Funny how we are really good, for the most part, at knowing where sounds are coming from. And it is funny since the ear provides the brain with no direct information about the actual relationship in space of different sound sources. Instead, the brain makes use of what happens to the sound as it reaches both ears by virtue of, well, being a sound wave and that we have two ears separated in space.
Imagine a sound coming from the front, the sound will arrive to the two ears at the same time. But if it is coming from the right it will arrive to the right ear first, and to the left ear a wee later. This ‘time difference‘ will depend on the speed of sound in air and how far apart our ears are. Even more, as the sound source moves from the far right to the front of the head those time differences will become smaller and smaller, until they are zero at the front. If one could put one microphone in each ear, one could reliably predict where the sound comes from by measuring that time difference. And this is exactly what a group of neurons in the brain does.
Easy enough? Not quite.
The way the brain works is that things on the left side of our body are mapped on the right side of our brains, and things on the right side of our bodies are mapped on the left side of our brains. So the ‘time comparison’ neurons on the right side of the brain deal mainly with sound from coming from the left (and neurons dealing with the sound from the right are on the left side of the brain). But to do the time comparison these neurons need to get the information from both ears, not just from only one side!
This raises this conundrum: the neural path that the information from the left ear needs to travel to get to the same (left) side of the brain will inevitably be shorter than the path travelled by information coming from the other side of the head. So how does the brain overcome this mis-match?
And here is where having paid attention at school during the “two trains travelling at the same speed leave two different stations blah blah blah” math problem finally pays off. When a sound comes from the front, the information arrives to each of the ears at the same time. The information also arrives to the first station in the brain (nucleus magnocellularis) at the same time. But time comparison neurons need information from both ears, and the path that the information needs to travel from the right side to the time comparison neurons in nucleus laminaris on the left side (red arrow in figure 1) is longer than the path from the same side (blue arrow in figure 1).
However, when you look into an actual brain, things are not so straight-forward (sorry for the pun). The axons from nucleus magnocellularis that go to the time comparison neurons on the same side of the brain take a rather roundabout route (as in figure 2). And for long we assumed that such roundabout way was enough to make signals from the left and right sides to arrive at about the same time.
Easy enough? Not quite
When Seidl, Rubel and Harris actually measured the length of the axons (red and blue) they found that there was no way that the information could arrive at about the same time and that the system could not work in the biological range. But this problem could be overcome (back to the old school problem) by having the two trains (action potentials rather) travel at different speeds. And this is something that neurons in the brain can relatively easily do in two ways: One is to change the girth or diameter of the axon. The other is to regulate how they are myelinated. Myelin forms a discontinuous insulating wrap around the axon, which is interrupted at what is called the Nodes of Ranvier. The closer the Nodes of Ranvier are, the slower the action potential travels down the axon.
What the group found was that both axon diameter and myelination pattern were different in the direct (blue) and crossed (red) axons. When they now calculated how long it would take for the action potential from both sides to reach the time comparison neurons in nucleus laminaris, adjusting speed for the differences in the two axons, they found that yup, that pretty much solved the problem.
Easy enough? Quite
Like the authors say:
The regulation of these axonal parameters within individual axons seems quite remarkable from a cell biological point of view, but it is not unprecedented.
But remarkable indeed, considering that this regulation needs to adjust to a very high degree of temporal precision. I have always used the train analogy when I lecture about sound localisation, and always assumed equal speed on both sides. Seidl, Rubel and Harris’ work means I will have to redo my slides to incorporate differences in speed. Hope my students don’t end up hating me!
Seidl, A., Rubel, E., & Harris, D. (2010). Mechanisms for Adjusting Interaural Time Differences to Achieve Binaural Coincidence Detection Journal of Neuroscience, 30 (1), 70-80 DOI: 10.1523/JNEUROSCI.3464-09.2010
Santiago Ramón y Cajal originally described spines in the dendrites of neurons in the cerebellum back in the late 19th century, but it wasn’t until the mid 1950’s with the development of the electron microscope that these structures were shown to be synaptic structures. Although it has been known that the number of dendritic spines changes during development and in association with learning, most studies have inferred the changes by looking at static time points rather than monitoring individual spines in the same animal over time, partly, due to the difficulty of tracking a single structure of about 0.1 micrometer in size (0.0001 mm). But new advances in imaging technology have allowed researchers to ‘follow’ individual spines over time both in vitro and in the whole animal.
Dendritic spines are no longer thought of as the static structures of Ramón y Cajal’s (or even my) generation, but rather dynamic structures that can be added and eliminated from individual dendrites. And because each spine is associated with a synaptic input, and because their structure and dynamic turnover is known to have a profound effect on neuronal signaling, one cannot but be tempted to propose that they are associated with specific aspects of memory formation.
Two developments have made it possible to monitor individual dendritic spines at different time points in the same animal: the ability to incorporate fluorescent molecules into transgenic mice that make the spines visible under fluorescent illumination, and the development of in vivo transcranial two photon imaging that allow researchers to go back to that individual dendrite and monitor how the dendritic spines change over time. Two papers published in Nature make use of these techniques to look at how dendritic spines change in the motor cortex of mice that have learned a motor task.
In one, Guang Yang, Feng Pan and Wen-Biao Gan looked at how spines changed when either young or adult mice were trained in to learn specific motor strategies. They observed that spines underwent significant turnover, but that learning the motor task increased the overall number of new spines and that a small proportion of them could persist for long periods of time. They calculated that although most of the newly formed spines only remained for about a day and a half, a smaller fractions of them could still persist for either a couple of months or a few years. Based on their data they suggest that about 0.04% of the newly formed spines could contribute to lifelong memory.
Another study by Tonghui Xu, Xinzhu Yu, Andrew J. Perlik, Willie F. Tobin, Jonathan A. Zweig, Kelly Tennant, Theresa Jones and Yi Zuo did a similar experiment, but using a different motor training task. Like the Yang group, they also saw that training leads to both the formation and elimination of spines. Although newly formed spines are initially unstable, a few of them can become stabilized and persist longer term. Further, training made newly formed spines more stable and preexisting spines less stable. The authors interpret their results as an indication that during learning there is indeed a ‘rewiring’ of the network and not just addition of new synapses.
The two papers were reviewed by Noam E. Ziv & Ehud Ahissar in the News and Views section. Here they raise the issue that, if such a small number of spines are to account for the formation of stable memories, then what are the consequences of the loss of a somewhat larger number of spines on the neuronal network?
For someone like me that more than once as an undergraduate used a microscope fitted with a concave mirror to use the sunlight to illuminate the specimen, the ability to monitor individual synaptic structures over time in a living organism can only be described as awesome. But, as pointed out by Ziv and Ahissar,
“[…] although it remains to be shown conclusively that these forms of spine remodeling are essential components of long-term learning and not merely distant echoes of other, yet to be discovered processes, these exciting studies make a convincing case for a structural basis to skill learning and reopen the field for new theories of memory formation.”
Yang, G., Pan, F., & Gan, W. (2009). Stably maintained dendritic spines are associated with lifelong memories Nature, 462 (7275), 920-924 DOI: 10.1038/nature08577
Xu, T., Yu, X., Perlik, A., Tobin, W., Zweig, J., Tennant, K., Jones, T., & Zuo, Y. (2009). Rapid formation and selective stabilization of synapses for enduring motor memories Nature, 462 (7275), 915-919 DOI: 10.1038/nature08389
Ziv, N., & Ahissar, E. (2009). Neuroscience: New tricks and old spines Nature, 462 (7275), 859-861 DOI: 10.1038/462859a
Songbirds have evolved special areas in the brain that are used for song learning and song production. Two types of output connections from a cortical area known as HVC (proper name) each go to two ‘separate’ pathways. Some HVC neurons connect directly with neurons in a brain area called RA (robust nucleus of the archopallium), which in turn connects with the motoneurons that control the muscles in the vocal control organ (syrinx). Another set of HVC neurons connect through what is called the anterior forebrain pathway, a collection of cortical, thalamic and basal ganglia nuclei that are important for birds to learn their song. The two pathways talk to each other through a nucleus called LMAN that sends a direct input to RA.
The anterior forebrain pathway sends an error signal through the connections from LMAN to RA to ultimately control the motoneurons in nXIIts to produce the desired song structure. What is puzzling about the circuit is how the precise timing for this to operate efficiently is achieved. Because it takes time for the action potential to travel down the axon, and because it takes time for information to travel through synapses, the anterior forebrain pathway roundabout way (HVC-to-X-to-DLM-to-LMAN-to-RA) should be much slower than the speed of travel of information from HVC to RA. And this is precisely what Arthur Leblois, Agnes Bodor, Abigail Person and David Perkel examined.
To determine this, they electrically stimulated HVC and recorded from area X, DLM and LMAN, and were able to explore the mechanisms by which information travels around the anterior forebrain pathway as well as how long it takes to get from one point to another (latency).
How is transmission routed along the anterior forebrain pathway?
What they found is that low intensity stimulation from HVC produces excitation of area X neurons, but that higher intensity stimulation also produces a rapid inhibitory input from local area X circuits. One of the effects of this early inhibition is a lengthening of the time interval between consecutive action potentials in the neurons in area X that project to DLM (pallidal neurons).
DLM is normally inhibited by pallidal neurons in area X. But if the time interval between action potentials in the pallidal neurons is increased, it releases the ‘veto’ signal on DLM neurons which can then fire action potentials (either in response to other excitatory inputs or as a result of ‘post inhibitory rebound’). Based on the results, DLM neurons will therefore become activated (and in turn activate LMAN) when the local inhibition in area X (in this case triggered by HVC stimulation at high intensity) lengthens the time period between action potentials in the pallidal neurons. This is consistent with the observation that responses in LMAN could only be elicited by high levels of stimulation in HVC.
In this way, an input from HVC sufficient to elicit fast inhibition in area X, removes the veto signal on neurons in DLM, which are then able to discharge and excite LMAN, which can then send the appropriate signals to RA.
Does the timing work?
The short answer is yes. First, the authors showed that although the path-length between HVC-Area X and that of Area X-DLM, are similar the conduction times are much smaller in the latter. This, they suggest, is achieved both by an increase in diameter of the axons projecting from AreaX to DLM, axons which are myelinated even within DLM. The population latency in DLM and LMAN following HVC stimulation is very similar, but the authors argue that perhaps the population of DLM neurons that have the shortest latencies that are the ones that are playing the key role.
The specialisations in axonal morphology and myelination of the pallidal neurons may be an evolutionary adaptation that contributes to a short latency pathway that can modulate fine temporal features of song production.
Leblois, A., Bodor, A., Person, A., & Perkel, D. (2009). Millisecond Timescale Disinhibition Mediates Fast Information Transmission through an Avian Basal Ganglia Loop Journal of Neuroscience, 29 (49), 15420-15433 DOI: 10.1523/JNEUROSCI.3060-09.2009
Most (if not all) questions about neuroscience can be answered with <blah blah blah> Calcium (or so it was rumoured at the Neural Systems and Behaviour Course in the MBL back in the ‘90s). Humour aside, there is some truth to the statement, and Sheng Wang, Luis Polo-Parada and Lynn Landmesser examined the role of calcium changes in developing motoneurons.
Their work looked at how calcium changes may be associated with the process through which neurons in the spinal cord find their target muscles, and they did so in a well known system, one that Lynn Landmesser has dedicated most of her career to. The neurons in the spinal cord at the lumbosacral level are organized in longitudinal columns that span several vertebral segments. Neurons in each column will connect with a very specific leg muscle. This means that neurons at different spinal levels, but innervating the same muscle, will have their axons come out through different spinal nerves. All of the axons from different nerves come together at the plexus at the base of the limb where they sort out; axons that will connect with the same muscle become clustered. This has become a wonderful system in which to study how neurons know ‘who’s who’, and make sure they just ‘stick with their own kind’, an important process that avoids incorrect innervation patterns during development.
Also during development, the motoneurons become electrically active, producing burst of rhythmic electrical activity. The patterns of activity are characteristic of each motoneuron pool (that is, the group of motoneurons innervating an individual muscle), and changing the normal rhythm produces errors in axon guidance. Because calcium is known to be involved in many cellular responses, and because electrical activity can increase the levels of calcium inside the cell, the group looked at how calcium in the cell was changing during the bursts of electrical activity.
They found that the electrical rhythmic activity produced calcium transients in early developing motoneurons, even in some that were still migrating towards their final position in the spinal cord. All motoneurons were initially quite synchronous with respect to the calcium changes, but the duration of the calcium transients was different in different motoneuron pools. These differences in duration in the calcium transient could contribute to the downstream signaling that leads to the identity-specific behavior of the axons in the periphery.
One interesting finding is that blocking non alpha-7 nicotinic receptors blocked the spontaneous bursting but did not prevent calcium transients from happening under electrical stimulation. Further, although these channels underlie the bursting activity under normal conditions, the calcium transients were able to propagate across motoneurons while the channels were still blocked. This suggests that although these receptors may normally be involved in the production of electrical bursts, other neurotransmitter systems may be able to operate to allow the propagation of calcium transients.
As the authors suggest, the next step will be to see whether the difference in the duration of calcium transients in different motoneuron pools are sufficient to produce the phenotypic differences that provide each motoneuron with its ability to recognize its ‘own kind’ and find their way to the correct target.
Wang, S., Polo-Parada, L., & Landmesser, L. (2009). Characterization of Rhythmic Ca2+ Transients in Early Embryonic Chick Motoneurons: Ca2+ Sources and Effects of Altered Activation of Transmitter Receptors Journal of Neuroscience, 29 (48), 15232-15244 DOI: 10.1523/JNEUROSCI.3809-09.2009
Disclaimer: Lynn Landmesser was my PhD supervisor.
The endbulb or calyx of Held is a very large synapse found in the auditory system. It consists of a very large ‘calyceal’ ending, literally wrapping around the cell body of the postsynaptic neuron. It was first described by H Held in the late 1800’s and has since been shown to characteristically be present in neuronal circuits that require very high temporal precision. (It is, by the way, my favourite synapse.)
Because the synapse is so large, there are numerous sites of contact where the neurotransmitters are released, which will happen whenever an action potential reaches the synaptic terminal. Because of this, it has always been thought that these synapses never fail to produce a response (action potential) on its target (postsynaptic) neuron, that is, that it is a fail-safe synapse: every time that there is neurotransmitter release, the postsynaptic neuron produces an action potential.
But is this true?
Jeannette Lorteije, Silviu Rusu, Christopher Kushmerick and Gerard Borst examined precisely this, and they did so in a series of really elegant experiments in mice. They examined whether the discrepancies in the data regarding the degree of reliability at the enbulb or calyx of Held could be attributed to different methodological approaches or differences in the interpretation of the raw data. To examine this they did a series of recordings from cells in the Medial Nucleus of the Trapezoid Body (MNTB), which is part of the mammalian auditory system. The authors conclude that that there is a significant incidence of failures of transmission at this level of the system.
This is in contrast with the results reported by Bernard Englitz, Santra Tolnai, Marey Typlt, Jürgen Jost and Rüdolf Rübsamen. Here the authors recorded the failure at the endbulb of Held in the auditory cochlear nucleus AVCN and the calyx of Held in the MNTB in mongolian gerbils. They report that although failures of transmission were often found in AVCN, this was not the case in MNTB.
Synaptic structures analogous to the endbulb or calyx of Held are found in neuronal circuits that require high temporal precision. In the auditory system high temporal resolution is necessary for the measurement of interaural time differences, which in mammals are used to localize low frequency sound in the horizontal plane. Benedikt Grothe has argued that low frequency hearing appeared later in mammalian evolution, and that anatomical differences in a nucleus that receives inputs from the MNTB and is involved in the detection of interaural time differences (MSO) reflect this evolution. He argues that although MSO may have evolved to detect ITDs in low frequency hearing mammals (such as gerbils), its function may be different in higher frequency hearing mammals. On therefore wonders whether the differences in the data between the two studies may be related to adaptations associated with different temporal processing requirements in mammals with different frequency hearing ranges.
What did Lorteije and collaborators do?
In order to decide whether there are times in which synaptic release fails to elicit an action potential on the target cell, one needs to simultaneously monitor the activity happening at the synapse as well as at the postsynaptic neuron. There are traditionally two ways of doing this: One is to record the currents near the synapse that are produced by the electrical activity of the synapse and the cell, and the endbulbs of Held are large enough to produce sufficient current that can be detected. The other is to actually record the activity simultaneously from the cell and the synaptic terminal, which is usually done in an ‘in vitro’ preparation.
Lorteije and colleagues produced a set of data that is simply amazing, and their findings explain many of the discrepancies that can be found in the literature. They answered some very straightforward questions:
- Are the extracellular recordings done in vivo representative of what is actually going at a single endbulb-neuron contact? (the answer is yes)
- Is there synaptic release that fails to produce an action potential in the postsynaptic neuron? (the answer is also yes)
- Is the short term synaptic depression seen in vitro also seen in the whole animal (in vivo)? (Short term depression is a reduction in the effect of synaptic release on the postsynaptic cell.). (The answer is basically no)
The authors recorded from cells in the Medial Nucleus of the Trapezoid Body (MNTB), which receives inputs in the form of the large calyces of Held and is involved in auditory processing. They did this by recording the spontaneous and auditory-evoked activity extracellularly (as most people do) as well as directly from the cells with a patch pipette in anaesthetized mice. They then repeated these experiments in vitro, this time simultaneously recording extracellularly and in whole cell patch, which allowed them to confirm that the extracellular recordings in vivo did indeed represent the activities of the terminal and the cell and that it could also provide information as to the size of the synaptic potential. Their results have two important findings:
- in vivo there is no observable short term synaptic depression. The synaptic depression observed in vitro may be partly due to the concentration of Calcium in the bathing solution, but other factors may be involved.
- They also found that the release of neurotransmitter at the synapse often failed to produce an action potential in the postsynaptic cell. A similar rate of failure to that observed in vivo can be obtained in vitro by lowering the calcium concentration of the bathing solution.
The authors summarize their findings by saying:
“Due to its low release probability and large number of release sites, its average output can be kept constant, regardless of firing frequency. Its low quantal output thus allows it to be a tonic synapse, but the price it pays is an increase in jitter and synaptic latency and occasional postsynaptic failures.”
This is a carefully designed study, and despite my concerns as to whether their results are generalizable to other mammals, they do provide data that will be welcome by many auditory neurophysiologists. Their ability to record from a patch in vivo is no small feat, and the correlation between intracellular and extracellular data is extremely useful. Further, there is a cautionary tale around the way that data obtained from in vitro data can be interpreted.
And if you think this post is long, try reading the paper! (There are heaps more gems in there.)
Lorteije, J., Rusu, S., Kushmerick, C., & Borst, J. (2009). Reliability and Precision of the Mouse Calyx of Held Synapse Journal of Neuroscience, 29 (44), 13770-13784 DOI: 10.1523/JNEUROSCI.3285-09.2009
Englitz, B., Tolnai, S., Typlt, M., Jost, J., & Rübsamen, R. (2009). Reliability of Synaptic Transmission at the Synapses of Held In Vivo under Acoustic Stimulation PLoS ONE, 4 (10) DOI: 10.1371/journal.pone.0007014
Grothe, B. (2000). The evolution of temporal processing in the medial superior olive, an auditory brainstem structure Progress in Neurobiology, 61 (6), 581-610 DOI: 10.1016/S0301-0082(99)00068-4