Archive for the ‘100 Questions’ Category

One of the main roles of the semicircular canal system is to maintain the location of eye focus despite any head movements. The three pairs eye muscles are matched by the three planes of the semicircular canals, and each plane of the canals have direct control over one of these muscle pairs–either the medial and lateral rectus, or the superior and inferior rectus, or the inferior and superior oblique. This control must occur quickly in order to be effective. Eye movements lag head ones by only 10 milliseconds, and the pathway from semicircular canal to eye muscle contains only three feedforward neurons, making this vestibular-ocular reflex (VOR) one of the fastest in the human body.

Researchers in this field define “gain” as magnitude of the eye movement velocity divided by the magnitude of the head movement velocity during head turns in darkness. In typical primates the gain should be 1.0, but the reflex shows adaptation via motor learning if the image tends to have additional movement in one direction or the other. For example, if head movements lead to image movement in the opposite direction, the reflex will adapt to increase the gain and keep pace with the image. If the image movement is in the same direction as the head movement, the gain will decrease to compensate. So the VOR reflex in primates is, in a sense, a model system for plasticity, and in particular for simple motor learning. The debate over the past quarter century has focused on which of the following hypotheses best explains how this motor learning occurs:

1) The cerebellum stores the memory for the VOR adaptation. In this model, head movements lead to visually-dependent climbing fiber activity and semicircular canal-dependent parallel fiber activity. The climbing fibers report error between eye velocity and target velocity, leading to a change in the postysnaptic activity of Purkinje cell synapses and altering their synaptic weights with the parallel fibers. This change in synapse strength encodes the altered motor memory. Evidence: Lesions to the cerebellar flocculus eliminate VOR adaptation; activity of floccular Purkinje cells is highly correlated to adaptation of the VOR in monkeys (see here); and injecting 10 μM hemoglobin (to absorb N2O, diminishing cerebellar synaptic plasticity) into the subdural space over the flocculus on the same side as the observed eye eliminates VOR adaptation in monkeys. Moreover, when Nagao and Kitazawa (2003) injected the depressant lidocaine into the floculli, their monkey’s immediately reversed the VOR adaptation they had learned during 2 hours of visual–vestibular training.* These results indicate that the flocculus is at least necessary for short term cerebellar memory.

2) The role of cerebellum is to compute signal guiding induction of plasticity, but not to store the motor memory. In this model, Purkinje cells in the cerebellum convey the instructive error signal to the vestibular nucleus of the pons and medulla. So, the synaptic changes necessary for memory encoding occur between the axons aferrent to and neurons in the vestibular nucleus. Evidence: It’s possible that the correlations of floccular Purkinje cells and adaptation of the VOR in monkeys (from above) can be better explained by altered input to the cerebellum from mossy fibers that relays an efference copy of adaptation stored in the vestibular nucleus. To test this, researchers isolate the input from vestibular pathways to cerebellar Purkinje cells, possibly by using VOR cancellation. They find that the pattern of Purkinje cell sensitivity is opposite to that required by the VOR adaptation, meaning that VOR plasticity could not be dependent upon the Purkinje cells. Moreover, lesions to the cerebellum do not completely eliminate the memory of VOR adaptation. Finally, Porill and Dean’s (2007) computer sim that includes a realistic 100 ms delay before a report of the retinal error to the cerebellum prevents cerebellum-based learning above frequencies of 2.5 Hz, even though VOR adaptation can occur at ranges up to 25 Hz. In order to account for the biological VOR adaptation capability, extracerebellelar plasticity must be postulated. This second theory now seems to be the consensus view.

For more recent work on the role of the cerebellum and brainstem in the VOR, see here and here. The general lesson I draw from this research is that models based heavily on anatomy, like the classical Marr-Albus-Ito theory (#1) can look very appealing but must be heavily validated just like any other theory before they can be accepted.

* During the adaptation phase the monkeys in the lidocaine group had an increase in gain from 0.76 +/- 0.05 to 0.95 +/- 0.05, but this gain decreased to 0.76 +/- 0.08 only 10 minutes after injection of lidocaine. As opposed to the control solution in which the adaptation remained steady for ~ 1 hour of darkness.


Tabata K, et al. 2002 Computational Study on Monkey VOR Adaptation and Smooth Pursuit Based on the Parallel Control-Pathway Theory. J Neurophysiol. Link.

Nagao S, et al. 2003 Effects of reversible shutdown of the monkey flocculus on the retention of adaptation of the horizontal vestibulo-ocular reflex. Neuroscience doi:10.1016/S0306-4522(02)00991-0 .

Nagao S, et al. 1991 Subdural application of hemoglobin to the cerebellum blocks vestibuloocular reflex adaptation. Neuroreport. PubMed.

Porrill J, et al. 2007 Cerebellar Motor Learning: When Is Cortical Plasticity Not Enough? PLOS Comp Bio. Link.

Boyden ES, et al. 2004 Cerebellum-dependent learning: the role of multiple plasticity mechanisms. Annu. Rev. Neuroscience. doi: 10.1146/annurev.neuro.27.070203.144238

De Schutter E, et al. 1996 The cerebellum: cortical processing and theory. Current Opinion in Neurobio. Link.


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The crustacean stomatogastric nervous system has some particularly attractive features as a model system for neuroscience. Both their 14-cell pyloric network and 11-cell gastric mill network are anatomically separated from the rest of the nervous system and produce distinct motor patterns, allowing researchers to study the properties of each network individually. Here are three of the major findings from this research:

1) Many if not most neurons in the network have intrinsic modes of firing, including endogenous repetitive bursting, rhythmic oscillatory capability, bistability, post-inhibitory rebound, and spike adaptation. Real-life neurons do not just integrate and fire! These different modes of activity can be modulated by the opening / closing or activation / deactivation of various types of ion channels (i.e., types of Na, Ca, K, Cl). In order to model the neural networks accurately with respect to their known biological activity, researchers must incorporate these complex intrinsic properties of the component neurons. Knowledge of the synaptic connections alone is not enough.

2) The neural networks are subject to extensive biological modulation via injection of hormones, stimulation of input nerves, or some other manipulation. For example, the  electrophysiological properties of a given neuron can be changed, inducing an intrinsic mode of firing, or the synaptic connections between neurons can be made stronger or weaker. Enough of these changes can produce a distinct motor output–there is no evolutionary need for a separate neural network for each motor output.

3) Individual networks that appear independent will interact with one another in non-trivial ways given the appropriate environmental cues. It is useful to consider each network as a unit central pattern generator segment that can aggregate with others to form more complex behaviors. This is true of other animals too. For example, Cramer and Keller (2006) found that microstimulation of the vibrissae representation in the motor cortex of rats in frequencies of 50–90 Hz led to evoked whisking frequencies of 5 to 15 Hz, suggesting that the stimulation activated a subcortical central pattern generator instead of allowing for direct motor control.

Research into simple neural networks such as the STNS and C. elegans is indicative of  the progress in neurobio generally: Until we can accurately model these systems, there is little reason to suspect that we will be able to accurately model systems with even more neurons and connections.

Inspired by CalTech’s Question #10 for cognitive scientists: “Describe several main findings resulting from the study of the crustacean stomatogastric nervous system and their implications for the study and understanding of local circuit function in larger, more complex systems.”


Harris-Warrick RM, et al. 1992 Dynamic Biological Networks: The Stomatogastric Nervous System. Parts available on Google Books here.

Cramer NP, Keller A. 2006 Cortical Control of a Whisking Central Pattern Generator. J Neurophysiol doi:10.1152/jn.00071.2006 .

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The fact that so many sensory systems eventually project to the cerebral cortex indicates that there are likely similarities in their processing. Evolutionarily, it’s plausible that most neural sensory systems diverged from one common system that involved early analogue of the cortex.

Within mammals, there is a large amount of variation in the number of cortical areas, with most of these subdivisions in all mammals involved in sensory-perceptual tasks. Additionally, some sensory regions of the neocortex have been well preserved throughout the mammalian lineage, including visual cortices V1 / V2, somatosensory cortices S1 / S2, and possibly the primary auditory cortex A1. In order to add additional regions from this baseline amount, evolution might have either selected for rapid duplication of one existing region, or the gradual differentiation of one region into two.

In the visual pathway, the different brain regions contain neurons with different cell morphologies that are arranged in hierarchical fashion. Elston et al gathered data from layer III pyramidal neurons of V1, V2, the medial temporal lobe, V4, the inferior temporal cortex, and finally the superior temporal polysensory of the macaque. When they arranged the data from the pyramidal cells in order they found a beautiful pattern in the basal dendritic field areas, spine densities, and somal areas. I’ve reproduced part a of their figure 2 below but check out the open-access pdf of their paper for all of the data:

This data shows that there is a hierarchical organization of pyramidal cell morphology in the visual system. If there is indeed a common form of neural processing between the various types of sensory systems, we should expect a similar hierarchical organization of cells in non-visual sensory systems as well.

Inspired by Inspired by CalTech’s Question #9 for cognitive scientists: “In mammals, somatosensory, visual, auditory, and olfactory sensory systems all project to the cerebral cortex. To what extent does this imply some common form of neural processing? Justify your answer by referring to and comparing specific details of cortical anatomy and physiology.”


Kaas JH. 1989 The evolution of complex sensory systems in mammals. Pdf.

Elston GN, Tweedale R, Rosa MG. 1999 Cortical integration in the visual system of the macaque monkey: large-scale morphological differences in the pyramidal neurons in the occipital, parietal and temporal lobes. Proc Biol Sci. 1999 July 7; 266(1426): 1367–1374. PubMed link here.

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Lakatos et al have noted that “the role of neuronal oscillations in brain operations has been debated since the discovery of the electroencephalogram.” So this is no small cookie. But the current consensus seems to be that gamma waves with frequencies between 25 and 100 Hz are necessary for sensory processing.

Here’s the theory: Activated groups of neurons have the tendency to oscillate in coherent fashion, affecting the output of the given group and its sensitivity to input. Thus, two groups of neurons will be able to communicate much more effectively if their oscillations are phase-locked. Conversely, neural groups that don’t have this frequency oscillation synchrony will have a much reduced capability to communicate. So, incoming sensory info from the currently attended stimulus will have an advantage during recieving in upstream cortical regions. Also, a “broadcasting center” in the thalamus could distribute the selected rhythm to appropriate cortical regions and prime them to preferentially recieve certain frequencies of sensory input.

The theory implies that the spike-traveling time from sending to recieiving group must be timed correctly and have high fidelity. This is true of afferent thalamocortical axons. Spikes in thalamic neurons arrives in cortical cells in between 1-4 ms and peaks at 2 ms. Indeed, Salami et al (2003) found that the conduction velocity along axons on the thalamocortical tract is 10 times faster than other afferents in mice, due to its selective myelination. So this particular tract must be selected from development to be able to have a low latency interaction with the cortex.

The empirical work I’ve seen backs this theory up. For example, Dockstader et al (2010) used MRI and MEG on healthy participants while administering electrical current stimuli for 0.2 ms just above motor threshold. The participants attended either to the electrical stimuli or a distracting video. Selective attention to the electrical stimuli significantly increased activation in the early phase-locked contralateral primary somatosensory cortex gamma response, starting at 20 ms post-stimuli presentation. This shows that selective attention is likely mediated in neural circuits via gamma oscillations.

Note: Very high oscillations (100-500 Hz) are associated with epilepsy, and those between 250 and 500 Hz are often identified via EEG near the onset of focal seizures. The consensus on the role for lower Hz delta-range oscillations is much less distinct, but it may also be involved in early sensory selection. It is of course possible and likely that there are multiple roles for the gamma waves, and that sensory integration is only one of them.

Inspired by CalTech’s Question #8 for cognitive scientists: “What do you know about high-frequency oscillations (20-50 Hz) in invertebrates or vertebrates? What causes them?”


Salami M, et al. 2003 Change of conduction velocity by regional myelination yields constant latency irrespective of distance between thalamus and cortex. doi: 10.1073/pnas.0937380100 .

Fries P. 2005 A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. doi:10.1016/j.tics.2005.08.011 .

Dockstader C, et al. 2010 Cortical dynamics of selective attention to somatosensory events. doi:10.1016/j.neuroimage.2009.09.035 .

Jirsch JD. 2006 High-frequency oscillations during human focal seizures. doi:10.1093/brain/awl085.

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Visual: Path goes, photoreceptor cells in retina –> ganglion cells — (via optic nerve) –> optic chiasm (crossing of axons) –> lateral geniculate nucleus (mostly) + superior colliculi (can mediate saccades) + pretectal area (pupillary light reflex: if light shined in one eye, both pupils constrict). Then from lateral geniculate nucleus — (via optic radiations) –> V1 –> V2 –> parietal visual cortical areas (moving objects around in your head) + temporal visual areas (complex perception of patterns and forms). See here.

Auditory: Path starts in the hair cells of the cochlea, specifically the center axis called the spiral ganglion — (via auditory nerve) –> cochlear nucleus –> location of sound detection: (ventral cochlear nucleus –> superior olive of medulla) + quality of sound: (dorsal cochlear nucleus: frequency differences) — (via lateral lemniscus fiber tract) –> inferior colliculus –> auditory nucleus of the sensory thalamus (aka medial geniculate nucleus) –> primary auditory cortex of temporal lobes. See here.

Olfactory: Path is from olfactory receptors of roof of the nasal cavity — (via axons of receptors projecting as first cranial nerve) –> olfactory bulb — (via axons of mitral cells projecting as olfactory tract) –> olfactory cortex — (via mediodorsal nucleus of the thalamus) –> insular cortex (taste integrates with smell to produce flavor) + orbitofrontal cortex (odor-taste association learning at single neuron level). Notice that olfaction is the one sensation that gives info directly to the cortex from receptors without first passing through the thalamus. See here.

However, there are many multisensory connections in the cerebral cortex, superior colliculus, and thalamus, such that our sensations can feedback and “correct” one another well before conscious awareness. For example, the premotor cortex achieves multisensory integration by converging visual, auditory, and somatosensory inputs, and has large amounts of overlap with axons of various sensory systems sending projections to other cortical regions. These integration mechanisms vary by behavioral task as well type of sensory input and context must be taken into account.

Inspired by CalTech’s Question #7 for cognitive scientists: “Describe the main pathways between sensory receptors and cortex (including intra-cortical circuits) for mammalian vision, hearing and olfaction.”


Critchley HD, Rolls ET. 1996 Olfactory neuronal responses in the primate orbitofrontal cortex: analysis in an olfactory discrimination task. Abstract.

Cappe C, et al. 2009 Multisensory anatomical pathways. doi:10.1016/j.heares.2009.04.017.

The Washington University School of Medicine Neuroscience Tutorial, here.

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Auditory: Mechanical basilar membrane displacement in the cochlea opens mechanoelectrical transduction channels in hair cells, allowing an influx of potassium (K+) mediated current. This leads to the downstream neural pathways for hearing. Amplification of this signal across the entire hearing range occurs as a result of one specific subset of these hair cells, the outer hair cells. In response to the transmembrane receptor potential, outer hair cells actively oscillate at the frequency of the incoming sound, in a process called electromotility. It is believed that these voltage-dependent cell vibrations are dependent upon a protein called prestin that is expressed highly in the lateral plasma membrane of the outer hair cells. This is supported based on Liberman et al’s homozygous gene disruption paradigm in mice that led to a greater than 100 fold loss of auditory sensitivity. Its mechanism seems to mediate the electroneutral exchange of two anions across the plasma membrane, chlorine (Cl -) and carbonate (CO2 -3), which causes a direct voltage to displacement conversion. The unique morphology of the outer hair cells allows them  to operate at frequencies higher than 50  kHz if properly stimulated. This amplification via outer hair cells only occurs in mammals and allows for improved frequency selectivity, which is necessary for the complexities of human speech!

Olfactory: The mucous epithelial layer of the nose contains olfactory receptor neurons, each of which has 8-20 whip-like cilia that are each 30-200 microns long, and are where molecular reception of the odor commences. The incoming odor stimulates the transmembrane protein andenylate cyclase which catalyzes the conversion of ATP to 3′,5′-cyclic AMP (cAMP). cAMP is directly connected to an ion channel, allowing an influx of cations (primarily calcium) that depolarize the cell. The influx of calcium leads to an opening of calcium-dependent chloride channels. The chloride conductance is the amplification step and accounts for 80-90% of the odorant-induced depolarizing current, in sigmoidal fashion. Lowe and Gold (1993) had a classic study showing the effects of this. Using a newt olfactory receptor cell, they used flash photolysis of caged cAMP to simulate an upstream odor reception and analyzed the chloride influx. In addition to varying the intensity of the light source to uncage varying amounts of cAMP, and measuring the chloride amplitude, the researchers also used a condition with 5 millimoles of a chloride channel blocker, SITS. Here’s their figure 3D. The normal condition shows a sigmoidal amplification curve (black circles), and the SITS condition of open circles has no such amplification:

Very cool!

Inspired by CalTech’s Question #6 for cognitive scientists: “Every sensory system relies on receptor cells that transduce a stimulus into an electrical signal. This clearly requires some significant amplification. Describe two different sensory receptor cells, with attention to the location(s) and the mechanism(s) of this amplification.”


Liberman MC, et al. 2002 Prestin is required for electromotility of the outer hair cell and for the cochlear amplifier. doi:10.1038/nature01059.

Mistrik P, et al. 2009 Three-dimensional current flow in a large-scale model of the cochlea and the mechanism of amplification of sound. doi: 10.1098/​rsif.2008.0201.

Stephan AB, et al. 2009 ANO2 is the cilial calcium-activated chloride channel that may mediate olfactory amplification. doi: 10.1073/pnas.0903304106.

Lowe G and Gold GH.  1993 Nonlinear amplification by calcium-dependent chloride channels in olfactory receptor cells. doi:10.1038/366283a0

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The limbic system is a set of brain regions that combine to control long term memory, emotion, and olfaction. There are many areas of the brain that are sometimes considered a part of the system, but the main ones are the amygdala, the hippocampus, the cingulate gyrus, the hypothalamus, the thalamus, and the fornix, all except the last of which are described here.

It has somewhat of a spotted history. As Wikipedia’s article explains, it was originally assumed to control emotion while the neocortex controls cognition, but evidence has since come out stating that the truth is more integrated than that (ie, both the limbic system and the neocortex process at least a little bit of both cognition and emotions). The journal mentions per year (via Scopus) have been declining for the search “limbic system” when normalized to the mentions of the word “brain,” as you can see here:

Nevertheless, the concept of a limbic system is still a useful paradigm for some scientific research. Brankovic (2008) used it as a general brain region where symptoms from patients suffering from depression would manifest themselves. Turner et al (2008) used it as general brain region that was affected when the ventral pallidum was activated in a rat model of Parkinson’s disease. Although it may not totally reflect the reality in the brain, it represents a simplifying assumption that may help spur research.

Inspired by CalTech’s Question #5 for cognitive scientists: “What is the limbic system?”


Turner MS, Gray TS, Mickiewicz AL, Napier TC. 2008 Fos expression following activation of the ventral pallidum in normal rats and in a model of Parkinsons Disease: Implications for limbic system and basal ganglia interactions. Brain Structure and Function 213:197-213. doi:10.1007/s00429-008-0190-4.

Brankovic SB. 2008 System identification of skin conductance response in depressionAn attempt to probe the neurochemistry of limbic system. Psychiatria Danubina 20:310-322.

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