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.


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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.

obamaIn a special session about the BRAIN initiative, a panel of experts reported on the current plans for implementing the BRAIN initiative. The initiative was announced this spring and was described by President Obama as the next great American project. A working group, which includes Bill Newsome, Cori Bargman, Terri Sejnowski and others have been hard at work laying a plan for how to implement the initiative. They came up with a list of recommendations which include goals such as linking neural activity to behavior and integrating theory, modeling and computation. The group also emphasized that there must be a means to disseminate the technologies that are developed as part of the initiative. This is key: through dissemination of technology, the effects of the initiative can reach far beyond the funded labs and impact a larger community of scientists. Geoff Ling feels particularly passionate about these tools. He argues that as neuroscientists, we have no shortage of compelling hypotheses, but that “we are stymied by the tools we have available to test our hypotheses.” I partially agree: good tools are necessary, but we need to think deeply about what the fundamental hypotheses are and how to test them.

Nuo Li from Janelia Farm

Nuo Li from Janelia Farm

Manipulating neural activity and measuring the effect on behavior is a key tool for understanding the function of a structure in the brain. Sometimes, experimenters will manipulate activity in two areas to gain insight into the flow of information through the brain. In a poster this year at SFN, Nuo Li, a postdoc in Karel Svoboda’s lab, took things a big step further by systematically suppressing the activity of 55 locations, each 1 mm wide, in the cortex of individual mice. He achieved this by using a pair of mirror galvos to move a stimulating beam to different places in the brain. In a given session, he could inactivate the parietal cortex on some trials, the somatosensory cortex on other trials and the anterior lateral motor cortex (ALM) on others still. This approach has a few advantages: first, by surveying the cortex broadly, he leaves open the possibility of identifying relevant areas that weren’t even on his radar. Second, the approach avoids a common pitfall of traditional stimulation experiments: in those experiments,  the animal can notice a change in its performance and adapts its strategy. A signature of this nonstationary strategy is usually apparent in control trials. Here, the stimulation causes different effects depending on WHERE its targeted and WHEN in the trial it takes place, making it a challenge for the animal to respond with an altered strategy.

DataSlide_for_AnneThe group found that stimulating the ALM and the barrel cortex had the biggest effects on behavior. Barrel cortex inactivation mattered most when it was presented early in the trial, and ALM inactivation mattered most when it was late in the trial, consistent with the idea that information flows from the sensory area to the motor area over the course of decision formation. An interesting next step would be to uncover what aspect of the animal’s performance was disrupted by the inactivation. For example, interpreting the results would be aided by knowing whether the reduced performance on their task was driven by a change in sensitivity or a change in bias.

Sharon Gobes alongside Sahitya Raja and

Sharon Gobes alongside Sahitya Raja and Pim Chirathivat

In humans, massive changes take place in the brain over the course of language learning in infants. A poster today from Wellesley College, presented by 3 undergraduate students from Sharon Gobes lab, hope to gain insight into this process by studying the brains of juvenile birds around the time they learn their father’s song. In birds, song is known to preferentially activate neurons on the left side of the brain; a leftwards lateralization is likewise seen in humans. The group wondered whether this is an active process: would song natually activate the left hemisphere, even in birds who weren’t exposed to song during development?


Song stimulus (left) and a frequency matched control (right)

Song stimulus (left) and a frequency matched control (right)

To test this, they reared a cohort of birds without exposure to a tutor’s song and measured neural activity in the caudomedial nidopallium, a structure involved in song. In this special cohort, birdsong didn’t have its characteristic effect on the left hemisphere. Instead, both hemispheres responded. Interestingly, a control sound with the same frequency profile activated the birds’ brain in a normal fashion, suggesting that they could process ordinary sounds typically. These results suggest that changes in the brain during vocal learning are driven by exposure to the right stimulus- in this case, the song of a tutor. The take home message? Appropriate developmental environments are necessary, even for innate behaviors.


Temidayo Orederu reports on stress and reversal learning

Temidayo Orederu reports on stress and reversal learning

I attended two interesting posters at tonight’s SFN Diversity Poster Session. Temidayo Orederu, a Hunter college student working Liz Phelp’s lab at NYU, explored the effect of stress on learning. Temidayo is particularly interested in “Reversal learning”: the ability to unlearn an association which once was positive but now is aversive. She brought a large cohort of human subjects into the lab, and examined how a stressful situation affected their reversal learning. She found the the stressed-out subjects were able to learn that a once-positive stimulus was now negative, but NOT that a once-negative stimulus was now positive. This dissociation was surprising and suggests that the two aspects of reversal learning might be mediated by separate circuits, one of which is susceptible to stress. The lesson? Stay calm if you want to learn new contingencies about the world.

Nancy Padilla dissects neural circuits for anxiety

Nancy Padilla dissects neural circuits for anxiety, and her PI Josh Gordon

In another poster, Nancy Padilla explored the neural mechanisms underlying anxiety. She works in Josh Gordon’s lab at Columbia University. Nancy expressed Archaerhodopsin in the axon terminals of vental hippocampal neurons that project to the medial prefrontal cortex. She then examined the behavior of mice in an elevated plus maze with and without optical stimulation that causes the Arch to suppress activity in the terminals. She saw a clear behavioral effect: Animals spent much more time in the open arms of the maze during stimulation, suggesting that activating the hippocampal inputs reduced anxiety, encouraging animals to explore. Seeing a clear behavioral effect from this stimulation is exciting and suggests that the hippocampal inputs play an important role in anxiety.


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