately- if you want one, maybe they will turn up for auction on eBay??

Onyekachi Odoemene: Kachi’s poster described some work that is at an early stage but is very exciting. He has been working on developing decision-making behavior in mice. His poster described early efforts to determine which structures are required for these decisions. Keep a look out for Kachi next year: we joke in the lab that whenever we think of an innovative idea, it turns out Kachi already thought of it, built the apparatus to test it, and has the data in a power point presentation.

Amanda Brown: Amanda presented work alongside Ingmar Kanitschneider, a postdoc in Alex Pouget’s lab with whom we have an ongoing collaboration. Their poster described human behavioral data about a new version of our multisensory decision task. In this version, the stimulus is configured so that subjects must make a multisensory estimate of the number of events (as opposed to the rate of those events, which is what animals do in our usual task). Their poster was very busy so they got to spread the word about their new view of probabilistic number representation.

We finished the meeting off with anentertaining lab dinner at a local restaurant. We were joined by some outside collaborators, and some internal collaborators as well, including Ashlan Reid.


All in all the meeting was a big success. Lab members got the word out about a bunch of new observations we have made, and returned to Cold Spring Harbor overflowing with ideas for new experiments and analyses. These new directions will keep us busy- stay tuned for more updates in the coming months.

Neurons across the cortex differ considerably in the degree to which they exhibit persistent activity. Neurons in frontal areas might fire persistently for seconds even in the absence of a sensory stimulus, while neurons in early visual cortex (V1) are more tightly linked to incoming sensory input. Does this tight linking arise because V1 circuits simply lack the features that allow persistent activity, or might the tight linking arise as the result of an active process?

photoAmyAn intriguing poster from Kim Reinhold in Massimo Scanziani’s lab suggests the latter. She has been running experiments to determine the timescale over which cortical activity changes when she removes thalamic inputs (via an optogenetic strategy). She finds that the time constant for the decay of activity is super-fast: about 9 ms. Given that 9 ms is around the membrane time constant of the cell, it would seem at first that the membrane properties of individual cells define the time constant of persistent activity for the whole area. But the plot thickens: when Kim squelched cortical inhibition, the time constant got considerably longer. This observation suggests that inhibitory neurons actively squelch cortical responses thereby preventing persistent activity. Why might this be? Kim reasons that fast-acting inhibition would ensure that the visual cortex was always at-the-ready for new incoming stimuli. This suggests a tradeoff between the ability to maintain a persistent response, and the ability to response with high temporal resolution.


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