I’m trying to collect all of the lectures relevant to songbirds into one place. I’m going to start by combing  through some sources I’m already familiar with like podcasts. If I leave any out, please post them in the comments and I’ll try to update the main post. Thanks!

Songbird lectures sorted by date:

Sam Sober, UTSA Neuroscientists Talk Shop Oct 2014

Todd Roberts, UTSA Neuroscientists Talk Shop Mar 2014

Eric Fortune, UTSA Neuroscientists Talk Shop Sep 2013

Annemie Van der Linden, “Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds,” Jove Jun 2013

Mimi Kao, “What songbirds can teach us about learning and the brain,” TEDxCaltech Feb 2013

Sam Sober, “A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds” Jove Nov 2012

Erich Jarvis, “Brain Evolution: How Birds and Humans Learn to Sing and Talk” Georgia Tech Honors Program Oct 2012

Alan Kamail, “Bird Brain! Compliment or Insult,” Nebraska Lecture  Sep 2012

Erich Jarvis, UTSA Neuroscientists Talk Shop Apr 2012

Michale Fee, “The Role of Basal Ganglia Circuits in Vocal Learning in the Songbird: A Hypothesis” NIH Videocasts Jan 2012

Allison Doupe, 15th Annual Swartz Foundation MIND/BRAIN Lecture Apr 2011

William Grisham, UCLA Modular Digital Course in Undergraduate Neuroscience Education – Lecture 1Lecture 2, Lecture 3, Lecture 4 Oct 2011

Ila Fiete, UTSA Neuroscientists Talk Shop Nov 2009

Harvey Karten, “Bird Brains,” UCTV Grey Matters Jan 2008


Also possibly of interest:

Dopamine Symposium w/ Bruce Bean, Jim Surmeier, Carlos Paladini, Jochen Roper, John Williams, and Charles Wilson, UTSA Neuroscientists Talk Shop.


Differences between direct an indirect MSNs are not obvious from morphology or electrophysiology. However, the two cell types and pathways can be separated and selectively manipulated using genetic tools, such as lines of reporter mice that express GFP in either D1 or D2 expressing MSNs, so many recent studies have tried to sort apart the pathways. This paper is a review of more classic studies on DA’s differential effects through D1 versus D2 signalling on glutamatergic signaling, intrinsic activity, and plasticity.

Continue reading ‘Summary of D1 and D2 DA-Receptor Modulation of Striatal Glutamatergic Signaling in MSNs – Surmeier 2007.’


People and animals do not always make ‘rational’ choices that maximize the amount of reward they receive. For example, deviations from ‘rational’ economic decision making include risk aversion and decreasing marginal utility of a given reward. But what determines this utility? Continue reading ‘Is human irrationality due to irregularities in dopaminergic neuron firing? Notes from Stauffer, Lak, and Schultz 2014 – Dopamine Reward Prediction Error Responses Reflect Marginal Utility’

Try and cross your eyes to make the top gridded circles overlap. Give up? Try doing it on the bottom ones that are surrounded by the border.

A lot easier, right? The square surrounding the circles helps your eyes stabilize as you cross them.

Free fusion optical illlusion vergent eye movements

From Blake 1989

Also notice how your eyes process the diagonal lines themselves. Your brain is receiving drastically different signals from each eye creating a state of binocular rivalry, where your perception often alternates between the signals received from one eye and the other.

If you’re interested in this phenomena and learning a theory about it’s neural underpinnings check out the paper where I came across it. http://www.psy.gla.ac.uk/~martinl/Assets/MCMPS/Blake_89.pdf


Blake, R. A neural theory of binocular rivalry. Psychol. Rev. 96, 145-167 (1989).

Found this book at a bookstore and was hooked after reading the first few pages, based on it’s topic and clear and well-written prose.

James Davies

James Davies

Chapter 1

James Davies, with a PhD in social and medical anthropology from Oxford, begins with a history of psychiatry starting in the 1970s and a crisis of confidence it faced. A series of experiments questioned the validity and reliability of psychiatric diagnosis.

The 1973 Rosenhan experiments on “Being Sane in Insane Places” questioned the validity of psychiatric diagnoses. Neurotypical confederates checked themselves into asylums claiming to have heard a voice once, but then once admitted acted normal and saw what they were diagnosed with and saw the great lengths it took to be deemed healthy again and get discharged from the institutions. After publishing the results, they were challenged by a hospital to send pseudopatient imposters back in. Rosenhan agreed to the challenge but did not send any patients. A month later the hospital reported they suspected 41 imposters.

Another experiment showed that diagnoses were not consistent between psychiatrists. It sent the same patients to different psychiatrists and showed that they got different diagnoses from psychiatrists around a third of the time. Additionally, the prevalence of different diagnoses seemed to be regional, with some diagnoses being more prevalent in certain countries.


These criticisms of psychiatry lead to a drastic rewrite of the 3rd edition of psychiatrists diagnostic manual, the DSM III, headed by Robert Spitzer, who attempted to increase diagnostic reliability by making the definitions of disorders more precise and adding an explicit checklist to each disorder. These checklists created thresholds between psychiatric disease and “normal” human experience that, while decided by expert consensus, were ultimately arbitrary.

Additionally, the DSM III removed many disorders, especially those that had been introduced by psychoanalysts, as that discipline was falling out of favor. He brings up an interesting story about the removal of homosexuality as a “sexual deviation,” which he says was largely due to pressure from the Gay Rights movement and came down to a vote at an American Psychiatric Association meeting in the 70s, with 5,854 psychiatrists voting to remove homosexuality as a disease and 3,810 voting to keep it in. Davies makes the point that many of these decisions were political and not directly based on any changes in scientific research.

Despite the reforms made in the DSM III and subsequent manuals, diagnostic reliability remains a difficulty. Interestingly, I looked up a citation Davies makes to Aboraya, 2006, which he says “showed that reliability actually has not improved in thirty years.”I’m uncertain about how Davies reached that conclusion from this paper, as it clearly states:

  • that while diagnostic reliability remains a problem, the third generation of psychiatric diagnoses “from 1980 to present… more reliability papers were published and the reliability of psychiatric diagnosis has improved,” and
  • “The development of the DSM-III and its subsequent versions has been a major accomplishment in the history of psychiatric nomenclature. Clinicians use the DSM criteria in clinical practice as an effective way to communicate the clinical picture, the course of illness, and efficacy of treatment.”

This citation seems academically sloppy and perhaps shows that Davies seeks to oversimplify a complex and murky issue into a one-sided story (though this also might reflect my innate bias against pop-science books).

Chapter 1 ends questioning the validity of psychiatric diagnoses even if we fix the reliability problem. Even if we could get every psychiatrist to agree on the diagnoses, does that mean it’s a real disease entity, or that we’ve just made a reliable but arbitrary construct? He argues that we need biomarkers to prove it’s a “discrete, identifiable biological disease.” While I agree, I think that psychiatric definitions do a good job of separating normal but different from disease, by often requiring that the disease is disruptive to the patients social relationships or occupational function. What makes psychiatric illnesses, diseases is that they are problematic for people’s lives, and people, whether the patient themselves or their friends and family, want something done about it. I’m unsure if we will be able to find or need to find biomarkers for every disease. While some diagnoses may ultimately be arbitrary, if they are clinically helpful and can show statistical and long-term improvements in patients quality of life, then they are valuable.

Let me know if you have any comments on this blog post. I hope to continue blogging my thoughts on this book as I read through it.

SFN logo 2014 #SFN14 Society For Neuroscience Blog

Joe Paton from the Saltzman lab – Time encoding cells in the rodent striatum.

You can use an operant conditioning train animals to press a lever and get a reward. Using a fixed interval paradigm, you then do not reward the animal for lever presses until a certain time interval is passed. Animals will learn roughly learn this time association, and pause from lever presses until a point some intermediate time before the interval will expire, and then they begin pressing the lever again.

If you record in the striatum of rodents that have learned this task, you see neurons that fire at every point in the fixed interval and rescale with the fixed interval, if the interval changes. They saw that the rescaling was always slightly subproportional, and also that striatal cells multiplex information about action and time.

I think this is a really great clear electrophysiological link between striatal activity and the task being performed. I wonder if labs have tested mutant mice such as FOXP2 KOs or Shank3B knockouts with proposed striatal defects in tasks like this.

Also, it’s harder to learn the association between a Conditioned Stimulus (CS) and Unconditioned Stimulus (US), if you stretch out the time delay between the CS and US, while keeping the inter-trial interval the same. However, for some reason if you increase the inter-trial interval proportionally with the delay between CS and US, then that increased difficulty of learning the CS/US association doesn’t occur.

Maybe, it’s more difficult to make the association between CS/US with a longer delay, and therefore it takes the brain longer to process the association. But, if you leave the brain to process the old trial offline without interference from new tasks, then it can form the association. To restate that, when the trials happen too quickly, processing the subsequent trial interferes with the ongoing processing of the previous trial and interferes with learning.

Carlos Lois – Transgenic Songbirds – Genetic tools to investigate brain circuit assembly and cellular basis of behavior.

Lois mentioned that HVCà RA neurogenesis occurs during song learning. Neurons migrate into the HVC after making soma-soma contact with resident neurons. A thought I had never really had was how much is going on during windows of time when fetuses are learning about speech. A lot of developmental psychology work has shown that babies learn to identify prosaic cues of language, their mother’s voice, etc. while in the womb. I wonder to what extent these process are concurrent with and depend on neurogenesis. More generally, do we have a good sense from autopsy studies and/or radiation exposure studies when neurogenesis ends in normal human development for cortical areas, the striatum, etc.

Collaborating with the Gardner lab, Lois designed an adeno-associated virus (AAV) to express GCAMP6 in a small population of neurons in the HVC. (For some reason with the current viruses and promoters they’re getting much smaller yields of infected cells than people normally do with mice. ~2,000 neurons in birds compared to ~20 million in mice. Of course this isn’t always bad–sparse labeling can be good for measuring morphology or separating cell-autonomous effects from emergent effects). By mounting a low weight CMOS camera on top of the birds head, they could record activation of many cells simultaneously in a singing behaving bird (and know where these cells lay relative to each other in the rostro-caudal and medial-lateral axes).

Lois’s group also infected HVC with a virus driving expression of a bacterial Na+ channel ‘NaChBaC.’ Neurons in this channel went from firing single discrete spikes to prolonged depolarizations. As the channel starts being expressed, song gets completely distorted, but remarkably a few days later the bird has found a way to compensate and get song back to normal. They said that they did histology to confirm the infected neurons were still alive and expressing the channel. However, maybe the bird simply down-regulated all of the synaptic strengths of the infected neurons, and there is enough redundancy to produce the song with the remaining neurons.

Finally, Lois showed data from transgenic zebra finches that have been germline transfected with RNAi to knock down CNTNAP2 expression, which is associated with developmental language disorders and autism. They showed that the CNTNAP2 knockdown birds showed normal learning of simple syllables, but impaired learning of complex syllables. He also mentioned that they had developed a line of GCAMP6 transgenic animals which is pretty exciting. I believe viral infection of germline cells cann be combined with the CRISPR-CAS system to cause deletions and premature stops at target genes, and theoretically even induce homologous recombination (by also providing a template strand complementary to the region where the DNAase has cut the DNA.

Someone asked a question about CRE lines in birds, and Lois said that economically it probably wasn’t viable. Not enough people do research on birds to justify that kind of investment, and making such animals would be a largely thankless job. However, there are still a ton of experiments that could be done on simpler transgenics such as: plain KOs, KDs, transgenics that express fluorescent proteins and optogenetic channels. I think just having that level of genetic tools combined with the song system’s anatomical modularity, the strength of song as a behavior, and the strong phenotypes seen in FOXP2 KDs and CNTNAP2 KDs show that genetic songbirds could be an extremely powerful models for diseases of speech, social behavior, and motor learning. Further, birds have similar reproductive cycle lengths as mice and live longer, so stocks of transgenics can be bred in reasonable amounts of time. I wanted to ask Lois what he thought about creating inbred strains of zebra finches, but I didn’t have the chance. (I’m almost tempted to start this as a side project in my apartment, but I’m afraid of then bringing an infection into the lab.)

Lois had the impression that the NIH was not interested in funding zebra finch transgenics. His grants came from mouse projects and work he did on zebra finches were side projects.

A member of the Simons Foundation “SFARI program” said that they have wide agreement that rodents aren’t “sophisticated.” However, the program is most concerned with investigating the rare genetic causes of autism and generating high-throughput screening models for autism. Personally, I think this is short sighted and the basic science just isn’t there yet. We really need to investigate the basic mechanisms of communication, imitative learning, etc., and I think songbirds are one of the best model organisms for these behaviors—and without a doubt better than rodents. So, while using genetics we’ve done amazing work on specific etiologies like Rhett syndrome, as someone in the audience rightly pointed out there are still almost no examples of rational drug design in neuroscience and neurology, except for the new example of orexin antagonists for insomnia and how good of a drug they’ll be in the long term is unclear.

Dennis Drayna – genetics of stuttering and mouse models

Stuttering affects 4% of the population at some point in their life, but only .5-1% of adults are persistently affected and at that point there is a 4:1 ratio of males to females.

Most human genetics has focused on this persistent stuttering population. They’ve shown that it’s about 85% Continue reading ‘Striatal time cells, transgenic birdsongs, stuttering mice and more – Birdsong4 Sattelite and #SFN14 Notes’

Gabor Filter showing a receptive field of a hypothetical simple-cell in the visual cortex (Wikipedia)

Gabor Filter showing a receptive field of a hypothetical simple-cell in the visual cortex (Wikipedia)

In 1972, Horace Barlow, great-grandson of Darwin, wrote an article in which he put forth “A neuron doctrine for perceptual psychology?” (The question mark at the end shows that Barlow was a contemplative guy.) With this doctrine, Barlow tried to relate the firing of neurons in sensory pathways with subjectively experienced sensation. One of the principles he introduces is: at progressively higher levels of sensory processing, information is carried by fewer neurons because the system is organized to a near complete a representation with the fewest active neurons. In other terms, the encoding of sensory information gets ‘sparser’ as one moves up into higher levels of sensory processing. Since then, a number of lines of evidence have converged, supporting Barlow’s proposition and the general value of ‘sparse codes.’

On the blog today I’ll be reviewing one such paper, “Sparse coding of sensory inputs” by Bruno Olshausen and David Field. This paper defines ‘sparse coding’ as a computational strategy where brains encode sensory information using a small number of simultaneously active neurons at a given time[1].

This paper puts forth four ideas for why sparse coding theoretically might be a good strategy:

  1. More memories can be stored
  2. Makes use of the statistical structure of natural signals
  3. Represents data in a convenient way for further processing
  4. Save metabolic energy, by decreasing neuronal firing rates

When model neurons are trained to optimize spare representations of natural scenes, the receptive fields that emerge represent the simple-cells of the primary visual cortex.

Learned receptive fields to maximize sparseness of natural scenes (Olshausen and Field, 2004, Current Opinion in Neurobiology)

Learned receptive fields to maximize sparseness of natural scenes (Olshausen and Field, 2004, Current Opinion in Neurobiology)

One interesting feature of sensory representations that this paper points out, is that as visual signals move from the thalamus to the visual cortex, there is a 25:1 expansion (of axonal projects to cortex versus axonal projections to the LGN of the thalamus). They suggest that this 25:1 expansion possible emerges as a compromise between the trade-offs: with high sparseness eventually ending in ‘grandmother cells’ where a single unique neuron represents each element of a sensory[2], and low sparseness incurring developmental and metabolic costs of having to use many neurons to encode each element. Additionally, the more a neuron spikes the more energy is needed to maintain it’s electrochemical gradients through pumps like the sodium-potassium ATPase. From what we know about the metabolic use of the cortex, scientists have estimated that only 1/50th of neurons are active at any given time.

Another interesting point this paper makes, is that some experimental results show neurons with much higher firing rates than would be predicted by metabolic estimates. Because these experiments often involve searching for firing neurons with an electrode, we may be systematically biasing our studies towards a minority of neurons that fires “less sparsely” than the general populations. Solutions to this bias include chronically implanting electrodes where the positioning is set anatomically or using antidromic stimulation to identify neurons as opposed to stimulus elicited firing.

Sparse coding beyond sensory systems

This paper also points out that sparsely firing neurons are observed in motor cortex during movements, and that experimentally driving a single neuron can be enough to initiate whisker movements in rats (Brecht et al., 2004). In the zebra finch song production pathway, HVC neurons fire sparsely at precise points in song, and precise spike-timing in RA may be important as well.

How does one actually measure “sparseness”

Olshausen and Field, 2004, Current Opinion in Neurobiology

Olshausen and Field, 2004, Current Opinion in Neurobiology

Olshausen and Field write that a standard measure of “sparseness” is kurtosis, with a larger value indicating a “sparser” distribution.

Kurtosis equation sparseness

They also describe another method developed by Rolls and Tovee, the activity ratio, which is specialized for one sided distributions (and therefore good at modeling neurons since firing rate cannot drop below zero).

Activity Ratio equation sparseness

Finally, The activity ratio can then be scaled from 0-1, using Vinje and Gallant’s[3] sparse coding scale transformation:



I think there is compelling evidence for brains using of sparse coding in sensory systems and there are good theoretical reason for why brains should use sparse coding. It will be interesting to see if these findings hold up for motor systems as well. If they find evidence of sparse coding in relatively simple and blunt movements like locomotion, I would guess that they will be more present in higher premotor areas encoding complicated and learned movements.

I big issue in machine learning is how to process the data before unleashing machine learning algorithms on it. I think neurophysiology shows that a good strategy might be to duplicate the data and turn it into over complete sparse representations. I assume scientists working with big data are already doing this, but honestly I have no idea.


Barlow HB: Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1972, 1:371-394.

Brecht M, Schneider M, Sakmann B, Margrie TW: Whisker movements evoked by stimulation of single pyramidal cells in rat motor cortex. Nature 2004, 427:704-710

Olshausen BA, Field DJ (2004). Sparse Coding of Sensory Inputs. Current Opinion in Neurobiology, 14: 481-487.

Rolls ET, Tovee MJ: Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. J Neurophysiol 1995, 73:713-726.

Vinje WE, Gallant JL: Sparse coding and decorrelation in primary visual cortex during natural vision. Science 2000, 287:1273-1276.

[1] I had always thought of sparse coding from the perspective of individual neurons, as a strategy where a given sensory neuron fires only at a very specific stimuli or aspect of a stimuli, so therefore it fires ‘sparsely.’ I realized in reading this, that really this paper’s definition and my mental understanding are two sides of the same coin if the neurons are firing independently. However, even with a population of neurons that only fire very rarely might not be ‘sparsely coding’ by Olshausen and Field’s definition if they are all firing together and then silent together.

[2] How does that neuron spur the rest of the brain into action and what occurs when that neuron dies?

[3] This is the same gallant that did some of the coolest experiments ever decoding neural activity from fMRI activation: https://neuroamer.wordpress.com/2011/10/31/scientists-record-lucid-dreams-with-eeg-and-fmri-simultaneously/


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