Joshua Sanes: Regulation of Retinal Repulsion (and Rainbows)

This week, the Dart Neurosciences Seminar hosts Joshua Sanes, a professor of molecular and cellular biology at Harvard. Dr. Sanes’ scientific interests revolve around key fundamental questions in systems neuroscience: How do circuits form in development, how do they change over maturity, and how do they function to process signals in adulthood? To this end, Dr. Sanes has spent many years probing the retina as a model for the rest of the brain. In particular, he has been a pioneer in identifying retinal subtypes, probing their connectivity dynamics, and manipulating different parts of the circuit to evaluate their importance.

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Recently, Dr. Sanes and his lab members have focused on molecules that play in role in forming these synaptic connections- namely the cadherin superfamily. It has been known that in the retina, cells of the same subtype are spaced apart such that few neighboring cells are of the same subtype. However, a mechanism for the formation of such a mosaic was unknown. In a 2012 Nature paper, Jeremy Nay, Monica Chu, and Joshua Sanes identified two transmembrane proteins found in a subset of retinal cells that regulate positioning of Starburst Amacrine Cells (SACs) and Horizontal Cells (HCs) during development. In this paper, the group showed that these two proteins (MEGF10 and MEGF11) act as repulsive ligands, where cells that express these proteins will repel other cells expressing the same protein. They further showed that the protein is required as part of both the ligand and receptor sides, a homotypic interaction. This results in ‘exclusion zones’ around a particular cell, creating the mosaic spacing seen between cells of the same subtype in the retina. While the previous hypothesis was that each subtype had a unique ligand-receptor signal, these results show how two proteins work cooperatively to create a repulsive mosaic for horizontal cells, and how one of those proteins (MEGF10) also acts on SACs. As these two cell types are in different layers, it opens up the possibility that these mosaic ligands are layer specific, reducing the necessary diversity to create a structures, mosaic map in the retina.

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What is the importance of elucidating these mechanisms? As Dr. Sanes mentions frequently, the retina is an easy-to-access proxy for other brain areas. There are numerous examples of neuronal arrays throughout the CNS. This first evidence of homotyptic repulsion could lead to an understanding how uniformity is established in the brain.

Since the 1970s, Dr. Sanes has approached the question of neural synapses in different ways. From neuromuscular junctions in frogs, to discovering and cloning the protein lamin B2, he has tried to explore the molecules that form the junction, and how disturbing the system affects the synapse. With Jeff Lichtman, an expert in live imaging, Dr. Sanes hopes to continue probing the nature of cell-to-cell connections and synapse formation and change over time. In recent years, this duo has gained fame for the creation of ‘brainbow’ mice, lines of transgenic mice that express varying amounts of three fluorophores in each cell, creating a veritable rainbow of colors that allows visual separation of each cell and its axon from neighboring cells. The entire field of neuroscience will benefit as Dr. Sanes continues to push boundaries in the fields of development of retinal architecture, synapse formation, and circuit evolution.


If you’d like to find out more about these studies, come listen to Dr. Joshua Sanes on Tuesday, March 3rd in the Center for Neural Circuits and Behavior Marylin C. Farquhar Conference Room!

(Sahil Shah is an M.D./Ph.D. student in the lab of Jeffrey Goldberg. He is studying protein synthesis and transport in the retinal ganglion cell as it relates to aging and disease.)

Kay, J. N., Chu, M. W., & Sanes, J. R. (2012). MEGF10 and MEGF11 mediate homotypic interactions required for mosaic spacing of retinal neurons. Nature483(7390), 465-469.

Dr. Naoshige Uchida: Different Types for Different Likes

Listen to the post and follow along!

Who makes decisions?

How do we decide?

To run in fear, or hide?

Reward-based circuitry must be,

A part of our machinery!

Dr. Uchida has a lab,

At Harvard, you know, Boston, Mass,

They dive into the mouse’s brain,

Specifically the VTA.

If acronyms are scaring ya:

Ventral tegmental area.

Let’s take a look at what they did,

Examine their experiments.


At first they taught a mouse to tell,

Apart rewards based just on smell.

Sometimes a big reward, some small,

Sometimes no water came at all.

Sometimes an air puff to the face,

The mouse must differentiate.

It does – and licks more when it knows,

That water through lick port will flow.

Uchida Figure 1

They then recorded from some cells,

To see if they could really tell,

Which ones respond to which CS,

And which prefer reward the best.

Which cells might fire differently,

In groups or independently,

And their results – it’s no surprise,

Were elegant, I’ll summarize:

They clustered cells in groups, you see,

Type I, type II, and even III.

The types have different properties,

And different n-t-m release.

Type I spikes to rewarding cue,

More tonically spike cells Type II,

Type III cells are more limited,

And seem to be inhibited.

Uchida Figure 2

They optogened some channel rho,

To see how clean their data shows,

Each type of cell can be predicted,

By the n-t-m ejected.

And indeed results were clean,

Type I cells release dopamine,

And type II cells release the massive,

Gamma-Aminobutyric acid.

Uchida Figure 3

Wonderful! So now it’s known,

Each cell has functions of its own,

“And now,” the lab group said, “let’s show,

How they predictive error code!”

“Let’s signal that a big reward,

Is on its way, but just before,

The water comes we’ll just be fronting,

One of ten times the mouse gets nothing!”

“Oh no!” announced the type I cells,

“There’s no reward, dude, what the hell?!”

After the US time was finished,

They saw type I was quite diminished.

In contrast type II neurons went,

So crazy during punishment.

Rewarding stim plus puff of air,

The type II neurons said “Oh yeah!”

Uchida Figure 4

So now we know more things about,

Reward-based circuits in the mouse,

The push and pull of different types,

The dopamine and GABA life.

Come listen to Uchida speak,

He’ll have the answers that you seek.

On these experiments, and more,

Tuesday, CNCB, at four!


Uri Magaram is a first year graduate student in the UCSD Neuroscience program.  He is currently rotating in the lab of Jeff Isaacson.


Cohen JY, Haesler S, Vong L, Lowell BB, Uchida N. Neuron-type-specific signals for reward and punishment in the ventral tegmental area. Nature. 2012 Jan 18;482(7383):85-8. doi: 10.1038/nature10754.

Dr. Garret Stuber: Driving emotions with light

Why do we do what we do? What agent drives our decision-making? What motivates one person to work hard and another to stay in bed all day? These questions have no simple answers (if any at all), but neuroscience researchers believe they can access the underpinnings of motivated and reward-seeking behavior by studying the neural circuits that drive such behaviors. The lab of Dr. Garret D. Stuber at University of North Carolina at Chapel Hill does just that.

GStubFig. 0 | Dr. Garret D. Stuber with electrophysiology rig

Using techniques in-vitro (after an animal has been sacrificed for study) and in-vivo (while the animal is alive and behaving), scientists in the Stuber lab try to identify and isolate neural circuits that guide behavior in health and disease. In this post, we will look at a recent publication examining a particular circuit implicated in integrating a range of opposing emotional states and guiding behavior. Specifically, the authors focus on neuronal projections from the bed nucleus of the stria terminalis (BNST) to the ventral tegmental area (VTA), by first examining the electophysiological properties of this circuit, and then manipulating the circuit in-vivo to confirm that their predictions about its function can be observed in an awake, behaving mouse.

The BNST is considered to be a part of the extended amygdala. Many people readily recognize the amygdala as an important brain center for emotional processing (in fear and anxiety) and, naturally, decision making related to those emotions. The VTA is a group of neurons that include dopaminergic projections to various brain regions, and is functionally implicated in both reward and aversion. Thus, studying the connections between the BNST and the VTA could shed light on the neural substrates for integrating differing emotional states and driving anxiety-related behavior. Elucidating these mechanisms could have marked implications for diseases such as addiction, anxiety, and depression.

Since neurons in the brain can project to many different areas, the authors first had to ensure that they could identify neurons that reside in the BNST (particularly the ventral BNST: vBNST) and project to the VTA. To do this, the authors used a technique called optogenetics, wherein special ion channels that can be activated by light are expressed in neurons of interest. To ensure and categorize functional connections between the two areas, the authors used a clever trick: they stimulated, sequentially, with light, the cell bodies in the vBNST and their axon terminals in the VTA while recording from the vBNST. Because action potentials have no directional preference, but cannot travel through sections of axon that are in a refractory period (having just propagated an action potential), functional connections stimulated under precise conditions would induce “action potential collision.” The recording schematic is illustrated in Fig 1b, with an example recording in Fig 1c, and Fig 1d shows the attenuation of the antidromic spike (propagating backwards along the axon), indicating “spike collision”.

Figure 1 Stubber

Because the behavior of neurons projecting from vBNST to VTA is not homogenous, the authors next separated the population into a subset of excitatory projecting neurons (glutamatergic) and inhibitory projecting neurons (GABA-ergic). They did this by using promoters for Vglut2 or Vgat, which express differentially in the two populations, and recorded EPSCs and IPSCs in VTA neurons, respectively. To examine the significance of these two populations, the authors recorded from each type of neuron during aversive foot-shock stimuli and found that excitatory glutamatergic neurons increased their firing during foot shocks and subsequent relevant cues, while inhibitory neurons decreased their firing to aversive foot shock (Jennings et al, 2013).

To test whether this observed pathway would be sufficient to drive anxiety-related behaviors, the scientists next activated these subsets of neurons in-vivo. By expressing ChR2 in excitatory vBNST neurons, they could stimulate the vBNST-VTA pathway with light pulses when the mouse was in a specific context, in this case one of two chambers. They observed that mice significantly avoided the chamber paired with the photostimulation, indicating an anxiety-like response. To corroborate this observation, an injection of DNQX, which blocks glutamate activity, abolished this avoidance. Furthermore, mice that were photostimulated in an open-field test spent significantly more time in corners of the observation table versus control mice (Fig 4, Jennings et al 2013).

Figure 4 Stubber

As activating the excitatory pathway to VTA resulted in avoidance and anxiety-like behavior, the scientists also tested whether there would be behavioral consequence to activating the analogous inhibitory pathway. Indeed, stimulating the inhibitory pathway resulted in significant place preference to the chamber that was paired with the stimulation; again, this result was abolished by GABAzine, a GABA inhibitor. Mice even nose poked to receive the photostimulation, indicating a reward-related behavior. In fact, this pathway was successful in alleviating anxiety-related freezing produced by aversive foot shocks, providing strong evidence that these vBNST-VTA neurons are strongly related to anxiety or anxiety buffering (Jennings et al 2013).

This study by Dr. Stuber’s lab provides strong evidence for a neural pathway that regulates anxiety and reward-seeking behavior. Identifying these pathways could be a promising lead towards identifying physiological disruptions that cause anxiety, depression, and addiction. If you’d like to find out more about these experiments (and perhaps other, even more recent studies), come listen to Dr. Garret D. Stuber on Tuesday, February 3 in the Center for Neural Circuits and Behavior Marylin C. Farquhar Conference Room!

Uri Magaram is a first year graduate student in the UCSD Neuroscience program.

Jennings, J. H., Sparta, D. R., Stamatakis, A. M., Ung, R. L., Pleil, K. E., Kash, T. L., & Stuber, G. D. (2013). Distinct extended amygdala circuits for divergent motivational states. Nature496(7444), 224-228. doi:10.1038/nature12041

Same diagnosis, different working brains: Rethinking functional connectivity in autism

In recent years, there has been an explosion of interest in the neuroscience of autism spectrum disorder (ASD), and much of this research has targeted how activity in the autistic brain differs from that in the neurotypical brain. However, delving into this literature can be frustrating and immensely confusing; a quick PubMed search for “autism functional connectivity” returns over 300 results with titles describing connectivity in ASD in frequently contradictory terms such as “altered”, “preserved”, “disrupted”, “reduced”, “hyper”, etc. This is somewhat unsurprising given the well-known heterogeneity of ASD(s), but particularly for the purposes of improved diagnoses, interventions and general understanding of the disorder, it would be advantageous to be able to parse out some core characteristic(s) of the disorder.

As chance (and luck) would have it, this week’s speaker, Dr. Marlene Behrmann from Carnegie Mellon University, has been making quite a few headlines in the last week for doing just that. In a paper published in Nature Neuroscience on Jan. 19th, she and her co-authors propose that rather than a particular pattern of over- or under-connectivity being a distinguishing feature of autism, a core characteristic of ASD is having an “idiosyncratic brain”. In other words, whereas neurotypical adults tend to display relatively similar and stereotyped patterns of functional connectivity, adults with ASD deviate from this pattern in an inconsistent, “idiosyncratic” way.

What led Dr. Behrmann and her co-authors to this conclusion was an initial observation that compared to control brains, their ASD group as a whole demonstrated what the authors call a “regression to the mean” effect. This observation came from an analysis of resting-state fMRI scans (in which BOLD signal changes are measured in the absence of a task and taken as an indication of spontaneous activity, revealing functional networks through correlated patterns of activation) from five different datasets with a total of 73 controls and 68 ASD subjects. In areas of the brain with highly correlated activity between hemispheres (“homotopic interhemispheric connectivity”) in the control group, the ASD group demonstrated reduced connectivity; but where there was less interhemispheric connectivity in the control group, there was comparatively increased connectivity in the ASD group.

Although there are a number of possible explanations for this phenomenon, the one which Dr. Behrmann and her co-authors’ data best support is that there is substantial variance within the ASD group in terms of their patterns of functional connectivity, whereas control subjects are more similar to each other. The following schematic figure from their paper illustrates this nicely:


Figure 1: A schematic of how “regression to the mean” in ASD could arise from spatial distortions of interhemispheric functional connectivity

As one can see, when three hypothetical ASD subjects each have areas of strong (yellow/orange) and weaker (blue) interhemispheric connectivity but these areas are spatially inconsistent, their group average would be attenuated in each direction toward a mean level of connectivity.

And in fact, this is precisely what Dr. Behrmann and her co-authors observe. In each of their five datasets, individual control subjects’ patterns of homotopic interhemispheric connectivity are more correlated with each other (as demonstrated by more red/yellow in Figure 2a) whereas ASD subjects seem to be less correlated with each other (more blue; quantified in Figure 2b).


Figure 2: In all 5 datasets, there was greater inter-subject similarity in patterns of interhemispheric functional connectivity in the control than in the ASD group

Interestingly, the degree of autistic subjects’ deviation from the control group’s mean connectivity pattern was modestly correlated with behavioral measures of ASD as indicated by Autism Diagnostic Observation Schedule (ADOS) scores. In particular, there were significant correlations between total ADOS or ADOS communication scores and distortion index (i.e., the within-subject variance of interhemispheric connectivity differences) such that subjects with more severe symptoms had more idiosyncratic connectivity profiles. Moreover, they found similarly variable patterns of functional connectivity that deviated from the more stereotyped control patterns when they also looked at heterotopic interhemispheric connectivity (connectivity between different regions across hemispheres) and intrahemispheric connectivity in each hemisphere, although these connectivity deviation measures did not correlate as strongly with the behavioral measures. Nevertheless, Dr. Behrmann and colleagues’ data support their proposal of “functional idiosyncrasy” as a signature of individuals with ASD.

At first, one might say, “Ok, so functional connectivity in autism varies by individual; that seems obvious, what’s the big deal?”. I would argue that a major implication of this study is that it provides a new framework for thinking about functional connectivity in autism. Not only does it help clarify many of the discrepancies in previous studies’ findings, it helps guide future studies by focusing not only on how two groups (i.e., ASD and control) are different from each other but also on how the within-group variances differ between groups. Instead of the highly variable nature of ASD being merely a nuisance for statistical tests, it is precisely the variability in deviation from the “control template brain” which sets it apart. This also raises some interesting questions for how “canonical” vs. “idiosyncratic” connectivity profiles develop. For instance, the authors suggest that the functional idiosyncrasies in autism could arise (at least in part) from autistic individuals’ abnormal interactions with their environment throughout development, whereas neurotypical individuals who (presumably) interact with their environment in a more typical way would have similar functional connectivity patterns reinforced. This is an interesting hypothesis that could be addressed by looking for similarly variable and deviant connectivity patterns in some other clinical populations.

This is likely to be the primary topic of Dr. Behrmann’s lecture given this paper’s recent publication and the fact that her talk is titled “Alterations in canonical cortical computations in Autism”. However, Dr. Behrmann has been extremely influential in other realms of cognitive neuroscience research as well. She also studies aspects of complex visual processing such as object recognition, mental imagery, spatial attention, face processing and more, and employs a wide range of methods including behavioral measures in brain-damaged patients, functional neuroimaging, and computational modeling (you can find links to her MANY publications on her website – definitely worth the read!). Dr. Behrmann is both a wonderful speaker and a wonderful scientist, so don’t miss her on Tuesday, January 27th at 4pm in the Center for Neural Circuits and Behavior Marilyn C. Farquhar Conference Room!

Megan Kirchgessner is a first-year Neurosciences graduate student currently rotating with Dr. Eric Halgren. When not in lab or in class, she is probably running around the Torrey Pines Gliderport and getting distracted by the sunset, listening to weird music and pretending to be indie, eating excessive amounts of carbs, alternating between watching independent films and Will Farrell comedies, or stalking the La Jolla Cove sea lions.

Hahamy, A., Behrmann, M., & Malach, R. (2015). The idiosyncratic brain: distortion of spontaneous connectivity patterns in autism spectrum disorder. Nature Neuroscience. Advanced Online Publication. doi:10.1038/nn.3919

Yeast: good for beer, pizza dough, and drug development?

Dr. Lindquist’s Lab, using yeast, has taken an innovative approach to study and develop treatments for synucleinopathies, which are diseases that exhibit the toxic buildup of α−synuclein protein. These diseases include Parkinson’s Disease and dementia with Lewy bodies, which have increased in prevalence as our population ages. The use of yeast as a model to look at the underlying genetics of the pathology has several benefits: 1) The biological processes involving α−synuclein are evolutionary conserved from yeast all the way to humans 2) The ability to do whole genome unbiased screens in a simple organism with a uniform genetic and epigenetic population 3) The lack of confounding factors such as different cell types and cell to cell connections 4) The ability to test many small molecules quickly and determine their mechanism of action. To be clear, Dr. Lindquist is not claiming to be modeling human disease, but rather using the genetic and molecular tools in yeast to more quickly and effectively bring treatments back into human neural models.

The Lindquist lab has recently published a paper in Science (Tardiff et al., 2013) that uses yeast to investigate the genetic underpinnings of synucleinopathies. The authors first used an unbiased screen, a method that does not attempt to target a particular pathway or protein but rather looks at cellular level readouts such as cell death or cell growth to find molecules that are cyto-protective in yeast. The authors then figured out the mechanisms of action using the powerful genetic tools of yeast. The drug of focus here was NAB2, which had been previously shown to prevent α−synuclein toxicity in other models. Because NAB2 in yeast slows growth, this phenotype was used to screen for mutants that were unaffected by the drug. This screen implicated 12 closely related genes. Subsequently after knocking these genes out one at a time, Rsp5 (NEDD4 in human) was deemed the target for NAB2. The drug was then tested in yeast expressing human α−synuclein, which demonstrated NAB2 could rescue α−synuclein expressing yeast, but could not rescue a Rsp5 mutant yeast strain from α−synuclein toxicity. Thus the authors confirmed Rsp5 as the target of NAB2 (see figure 1). The Lindquist lab further highlighted the cellular mechanisms that this approach uncovered. The α−synuclein protein over expression seems to disrupt normal vesicle trafficking, while NAB2 through Rsp5 rebalances the trafficking system. This mechanistic understanding gives the authors a new genetic node to target drug therapies towards.

Lindquist Fig 1

 Figure 1: Schematic showing the use of yeast genetic tools to uncover the mechanism of action and cellular targets for the drug NAB2 A) The node of genes which interact with NAB2 B) NAB2 native function in WT cells is linked to Rsp5 C) NAB2 blocks α−synuclein toxicity with WT Rsp5 but not in Rsp5 mutant cells thus specifically implicating Rsp5 as a target of NAB2

The power of this system is demonstrated in a second paper from the Lindquist group (Chung et al., 2013), which takes the information learned in yeast and applies it to human cell lines from PD (Parkinson’s disease) patients. Using stem cell technology which allows researchers to take somatic cells from patients and re-differentiate them into neurons, the Lindquist group used the induced pluripotent stem cells (ipSc) to test drugs and corroborate mechanisms found in yeast. These ipSc contained specific mutations to α−synuclein, which could then be corrected using CRISPR in sister cell lines. Thus each cell line used had its own specific control with only the one gene of interest mutated. The α−synuclein mutant PD cell line and the edited control line could then be compared. This method picked up on two interesting facets of α−synuclein toxicity, which had previously been seen in the yeast: 1) nitrosative stress caused by increasing nitration and 2) increased accumulation of proteins normally degraded by ERAD (ER-acssociated protein degradation). This confirmed that the yeast model of α−synuclein toxicity was legitimate and that these mechanisms were conserved from yeast to human. Finally, the authors were able to test NAB2, the drug previously tested in yeast, in the ipSc. The Lindquist Lab found that α−synuclein toxicity was prevented. Thus “closing the loop” between the two model systems and confirming their drug discovery methodology.

The Lindquist lab’s work is needless to say extremely pertinent to our ability to better discover drugs for treatment of neurodegenerative diseases. Combining model systems and using yeast allows for a potentially more effective and hopefully faster way to get small molecules into the clinic. So as you continue to shovel copious amounts of pizza and swill your beer watching the forthcoming Super Bowl, appreciate the little yeast that make everything so tasty, and could be helping us understand and treat α−synuclein related diseases.

Marc Marino is a first year Neurosciences student currently rotating with Dr. Richard Daneman. He spends his spare time yelling at sports on his TV or reading/watching science fiction, and is currently looking forward to enjoying a yeast brewed beverage. 

Tardiff, D. F., Khurana, V., Chung, C. Y., & Lindquist, S. (2014). From yeast to patient neurons and back again: Powerful new discovery platforms. Movement Disorders29(10), 1231-1240. doi: 10.1002/mds.25989

Tardiff DF, Jui NT, Khurana V, et al. Yeast reveal a “druggable”Rsp5/Nedd4 network that ameliorates alpha-synuclein toxicity in neurons. Science 2013;342:979-983

Chung CY, Khurana V, Auluck PK, et al. Identification and rescue of alpha-synuclein toxicity in Parkinson patient-derived neurons. Science 2013;342:983-987.

What is My Brain Telling Me and How? Decoding the Neural Syntax

As I walk back to my car after a long and exciting day in class and lab, I have to pay attention to my environment for multiple reasons. First, I am clumsy and very likely to trip if I don’t. Second, I have to not only remember where my car is, but also know where I am in relation to it. As I scan the buildings around me, I am able to update my awareness of where I am based on everything I sense around me. Using sensory and spatial information (where am I?) together with memory (where is my car?) to drive behavior (walking in the correct direction to my car) is just one example demonstrating the incredible computing power of the brain.

Figure 1: Finding my car, the eternal struggle

Figure 1: Finding my car, the eternal struggle

How is my brain getting me to my car? To answer this question, Dr. György Buzsáki, Biggs Professor of Neural Sciences at NYU and this week’s Dart NeuroScience Seminar Series speaker, will tell us about theta oscillations in the hippocampus, the brain’s center for memory and spatial navigation. As shown by extensive research from Dr. Buzsáki’s lab and other labs, hippocampal theta oscillations, the 4-11 Hz collective activity of large neuronal populations, are essential for memory retrieval and spatial navigation, and deficits in neural oscillations in general have been associated with many cognitive deficits (for more information, Dr. Buzsáki’s review of theta oscillations can be found here).

Dr. Buzsáki and others hypothesize that the generation of theta oscillations brings the activity of sensory-activated and memory-activated neurons together, organizing individual spiking neurons into larger, oscillating networks that can accomplish a variety of computational tasks, including by communicating with other networks in distant brain regions. As elegantly described in the research goals on Dr. Buzsáki’s lab website, just as we put together words to form sentences, single-cell activity patterns are organized into overarching neural oscillations. Then, just as entire sentences express more complex meanings than do single words, neural oscillations convey more and accomplish more than the activity of only a few neurons.

Thus, by studying neural oscillations and how they are generated and maintained, Dr. Buzsáki studies neural syntax, the language of the brain, and he and his lab have greatly advanced our understanding of the nature and importance of hippocampal theta oscillations. In recent work from his lab, Dr. Eran Stark and colleagues recorded extracellularly from excitatory pyramidal cells (PYR) and inhibitory, parvalbumin-expressing interneurons (PV) in the hippocampus and neocortex of awake, freely-moving mice. At the same time, using optogenetic methods, channelrhodopsin-expressing PYR or PV were stimulated with a time-varying chirp pattern, a sinusoidal pattern of linearly increasing frequency.

These experiments highlighted two key observations. First, while direct PYR stimulation led to PYR spiking in a wide range of stimulation frequencies, PV stimulation (and therefore indirect PYR inhibition) led to suppression of PYR activity except during theta-frequency PV stimulation. Then, PV stimulation actually led to PYR theta-frequency spiking, indicating resonance selectively of theta-frequency activity patterns.

Figure 2: PV activation leads to theta-frequency PYR activity during theta-frequency stimulation

Figure 2: PV activation led to theta-frequency PYR activity during theta-frequency stimulation

Second, during PV stimulation, PYR spiking occurred specifically at the trough of the chirp-pattern input. This trough corresponded to when stimulation of PV was low and inhibition of PYR was removed. Thus, theta-frequency PV stimulation led to rebound spiking of PYR. Both theta resonance and PYR rebound spiking were shown to be dependent on PYR HCN channels, which typically activate at hyperpolarizing currents and allow positive current flow.

Figure 3: Application of ZD7288, an HCN inhibitor, blocks PYR theta-frequency activity during PV stimulation

Figure 3: Application of ZD7288, an HCN1-channel inhibitor, blocked PYR theta-frequency activity during PV stimulation

Overall, this research by Drs. Stark and Buzsáki demonstrates that in hippocampal and cortical populations, there is a strong ability and preference for passing theta-frequency activity from PV to PYR. With how important theta oscillations are for a variety of functions, both cells and networks are endowed with properties that facilitate the communication of theta activity, and as Drs. Stark and Buzsáki hypothesize, theta-frequency PYR rebound spiking could play a critical role in recruiting downstream networks in a time- and PV-activity-dependent manner. Because of this work, we are learning more and more about the syntactic rules that dictate how our brains make meaning, and it will be incredibly exciting to see what new findings about neural oscillations will emerge in the coming months and years.

If thinking about neural oscillations and syntax resonates with you, please come to “Brain oscillations organize neural syntax,” a seminar with Dr. Buzsáki at 4pm on Tuesday, January 13th, in the Farquhar Conference Room of the Center for Neural Circuits and Behavior. We hope to see you there!

Tammy Tran is a first-year UCSD Neurosciences graduate student currently rotating in Dr. Maryann Martone’s lab. She is shamelessly passionate about the minions from Despicable Me and currently has three toy ones on her desk. When she’s not thinking about neuroscience, she thinks about art and likes going to museums.

Figure 1: Courtesy of

Review: Buzsaki, G. Theta Oscillations in the Hippocampus. Neuron. 2002 Jan 31; 33(3):325-340. doi: 10.1016/S0896-6273(02)00586-X

Lab Website:

Article: Stark E, Eichler R, Roux L, Fujisawa S, Rotstein HG, Buzsáki G. Inhibition-induced theta resonance in cortical circuits.  Neuron. 2013 Dec 4; 80(5):1263-76. doi: 10.1016/j.neuron.2013.09.033.

It’s Electric! Sensory activity of an electric fish

Welcome to the new year all!

The first Dart NeuroScience seminar of 2015 will feature Dr. Leonard Maler from the University of Ottowa giving the Founder’s Day Lecture in honor of Dr. Theodore Bullock. Dr. Maler’s work focuses on the electrosensory system of the brown ghost knife fish, Apteronotus Leptorhynchu, employing morphological, electrophysiological, and computational techniques to better understand the way in which these fish process information about their environment.

Specifically, in a recent paper titled “Enhanced sensory sampling precedes self-initiated locomotion in an electric fish” his group suggests that volition, the ability to make a conscious decision to carry out an action, may be a capability earlier evolved than previously thought, present in aquatic vertebrates whose latest common ancestor with primates dates back to more than 450 million years ago! (Jun et al.) Wow time really flies.

Different life forms sense and interpret their environment in different ways. Take for example a human being who has resolved to hit the gym in the new year: she walks in and immediately sees the horde of fellow gym-goers, smells the running partner next to her, hears the powerlifters in all their grunting glory (the joys of gym-ing, right?) These are examples of active sensing behaviors that typically occur when animals are exploring an environment.

Now, let’s imagine we are brown ghost knife fish interacting with our Amazonian surroundings: given the dark underwater environment we’ll need to rely on a different sense to help us navigate around rocks and find tasty food. So, we fire up the electric organ and discharge pulses of electricity that stimulate electroreceptors on the skin. No predators or prey nearby if there’s no disturbance in the force, but if you sense a distortion in the electric field, look out! It could be lunch if we’re lucky, and open jaws if we’re not. For a more detailed explanation of their electric fish, check out the lab page here.

Since these electric organ discharges (EOD) can be measured and temporally mapped to movement, this could be a great way to answer the questions Dr. Maler has regarding volition, decision making, and active sensing in the electric fish. Check out this cool experimental setup:


Experimental tank in a sensory-isolation chamber with infrared (IR) lighting. Electric organ discharge (EOD) signal was captured by eight dipoles symmetrically placed around the edge of the tank to monitor the EOD rate (EODR) and movement activity; a video camera also directly captured movement. Subwoofers delivered random stimuli during a sensory-evoked condition and a microphone was used to record possible noise contamination.


Should I stay or should I go now?

Being the first week of a new year, a lot of us might have made decisions to get fit, save money, or volunteer more, but what about the more constant and subtle decisions regarding movement? Voluntary actions are a sort of decision making by which an animal chooses to enact a certain movement; they are typically exploratory behaviors (not having been caused by an unexpected stimulus) and are generally random (predictability would make it too easy for a predator).

It has been shown that in humans, cortical activity and an increase in sensory information acquisition precedes voluntary movement, however, the temporal dynamics of sensory sampling and movement initiation had not previously been established in electric fish. Alas! Dr. Maler’s group has shown a similar preparatory sensory acquisition in electric fish, and that this behavior had a random occurrence, exhibiting behavioral variability, two of the characteristics aforementioned in defining voluntary actions and decision making!


Fig. 4. Increase in the sensory sampling rate precedes voluntary movement. (A,B) Pseudo-colour plots of the normalized EOD Rate (A) and the EOD Acceleration  (B) time courses during spontaneous (top) and sound-evoked (middle) transitions. Time 0 indicates the movement onset, and trials are ordered by their EOD Rate up-transition onsets. Bottom, trial-averaged EOD Rate and EOD Acceleration normalized by the peak; both exhibited striking differences between spontaneous (green traces) and evoked (magenta traces) transitions.


The fish studied by Maler’s group display two behavioral states, defined as up-state and down-state (electrically active and non-active). Dr. Maler and colleagues saw self-initiated onsets of activity, associated with periods of increased preparatory sensory sampling rate (occurring up to 5 seconds prior to activity), which suggests that the animal is in a heightened sensory state and exhibiting exploratory behaviors! (Jun et al., Fig. 4) They conclude that this activity may represent volition in Apteronotus Leptorhynchu, that the neural processing occurring before self-initiated movement bears similarity to that in humans, and suggest the telencephalon as a possible region for further investigation. Given the homologies between teleost fish and mammalian telencephali, further study into the neural regions and mechanisms involved in modulating up-state activity in these fish could give insights into human neural circuitry involved in voluntary actions, sensory acquisition, and decision making. Tune in on Tuesday to find out more!

If your new year’s resolutions include enjoying more awesome science, then join us Tuesday, January 6th at 4pm in the CNCB large conference room for what’s sure to be an interesting Founder’s Day Lecture given by Dr. Leonard Maler, titled: “And now for something completely different: Active sensing, learning and recurrent networks in the telencephalon of a weakly electric fish.”

Nicole Hoffner is a first year Neurosciences student currently rotating with Dr. Bradley Voytek. She spends her spare time reading or sleeping, enjoys fishing on occasion, and wonders what an electric fish tastes like.

Jun J.J., Longtin A. & Maler L. Enhanced sensory sampling precedes self-initiated locomotion in an electric fish., The Journal of experimental biology, PMID:

A Correlation Tug of War

When you think of codebreakers, you usually think of Alan Turing.  But times have changed, and the hip new code everyone is trying to break these days is the Enigma of the neural code.

This week’s speaker is Dr. Alex Reyes.  His work has established some fundamental properties about the relationships between neural spike trains and sensation and the implications these relationships have for neural codes. Much of his work has been focused on understanding how sensory information is represented in neural networks.  Of particular interest to Dr. Reyes is the auditory cortex.  Dr. Reyes’ work blends computational, theoretical, and experimental approaches to understand the general principles behind signal processing in cortical circuits.

A fundamental concept in signal processing is correlation.  Measuring correlation is essentially asking, “how similar are these two signals?”   If you’re not familiar with how one calculates correlation, a simple analogy is comparing drawings on sheets of paper: overlap the sheets of paper and look at them through the light.  The darker the overlap, the more they match up. Similarly, to measure correlations between signals, one overlaps them and sums them up by taking a few integrals.

One question involving correlation that comes up frequently in neuroscience is,”How are correlations between neural signals relevant for coding information in spike trains?”

This is a huge question that is actively being debated and researched.  One of the reasons the debate is so persistent is pointed out in Dr. Reyes’ recent paper (Graupner and Reyes 2013).  It boils down to this: a wide range of correlation values between neural signals are measured in different parts of the brain, or even in the same part of the brain during different experiments.  Theoretical arguments have been put forward claiming that correlations enhance population coding, or claiming that they are detrimental to population coding.  Additionally, correlation patterns have been shown to change over time.

Clearly, understanding the mechanisms that effect neural correlations is a critical step in piecing together this apparently contradictory observations of neural correlations, and ultimately what the “neural code” is.

Given the wide range of correlations observed, a natural question to ask is whether these correlations simply come from correlations in the input to the neurons.  In (Graupner and Reyes 2013), the events that lead to correlations between neurons sharing synaptic inputs were investigated.

One might expect that neurons receiving similar (correlated) inputs would show correlated outputs.  Surprisingly, they found that neurons in the auditory cortex actively suppress input correlations to produce only weakly correlated outputs.  How could this be?

Graupner and Reyes performed their experiments in slices taken from the auditory cortex of mice. Of interest were pairs of pyramidal cells in layer IV: the “input” layer.  They did their recordings in media that had higher potassium concentrations to induce greater levels of spontaneous activity.  They noted two kinds of activity in their membrane potential recordings: low amplitude epochs and high amplitude epochs. They used these categories to epoch their data for separate analysis.

First, they analyzed the low amplitude epochs. A major goal of the paper was to look at the sub-threshold computations taking place on the correlated inputs. To do this, they measured the correlations between isolated inhibitory post-synaptic potentials (IPSPs), isolated excitatory post-synaptic potentials (EPSPs), and the composite EPSP and IPSPs.  To isolate the EPSPs and IPSPs, they did their recordings while holding the membrane potential at the reversal potentials for the inhibitory and excitatory inputs.  Thus, they were able to measure the correlations of the different kinds of input between pairs of neurons. Strikingly, they found that while the excitatory and inhibitory inputs were correlated between the two neurons, the combination of the two (the sub-threshold membrane potential) was significantly less correlated than either of the kinds of inputs! A similar effect was observed during the high amplitude epochs, where correlations were generally higher across the board.

The following figure from their paper captures this effect:


You can see that the red and green lines, representing only EPSP-EPSP correlations and only IPSP-IPSP correlations, are much higher than the blue, representing the actual membrane potential.

Somehow, the neurons were actively canceling out correlations in their shared inputs!

One possible explanation for this is that if the excitatory and inhibitory inputs are tightly coupled in time, then since they are of opposite sign, their combination will lead to cancellation of the correlations.  To test this hypothesis, Graupner and Reyes measured IPSCs and EPSCs to determine the correlations between the excitatory and inhibitory input currents and the relative timing of these inputs.

Similar to the results obtained from measuring post synaptic potentials, they found that excitatory-excitatory and inhibitory-inhibitory input currents were correlated between the neurons in each recording.  As predicted by the hypothesis, the excitatory-inhibitory correlations were relatively strong and negative – indicating that the excitatory inputs are tracked by correspondingly opposite inhibitory inputs. Moreover, the time delay between excitation and inhibition was short and got shorter with increased activity.  This indicates that inhibitory feedback happens on a very short time scale and so inhibition can effectively track excitation.  This is consistent with the idea that cancellation of the correlations between these inputs leads to the decorrelation in sub-threshold membrane activity.

At this point, readers may notice that these conclusions are critically dependent on being able to isolate the excitatory inputs from the inhibitory inputs to each neuron.  In a perfect world, each synapse of each kind of input would have the same reversal potential, and the potential to which the neuron was held at the electrode would be the same throughout the neuron.  Graupner and Reyes very astutely point out that because these pyramidal cells are spatially extended, the assumption that the membrane potential is the same throughout the cells is probably false.  Thus, it is unlikely that they are truly isolating the individual kinds of inputs.  Therefore, the correlations they measured between the inputs are probably not the true correlations.  How are they able to believe their conclusions? Two words: computational neuroscience.

The next section of their paper contains a beautiful example of the use of computational neuroscience techniques to address experimental questions. Graupner and Reyes constructed a model to estimate the impact of the spatial extent of the neurons on their measured correlations.  They set up a recurrent neural network to drive two test neurons.  These neurons were either point neurons, or spatially extended neuron models with different kinds of spatial input distributions. Essentially performing the same experiment on the model, they found was that the spatial extent of the neurons leads to an underestimate of the membrane correlations.  However, the amount of this underestimate depends on which correlations they were measuring.  When measuring excitatory input, the model indicated an underestimate by a factor of 8.2.  When measuring inhibitory input, the model indicated an underestimate by a factor of 3.1.  The exciting result was that the model indicated an underestimate by a factor of only 1.77 at the resting membrane potential, when both inputs were acting together.

This means that it is likely that even though they underestimated the correlations in the slice, the overall conclusions still stand.  Since the correlations at resting membrane potential were less underestimated than the individual inputs, the inputs still have higher correlations individually than together in the neurons and their conclusion still holds.  This is an excellent demonstration of how computational neuroscience offers tools to forge a reasoned path around experimental difficulties.

So, what implications does this study have?  For one, it demonstrates that this cortical circuit seems to be wired to decorrelate its inputs.  Thus, the observed correlations observed between neurons in previous studies can’t necessarily always be explained by correlations in their input.  The correlations are coming from somewhere, though! This suggests something deeper is happening, and future work must find where these correlations are generated and what they mean in the context of network activity. Graupner and Reyes suggest that modulators of overall network activity could shape these correlations.

For now, the tug of war between excitation and inhibition in our brains continues as we keep trying to break the neural code.

To hear from Dr. Reyes himself, be sure to come to his talk this Tuesday at 4 p.m. in the Center for Neural Circuits and Behavior Farquhar Conference Room.  His talk is titled, “Mathematical and eletrophysiological bases of maps between acoustic and cortical spaces.”  Based on that title alone, the seamless integration of theory and experiment that permeates Dr. Reyes’ work is sure to be apparent!

Brad Theilman is a first-year Neurosciences student currently rotating with Dr. Eran Mukamel. When not processing signals from the brain, he is probably out searching for signals from satellites on his amateur radio. 

Graupner, M. and Reyes, A. (2013). Synaptic input correlations leading to membrane potential decoration of spontaneous activity in cortex. J. Neurosci. 33(38): 15075-15085. doi: 10.1523/JNEUROSCI.0347-13.2013.

Ode to the Circuitry of the Visual Cortex

If you decide to take a short noon walk

And pass the cliffs that mark the edge of Muir,

You’ll see the labs founded by Jonas Salk

A worthy home for a bright professor–

Ed Callaway, let me tell you ‘bout him!

For a masterfully shaped talk Tuesday

Be sure you don’t miss Of mice and monkeys:

A journey into the visual system

Where we’ll learn of the circuits that relay

Signals telling cortex what the eye sees.

Pursing the mechanisms behind

Functional cortical activity,

He has pioneered mapping techniques and

Helped us understand connectivity.

To trace the complex circuits creating

The visual cortex, a new marker,

For cells connected synaptically,

Was created and has been worth the slaving.

The rabies virus vector was winner

Moving ‘cross synapses retrogradely.

But here’s the cool part kids, pay attention.

Without glycoprotein (G) the virus

Can only spread through direct connection

Thus mapping neural inputs, lucky us!

To know which inputs cause inhibition,

Cell-specific markers become crucial.

So the vector was further improved to

Make its envelope glycoprotein fit in

A receptor, not found in a mammal,

But expressed in the target cell by you!1

a and b part 2

An example of targeting the Glycoprotein (G)-deficient rabies vector to infect cells where the TVA-receptor, which binds to an envelope glycoprotein on the rabies virus, is expressed.  Taking advantage of selective expression of Camk2a-Cre in CA1 pyramidal neurons, the rabies vector was injected into the hippocampi of Camk2a-Cre:TVA double transgenic mice to study which neurons have direct synaptic connections with the CA1 pyramidal neurons.  In A and B, fluorescent neurons have been infected with the rabies virus.2

Between electrophysiology,

Rabies vectors, and high-tech imaging

Callaway has many tricks up his sleeve.

He’s shown inputs to layer 2/3 exciting,

If they’re from layer 5, but not always

If they’re from layer 4 or layer 2/3.

Wow, that’s some fine-tuned specificity!3

If this wets your whistle, just wait a few days.

Both talk and food are complimentary

Tuesday, 4 PM at CNCB.

Kelsey is a first year student in the Neurosciences Graduate Program and started the MD/PhD Program in 2012.  She is doing her thesis work in Dr. Subhojit Roy’s lab.  Outside of work, she enjoys singing and soccer but struggles with iambic pentameter.

1.  Osakada F. and Callaway E.M. (2013). Design and generation of recombinant rabies virus vectors. Nature Protocols. 8, 1583-1601.

2.  Sun Y., Nguyen A.Q., Nguyen J.P., Le L., Saur D.,  Choi J., Callaway E.M., and Xu X. (2014). Cell-Type-Specific Circuit Connectivity of Hippocampal CA1 Revealed through Cre-Dependent Rabies Tracing. Cell Reports. 7.1, 269-280.

3.  Yoshimura Y., Dantzker J.L.M., and Callaway E.M. (2005). Excitatory cortical neurons for fine-scale functional networks. Nature. 433, 868-873.

Delicious and Nutritious: The Tale of a Multitasking Taste Receptor

Amid much controversy, the Drosophila Department of Agriculture recently published a new food pyramid and updated the serving size recommendations for various fruits. Members of the fly community across the nation are now struggling to adjust their diets, hoping these new regulations will result in healthier body weights, longer lifespan, and increased reproductive success.

Just kidding.

We live in a society in which the words “nutritious” and “healthy” are informed by decades upon decades of scientific research on what food components help us stay energized, keep our bones strong, boost our immune systems, lower cholesterol, protect against cancer, etc. Subject to innovations in research tools and data collection, these perceptions of nutrition have morphed over time. (For example, a diet based on Wonder Bread today seems entirely comical, but apparently it was once in vogue.)

Other organisms, however, in the absence of a Department of Agriculture, World Health Organization, or other such institutions, must rely on internal, biological systems for determining what foods will keep them alive and healthy (I know, mind blown). Studying the mechanisms of nutrition perception in model organisms may lead to a more complete understanding of the interactions between human metabolic processes and brain systems, many of which remain unknown.

This week’s seminar speaker, Dr. Hubert Amrein from Texas A&M, studies taste and internal nutrient perception in Drosophila. He recently published a study (Miyamoto et al. 2012) throwing a spotlight on a certain taste receptor also endowed with fantastic nutrition-sensing ability. The star of the show is Gr43a, a member of the gustatory receptor (GR) family. GRs are present on gustatory receptor neurons (GRNs), located in taste sensillae (basically Drosophila taste buds) on various external body parts such as the proboscis and legs.

Gr43a caught the interest of Dr. Amrein and his lab because it is one of few Gr genes to have orthologs across insect species; this conservation suggests that Gr43a might play a more important role than its evolutionarily dispensable Gr counterparts. Dr. Amrein and his team established that Gr43a is a specific, high-affinity fructose receptor present not only in the sensory neurons but also in the brain. But the fly discovered that the food was sweet the moment he extended his little proboscis towards it, so what is the purpose of having a gustatory receptor in the CNS??

As neuroscientists, we spend a lot of time thinking about neurons and synapses, how we create memories, move muscles, and observe our environment. Many of us tend to forget about the vasculature that winds its way through the brain delivering oxygen and sugar. It turns out that the Gr43a receptors in the brain act as a sensor for fructose circulating in the hemolymph (essentially a fancy name for Drosophila blood)! The authors found that after a fly feasts on the sugarlicious fruit on your kitchen counter, the circulating levels of other sugars (namely glucose and trehalose) remain relatively constant while fructose levels, which are normally quite minimal, shoot up until they are high enough to stimulate those Gr43a receptors in the brain. In this way, Gr43a stimulation acts as an indicator of a nutritious meal.

Delving further into the idea of Gr43a as a nutrition sensor, the authors used the capillary feeding (CAFE) assay in which hungry flies had a choice between water and sorbitol, which is tasteless but nutritious. Gr43a mutant flies had no preference, but wildtype flies preferred the sorbitol (somehow—aka via their Gr43a receptors—they just knew it was providing much-needed sugary goodness). The Gr43a mutants, on the other hand, entirely nonplussed by both meal choices, showed no preference. Look at Gr43a, saving flies from their pangs of hunger. What a hero! But is he just a clutch hitter or does he also perform when flies are happily satiated and the game isn’t on the line?

The authors did a second CAFE assay with satiated flies and allowed them to choose among a array of nutritious, sweet-tasting sugars. Intriguingly, the Gr43a mutants went hog-wild and devoured 60-80% more sugar than the wildtype flies! However, when the assay was repeated with sweet, non-nutritious sugars (sugars that cannot be converted to fructose), there was no difference in feeding amount between the two groups, suggesting that in satiated flies, Gr43a acts to suppress rather than promote nutrient intake. So not only is our hero a lifesaver in sticky situations, he also endows flies with a modicum of self-control.

Could Gr43’s awesome bidirectional powers arise from an ability to change valence depending on the feeding state? To address this question, the authors created flies that had temperature sensitive TRPA1 ion channels in their Gr43a brain neurons. Effectively, if flies at ambient 23 °C were moved to above 25 °C, their TRPA1 channels opened, activating neurons expressing Gr43a. This allows control of Gr43a neurons completely independent of hemolymph fructose concentration. Flies were exposed to odor A at 29 °C (activated Gr43a-expressing neurons) and to odor B at 23 °C (inactive Gr43a neurons). Then they had to decide between A and B in a T-maze odor choice assay. Odor A was a magnet for hungry flies, but satiated flies wanted nothing to do with it (Fig. 7A-B). These results imply that, via the Gr43a receptor, fructose triggers a pleasant sensation in hungry flies and an aversive sensation in satiated flies (Fig. 7C)!

Screen Shot 2014-10-31 at 2.31.45 PM

I should mention that it gets more complicated for mammals; in mice, fructose promotes feeding behavior while glucose suppresses it. So don’t go seeking all of the high fructose corn syrup you can find—it likely will not aid your food-related self-control as it does for our fly friends.

If you’re hungry for more on this fascinating topic, please come listen to Dr. Hubert Amrein’s talk this Tuesday at 4pm in the CNCB large conference room… It’s going to be sweet!

Catie Profaci is a first-year Neurosciences student currently rotating with Dr. Byungkook Lim. When she is not in lab, you likely can find her running barefoot on the beach, listening to NPR, cooking and consuming spicy food, or watching the Yankees game.

Miyamoto T., Xiangyu Song & Hubert Amrein (2012). A Fructose Receptor Functions as a Nutrient Sensor in the Drosophila Brain, Cell, 151 (5) 1113-1125. DOI: