Michael A. Farries

Evidence for “direct” and “indirect” pathways through the song system basal ganglia

Song learning in oscine birds relies on a circuit known as the “anterior forebrain pathway,” which includes a specialized region of the avian basal ganglia. This region, area X, is embedded within a telencephalic structure considered homologous to the striatum, the input structure of the mammalian basal ganglia. Area X has many features in common with the mammalian striatum, yet has distinctive traits, including largely aspiny projection neurons that directly innervate the thalamus and a cell type that physiologically resembles neurons recorded in the mammalian globus pallidus. We have proposed that area X is a mixture of striatum and globus pallidus and has the same functional organization as circuits in the mammalian basal ganglia. Using electrophysiological and anatomical approaches, we found that area X contains a functional analog of the “direct” striatopallidothalamic pathway of mammals: axons of the striatal spiny neurons make close contacts on the somata and dendrites of pallidal cells. A subset of pallidal neurons project directly to the thalamus. Surprisingly, we found evidence that many pallidal cells may not project to the thalamus, but rather participate in a functional analog of the mammalian “indirect” pathway, which may oppose the effects of the direct pathway. Our results deepen our understanding of how information flows through area X and provide more support for the notion that song learning in oscines employs physiological mechanisms similar to basal ganglia‐dependent forms of motor learning in mammals. J. Comp. Neurol. 484:93–104, 2005.

Organization of the songbird basal ganglia, including area X

Area X is a songbird basal ganglia nucleus that is required for vocal learning. Both Area X and its immediate surround, the medial striatum (MSt), contain cells displaying either striatal or pallidal characteristics. We used pathway‐tracing techniques to compare directly the targets of Area X and MSt with those of the lateral striatum (LSt) and globus pallidus (GP). We found that the zebra finch LSt projects to the GP, substantia nigra pars reticulata (SNr) and pars compacta (SNc), but not the thalamus. The GP is reciprocally connected with the subthalamic nucleus (STN) and projects to the SNr and motor thalamus analog, the ventral intermediate area (VIA). In contrast to the LSt, Area X and surrounding MSt project to the ventral pallidum (VP) and dorsal thalamus via pallidal‐like neurons. A dorsal strip of the MSt contains spiny neurons that project to the VP. The MSt, but not Area X, projects to the ventral tegmental area (VTA) and SNc, but neither MSt nor Area X projects to the SNr. Largely distinct populations of SNc and VTA dopaminergic neurons innervate Area X and surrounding the MSt. Finally, we provide evidence consistent with an indirect pathway from the cerebellum to the basal ganglia, including Area X. Area X projections thus differ from those of the GP and LSt, but are similar to those of the MSt. These data clarify the relationships among different portions of the oscine basal ganglia as well as among the basal ganglia of birds and mammals. J. Comp. Neurol. 508:840–866, 2008.

The oscine song system considered in the context of the avian brain: lessons learned from comparative neurobiology.

The oscine song system has emerged as one of the leading model systems for studying motor learning in vertebrates, combining an easily recorded behavior with a discrete neural substrate. That neural substrate seems to be distinct from other structures in the avian brain and thus is often studied in isolation. However, the song system is unlikely to have evolved ex nihilo, and should share some features with the parts of the avian brain from which it evolved. Identification of its evolutionary precursors should help us apply what we know about the song system to other vertebrate motor systems, and vice versa. Here, I review the homologies between parts of the avian and mammalian telencephala and explain where the song system nuclei reside in this context. The organization of the song system is then compared to other parts of the avian brain and the brains of nonoscine birds. Study of the nonoscine brain has revealed a ‘general motor pathway’ from caudolateral neostriatum (NCL) to intermediate archistriatum (Ai) that resembles the song system motor pathway in its anatomical organization. No part of this motor pathway projects directly to brainstem vocal or respiratory centers in nonoscines, but it does innervate a wide variety of motor and premotor neuron populations that mediate other behaviors. This general motor pathway may be accompanied by an ‘anterior forebrain pathway’ , suggesting that the song system is simply a specialization of a part of this preexisting circuit. This hypothesis has implications for how accessory structures of the song system (e.g. HVc shelf, LMAN shell) are regarded, can help explain how the forebrain vocal control systems of three avian taxa (parrots, hummingbirds, and songbirds) could have evolved independently yet be so similar in organization, and makes testable predictions concerning the anatomy of the song system and the nonoscine brain.

The Avian Song System in Comparative Perspective

Abstract: The song system of oscine birds has become a versatile model system that is used to study diverse problems in neurobiology. Because the song system is often studied with the intention of applying the results to mammalian systems, it is important to place song system brain nuclei in a broader context and to understand the relationships between these avian structures and regions of the mammalian brain. This task has been impeded by the distinctiveness of the song system and the vast apparent differences between the forebrains of birds and mammals. Fortunately, accumulating data on the development, histochemistry, and anatomical organization of avian and mammalian brains has begun to shed light on this issue. We now know that the forebrains of birds and mammals are more alike than they first appeared, even though many questions remain unanswered. Furthermore, the song system is not as singular as it seemed—it has much in common with other neural systems in birds and mammals. These data provide a firmer foundation for extrapolating knowledge of the song system to mammalian systems and suggest how the song system might have evolved.

Biophysical basis of the phase response curve of subthalamic neurons with generalization to other cell types

Experimental evidence indicates that the response of subthalamic neurons to excitatory postsynaptic potentials (EPSPs) is well described by their infinitesimal phase response curves (iPRC). However, the factors controlling the shape of that iPRC, and hence controlling the way subthalamic neurons respond to synaptic input, are unclear. We developed a biophysical model of subthalamic neurons to aid in the understanding of their iPRCs; this model exhibited an iPRC type common to many subthalamic cells. We devised a method for deriving its iPRC from its biophysical properties that clarifies how these different properties interact to shape the iPRC. This method revealed why the response of subthalamic neurons is well approximated by their iPRCs and how that approximation becomes less accurate under strong fluctuating input currents. It also connected iPRC structure to aspects of cellular physiology that could be estimated in simple current-clamp experiments. This allowed us to directly compare the iPRC predicted by our theory with the iPRC estimated from the response to EPSPs or current pulses in individual cells. We found that theoretically predicted iPRCs agreed well with estimates derived from synaptic stimuli, but not with those estimated from the response to somatic current injection. The difference between synaptic currents and those applied experimentally at the soma may arise from differences in the dynamics of charge redistribution on the dendrites and axon. Ultimately, our approach allowed us to identify novel ways in which voltage-dependent conductances interact with AHP conductances to influence synaptic integration that will apply to a wide range of cell types.

Predicting the Responses of Repetitively Firing Neurons to Current Noise

We used phase resetting methods to predict firing patterns of rat subthalamic nucleus (STN) neurons when their rhythmic firing was densely perturbed by noise. We applied sequences of contiguous brief (0.5–2 ms) current pulses with amplitudes drawn from a Gaussian distribution (10–100 pA standard deviation) to autonomously firing STN neurons in slices. Current noise sequences increased the variability of spike times with little or no effect on the average firing rate. We measured the infinitesimal phase resetting curve (PRC) for each neuron using a noise-based method. A phase model consisting of only a firing rate and PRC was very accurate at predicting spike timing, accounting for more than 80% of spike time variance and reliably reproducing the spike-to-spike pattern of irregular firing. An approximation for the evolution of phase was used to predict the effect of firing rate and noise parameters on spike timing variability. It quantitatively predicted changes in variability of interspike intervals with variation in noise amplitude, pulse duration and firing rate over the normal range of STN spontaneous rates. When constant current was used to drive the cells to higher rates, the PRC was altered in size and shape and accurate predictions of the effects of noise relied on incorporating these changes into the prediction. Application of rate-neutral changes in conductance showed that changes in PRC shape arise from conductance changes known to accompany rate increases in STN neurons, rather than the rate increases themselves. Our results show that firing patterns of densely perturbed oscillators cannot readily be distinguished from those of neurons randomly excited to fire from the rest state. The spike timing of repetitively firing neurons may be quantitatively predicted from the input and their PRCs, even when they are so densely perturbed that they no longer fire rhythmically.

How 'basal' are the basal ganglia?

years ago [Erwin et al., 2011], did not merely have a substantial rostral brain, but specifically a telencephalon, an elaborate set of forebrain structures hitherto recognized only in vertebrates. The set of structures that Strausfeld and Hirth [2013a] identify as the arthropod homolog of the basal ganglia is the central complex, a group of median neuropils located in the posterior part of the protocerebrum, the rostral-most major division of the arthropod brain. Their hypothesis is based on similarities in function, topological position, anatomical organization, neurotransmitter content and developmental genetics. Strong similarities in all these categories would indeed make a good case for homology between the central complex and basal ganglia, although the absence of these structures in the great majority of Bilaterian taxa would still pose serious problems. To usefully compare such manifestly (if perhaps superficially) different brains, one must not let differences in detail obscure deep parallels nor focus exclusively on similarities, i.e. one must chart a course between nit-picking and cherry-picking. This task is complicated by the fact that most of what we know about the ‘vertebrate’ basal ganglia is derived from the study of a handful of mammalian species. For example, what seems to be a ‘cardinal feature of the striatum’, the Of the roughly 35 metazoan phyla alive today, only 4 possess nervous tissue that is concentrated and elaborate enough to be called a ‘brain’, suggesting that brains evolved independently in these lineages [Holland, 2003; Northcutt, 2012]. However, growing knowledge of developmental genetics, particularly in vertebrates and insects, has revealed remarkable conservation in the genetic regulatory networks that pattern the developing embryo, including the developing nervous system [Carroll, 1995; Hirth and Reichert, 1999]. This has led to the hypothesis that chordates and arthropods inherited a tripartite rostral brain from their last common ancestor, which would make possession of such a brain the ancestral condition for the entire superclade of bilaterally symmetric animals (Bilateria); the many bilaterian phyla that lack such a brain would have lost it secondarily [Hirth et al., 2003]. A recent review by Strausfeld and Hirth [2013a] takes this a step further by arguing that arthropods have a homolog of the vertebrate basal ganglia, one of the major subdivisions of the telencephalon. Together with claims of a pallium homolog in annelids [Tomer et al., 2010] and suggestions of homology between the vertebrate olfactory bulb and the arthropod antennal lobe [Strausfeld, 2012], this implies that the common ancestor of all Bilateria, living more than 600 million Published online: November 26, 2013

Birdsong and the Brainstem

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Spike threshold dynamics shape the response of subthalamic neurons to cortical input

The subthalamic nucleus (STN) is a population of autonomously active glutamatergic neurons within the basal ganglia (BG) that innervates BG output nuclei and is reciprocally connected with the globus pallidus (GP). The STN receives cortical input and so forms a direct bridge to BG output nuclei that bypasses the striatum. The STNs response to cortical stimulation in vivo begins at very short latency (2-5 ms) and consists of two excitatory peaks divided by a brief period of inhibition. The brief inhibition is generally ascribed to disynaptic inhibition from the GP, but signs of cortically-evoked inhibition persist in some STN recordings made in rats with GP lesions. We investigated the contribution of intrinsic properties to the STNs response to cortical excitation by studying their response to cortical fiber stimulation in brain slices in the presence of GABAergic antagonists. Responses to relatively strong stimulation often exhibited two distinct excitatory peaks in the PSTH separated by a gap that resembles inhibition. The distribution of latencies to the first poststimulus spike could also exhibit this gap, so this effect cannot be attributed to the AHP of the first spike. We found that spikes fired shortly after the onset of large EPSPs were triggered at a substantially lowered threshold (2-7 mV). The threshold dropped rapidly (within 1-2 ms of EPSP onset) and rose quickly back to the baseline level (or to a slightly elevated threshold). This drop in threshold can explain the two peaks seen in PSTHs and latency distributions: the cell fires immediately when above the lowered threshold but must otherwise wait until reaching the higher baseline threshold if it misses the narrow low-threshold window. Thus, EPSP-evoked changes in spike threshold can both facilitate a rapid, short-latency response in the STN to strong cortical input and change the firing pattern evoked by that input. Smaller EPSPs advance the time of the next spike but evoke smaller changes in spike threshold that do not produce the appearance of an excitation-inhibition sequence. The change in firing pattern associated with large EPSPs could allow targets of STN projections to distinguish activity driven by sharp increases in cortical drive from autonomous or tonically-driven activity. A model of the STNs response to cortical input suggests that two modes of operation--a coincidence-detecting short latency response to sharp increases in excitation and a more subtle response to smaller fluctuations in synaptic drive--can coexist and operate in parallel.

Spike threshold dynamics reshape the phase response curve and increase the degree of synchronization among neurons coupled by excitatory synapses

The collective behavior of a neuronal population can often be illuminated by representing neurons as simple phase oscillators, where the response of each neuron to its synaptic input is given by an infinitesimal phase response curve (iPRC). This approach can, for example, predict whether neuronal activity will tend to synchronize across the population depending on the nature of the synaptic coupling and the shape of the iPRCs[1-3]. Despite the extreme simplicity of phase models, we have found that the response of subthalamic neurons to excitatory synaptic input is remarkably well described by iPRCs; this is probably true of many other cell types. However, one aspect of subthalamic neurons’ response to input may undermine the phase model description: spike threshold accommodation[4], a phenomenon also found in many other cell types. In spike threshold accommodation, spikes fired following more rapid depolarization are triggered at a lower voltage threshold. We developed a phenomenological model of spike threshold accommodation that treats the threshold itself as a dynamical variable; this model successfully accounted for the experimentally observed features of this phenomenon[4]. However, the introduction of a new dynamical variable raises the possibility that one-dimensional phase models will be unable to represent adequately the impact of spike threshold dynamics. This danger seems more pressing given that most of the threshold accommodation phenomenon arises from the fact that spikes are initiated in the axon at some distance from the soma[5,6], a factor that cannot be accounted for by standard methods for reducing a biophysical model to a phase model. We analyzed the effect of adding our phenomenological model of spike threshold dynamics to biophysical models that are otherwise well described by phase models. We discovered that spike threshold dynamics change the shape of the iPRC and that this effect alone accounts for much of this phenomenon’s impact on the response to synaptic input. Specifically, threshold dynamics increase the input sensitivity at late input phases, causing the iPRC to have higher values than one would otherwise predict. We show numerically that this alteration of the iPRC promotes synchronization of neurons coupled by excitation, even if those neurons have type I iPRCs. In addition to reshaping the iPRC itself, spike threshold dynamics also cause a deviation from the response predicted by the iPRC as the size of the stimulus grows; this effect further enhances the sensitivity to excitation at late input phases but suppresses the sensitivity to inhibition. We compare the results obtained with our phenomenological model of threshold dynamics to experimental data and to a full multicompartment biophysical model that exhibits spike threshold accommodation naturally, by virtue of the cable properties of the axon. We were able to explain some otherwise anomalous aspects of our data and predicted changes in iPRC shape as neurons are driven to fire at higher rates by DC current injection. We confirmed this effect experimentally in subthalamic neurons; a similar phenomenon has also been reported in cerebellar Purkinje neurons [7].