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The cerebellum is considered a "learning machine" essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellum's input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from time-varying inputs, typical during movements. The model robustly produced temporal basis sets from these inputs, and the resultant GCL output supported better learning of temporally complex target functions than mossy fibers alone. Learning was optimized at intermediate threshold levels, supporting relatively dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that moderately dense population activity optimizes learning.

Infinitesimal phase response curves (iPRCs) provide a simple description of the response of repetitively firing neurons and may be used to predict responses to any pattern of synaptic input. Their simplicity makes them useful for understanding the dynamics of neurons when certain conditions are met. For example, the sizes of evoked phase shifts should scale linearly with stimulus strength, and the form of the iPRC should remain relatively constant as firing rate varies. We measured the PRCs of rat subthalamic neurons in brain slices using corticosubthalamic excitatory postsynaptic potentials (EPSPs; mediated by both AMPA- and NMDA-type receptors) and injected current pulses and used them to calculate the iPRC. These were relatively insensitive to both the size of the stimulus and the cell's firing rate, suggesting that the iPRC can predict the response of subthalamic nucleus cells to extrinsic inputs. However, the iPRC calculated using EPSPs differed from that obtained using current pulses. EPSPs (normalized for charge) were much more effective at altering the phase of subthalamic neurons than current pulses. The difference was not attributable to the extended time course of NMDA receptor-mediated currents, being unaffected by blockade of NMDA receptors. The iPRC provides a good description of subthalamic neurons' response to input, but iPRCs are best estimated using synaptic inputs rather than somatic current injection.

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.

Although the basal ganglia of birds and mammals share an enormous number of anatomical, histochemical, and electrophysiological characteristics, studies in songbirds have revealed some important differences. Specifically, a specialized region of songbird striatum (the input structure of the basal ganglia) has an anatomical projection and a physiologically defined cell type that are characteristic of the globus pallidus. At present, it is not clear if these differences result from adaptations specific to songbirds and perhaps a few other avian taxa or are common to all birds. We shed some light on this issue by characterizing the morphology and electrophysiological properties of basal ganglia neurons in an avian species that is only distantly related to songbirds: the domestic chick. We recorded neurons in chick basal ganglia in a brain slice preparation, using the whole cell technique. We found that chick striatum, like songbird striatum, contains a pallidum-like cell type never reported in mammalian striatum, supporting the hypothesis that this feature is common to all birds. We also discovered that spiny neurons, the most common cell type in the striatum of all amniotes, possess a diverse set of physiological properties in chicks that distinguish them from both mammals and songbirds. This study revealed an unexpectedly complex pattern of conservation and divergence in the properties of neurons recorded in avian striatum.

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. © 2005 Wiley-Liss, Inc.

Vocal behavior in songbirds exemplifies a rich integration of motor, cognitive, and social functions that are shared among vertebrates. As a part of the underlying neural substrate, the song system, the anterior forebrain pathway (AFP) is required for song learning and maintenance. The AFP resembles the mammalian basal ganglia–thalamocortical loop in its macroscopic organization, neuronal intrinsic properties, and microcircuitry. Area X, the first station in the AFP, is a part of the basal ganglia essential for vocal learning. It receives glutamatergic inputs from pallial structures and sends GABAergic outputs to thalamic structures. It also receives dense dopaminergic innervation from the midbrain. The role of this innervation is essentially unknown. Here we provide evidence that dopamine (DA) can modulate the glutamatergic inputs to spiny neurons in area X. In whole-cell voltage-clamp recordings from neurons in brain slices of adult zebra finches, we found that activation of D1-like DA receptors depresses ionotropic glutamatergic synaptic current in area X spiny neurons. This effect is mediated by a presynaptic site of action, mimicked by activation of adenylyl cyclase, and blocked by protein kinase A inhibitor and an adenosine A1 receptor antagonist. These results suggest that, in addition to altering the input–output function of spiny neurons by modulating their excitability, as we have shown previously, DA can directly influence the excitatory inputs to these neurons as well. Thus, DA can exert fine control over information processing through spiny neurons in area X, the dynamics of the AFP output, and ultimately song learning and maintenance.

The song system of oscine birds has frequently been presented as a model system for motor learning in vertebrates. This practice has been bolstered by the growing recognition that one part of the song system that is essential for song learning, area X, is a component of the avian striatum. The mammalian striatum, the input structure of the basal ganglia, has been implicated in a number of motor-related functions, including motor learning, suggesting that song learning in birds and motor learning in mammals may use similar physiological mechanisms. We studied the intrinsic physiological properties of area X neurons in brain slices to see how closely they match properties identified in mammalian striatal neurons and to collect data that are necessary to understand how area X processes information. We found that area X contains all four physiological cell types present in the mammalian striatum and that each is very similar to its mammalian counterpart. We also found a fifth cell type in area X that has not been reported in mammalian striatum; instead, this cell type resembles neurons that have been recorded in the mammalian globus pallidus. This pallidum-like cell type morphologically resembles the projection neurons of area X. We suggest that area X contains a pathway equivalent to the “direct” striatopallidothalamic pathway through the mammalian basal ganglia, with the striatal and pallidal components intermingled in one nucleus.

The discrete, interconnected nuclei of the songbird brain, collectively termed the song system, underlie the learning and production of song. Two main forebrain pathways have been identified that contribute to song production, learning, and adult plasticity. A posterior "motor pathway" including nucleus HVc (used as the proper name), the robust nucleus of the archistriatum (RA) and descending projections to the brainstem, is essential for song production. An "anterior forebrain pathway," arising from HVc, passing through area X of the lobus parolfactorius, the medial portion of the dorsolateral nucleus of the anterior thalamus and the lateral magnocellular nucleus of the anterior neostriatum, and finally terminating in RA, is essential for song learning and adult plasticity. The fact that the lobus parolfactorius is thought to form a part of the avian striatum implies several predictions for the connections of area X and for the properties of its neurons. Here, we review the existing anatomical and electrophysiological data bearing on the nature of area X as a striatal structure. In general, the data strongly favor the notion that area X is striatal. One set of observations, however, is at odds with that idea, and we provide and partially test the hypothesis that area X also contains a pallidal component. We discuss further tests of this idea and implications for thinking of the song system as a basal ganglia loop similar to that described in mammals.

Injecting white noise currents has been shown to increase mean firing rate in several types of neuron. This speed up is generally attributed to the fact that depolarizing currents on the tails of the noise distribution push cells past spike threshold sooner than they would have otherwise. As part of a larger effort to understand the dynamic properties of rat subthalamic (STN) neurons, we have performed perforated patch recordings of these tonically active neurons in brain slices, exposing them to contiguous sequences of brief current pulses drawn from a Gaussian distribution. Pulsed noise disrupted firing pattern with no significant change in rate over a wide range of noise amplitudes, so long as the pulse durations were brief (

The forebrains of mammals and birds appear quite different in their gross morphology, making it difficult to identify homologies between them and to assess how far they have diverged in organization. Nevertheless one set of forebrain structures, the basal ganglia, has been successfully compared in mammals and birds. Anatomical, histochemical, and molecular data have identified the avian homologues of the mammalian basal ganglia and indicate that they are very similar in organization, suggesting that they perform similar functions in the two classes. However, the physiological properties of the avian basal ganglia have not been studied, and these properties are critical for inferring functional similarity. We have used a zebra finch brain slice preparation to characterize the intrinsic physiological properties of neurons in the avian basal ganglia, particularly in the input structure of the basal ganglia, the striatum. We found that avian striatum contains a cell type that closely resembles the medium spiny neuron, the principal cell type of mammalian striatum. Avian striatum also contains a rare cell type that is very similar to an interneuron class found in mammalian striatum, the low-threshold spike cell. On the other hand, we found an aspiny, fast-firing cell type in avian striatum that is distinct from all known classes of mammalian striatal neuron. These neurons usually fired spontaneously at 10 Hz or more and were capable of sustained firing at very high rates when injected with depolarizing current. The existence of this cell type represents an important difference between avian striatum and mammalian dorsal striatum. Our data support the general idea that the organization and functional properties of the basal ganglia have been largely conserved in mammals and birds, but they imply that avian striatum is not identical to mammalian dorsal striatum.

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., Author Summary Most neurons receive thousands of synaptic inputs per second. Each of these may be individually weak but collectively they shape the temporal pattern of firing by the postsynaptic neuron. If the postsynaptic neuron fires repetitively, its synaptic inputs need not directly trigger action potentials, but may instead control the timing of action potentials that would occur anyway. The phase resetting curve encapsulates the influence of an input on the timing of the next action potential, depending on its time of arrival. We measured the phase resetting curves of neurons in the subthalamic nucleus and used them to accurately predict the timing of action potentials in a phase model subjected to complex input patterns. A simple approximation to the phase model accurately predicted the changes in firing pattern evoked by dense patterns of noise pulses varying in amplitude and pulse duration, and by changes in firing rate. We also showed that the phase resetting curve changes systematically with changes in total neuron conductance, and doing so predicts corresponding changes in firing pattern. Our results indicate that the phase model may accurately represent the temporal integration of complex patterns of input to repetitively firing neurons.

The basal ganglia (BG)-recipient thalamus controls motor output but it remains unclear how its activity is regulated. Several studies report that thalamic activation occurs via disinhibition during pauses in the firing of inhibitory pallidal inputs from the BG. Other studies indicate that thalamic spiking is triggered by pallidal inputs via post-inhibitory ‘rebound’ calcium spikes. Finally excitatory cortical inputs can drive thalamic activity, which becomes entrained, or time-locked, to pallidal spikes. We present a unifying framework where these seemingly distinct results arise from a continuum of thalamic firing ‘modes’ controlled by excitatory inputs. We provide a mechanistic explanation for paradoxical pallidothalamic coactivations observed during behavior that raises new questions about what information is integrated in the thalamus to control behavior., National Institutes of Health (U.S.) (Grant R01DC009183), National Institutes of Health (U.S.) (Grant K99NS067062)

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

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.

The basal ganglia-recipient thalamus receives inhibitory inputs from the pallidum and excitatory inputs from cortex, but it is unclear how these inputs interact during behavior. We recorded simultaneously from thalamic neurons and their putative synaptically connected pallidal inputs in singing zebra finches. We find, first, that each pallidal spike produces an extremely brief (∼5 ms) pulse of inhibition that completely suppresses thalamic spiking. As a result, thalamic spikes are entrained to pallidal spikes with submillisecond precision. Second, we find that the number of thalamic spikes that discharge within a single pallidal interspike interval (ISI) depends linearly on the duration of that interval but does not depend on pallidal activity prior to the interval. In a detailed biophysical model, our results were not easily explained by the postinhibitory “rebound” mechanism previously observed in anesthetized birds and in brain slices, nor could most of our data be characterized as “gating” of excitatory transmission by inhibitory pallidal input. Instead, we propose a novel “entrainment” mechanism of pallidothalamic transmission that highlights the importance of an excitatory conductance that drives spiking, interacting with brief pulses of pallidal inhibition. Building on our recent finding that cortical inputs can drive syllable-locked rate modulations in thalamic neurons during singing, we report here that excitatory inputs affect thalamic spiking in two ways: by shortening the latency of a thalamic spike after a pallidal spike and by increasing thalamic firing rates within individual pallidal ISIs. We present a unifying biophysical model that can reproduce all known modes of pallidothalamic transmission—rebound, gating, and entrainment—depending on the amount of excitation the thalamic neuron receives.

The subthalamic nucleus (STN) is an autonomously active population of glutamatergic neurons that occupies a pivotal location within the basal ganglia, receiving direct cortical input and innervating the globus pallidus and substantia nigra. As intrinsically oscillating neurons, STN cells may be suitable candidates for reduction to phase models, where the state of each neuron is represented by a single number (phase) and their response to synaptic input is given by infinitesimal phase response curves (iPRCs). Phase models are analytically tractable and assist the study of large neural populations, but whether they can describe the behavior of STN cells with sufficient accuracy is an open question. We addressed this issue by measuring the iPRCs of STN cells in brain slices, using both glutamatergic synaptic input and brief current injection. We found that iPRCs measured synaptically were relatively insensitive to the size of the EPSP used to measure them, and that iPRCs measured with current pulses were the same regardless of the polarity of the current, at least during the first 60% of the oscillation cycle; both observations suggest that iPRCs might accurately describe the response of STN cells to finite inputs. However, we also found that iPRCs measured synaptically and by current injection were dramatically different, with current pulse iPRCs giving much less phase shift for a given stimulus amplitude. We developed and analyzed a biophysical model of STN cells, to better understand the nature of subthalamic iPRCs. This model produced an iPRC that closely resembled those we measured experimentally. Observing that this model consisted mainly of kinetically fast voltagedependent currents and AHP currents that can be treated as a function of time since the last spike, we explored the iPRCs of cells whose conductances are direct functions of voltage. Ignoring the spike itself by assuming that it is triggered at a certain threshold potential followed by return (2 ms later) to resetting potential, we found that the iPRC of such cells is a simple function of their IV curves and that their iPRC is valid for arbitrarily large currents. The contribution of AHP currents could therefore be covered by treating them as applied currents acting through this IV curve-based iPRC. Following this approach, we developed a method for deriving the iPRC of any cell well described by a combination of fast voltage-dependent currents and time-dependent AHP currents, covering a wide range of cell types. We were also able to estimate experimentally the IV curves and AHP currents of STN cells in which we had empirically determined the synaptic and current pulse iPRCs, allowing us to compare theoretically predicted iPRCs to experimentally measured iPRCs. We found that the theoretically predicted PRC approximated the synaptic–but not the current pulse–iPRC. We hypothesize that somatically injected charge slowly trickles into distal dendrites, reducing its effectiveness at advancing the phase, whereas synaptically delivered charge comes from precharged dendrites. We conclude that current injection is not an effective method for measuring iPRCs in dendritic neurons.

The subthalamic nucleus (STN) provides a second entry point for cortical input to the basal ganglia, supplementing the corticostriatal pathway. We examined the way intrinsic properties shape the response of the STN to cortical excitation, recording from rat STNin vivoand in brain slices. STN cells exhibited a near-zero slope conductance—and hence an effectively infinite membrane time constant—at subthreshold potentials. This makes STN cells exceptional temporal integrators, consistent with the common view that basal ganglia nuclei use rate coding. However, STN cells also exhibited a drop in spike threshold triggered by larger EPSPs, allowing them to fire time-locked spikes in response to coincident input. In addition to promoting coincidence detection, the threshold dynamics associated with larger EPSPs reduced the probability of firing spikes outside of a narrow window immediately after the stimulus, even on trials in which the EPSP did not directly trigger a spike. This shift in stimulus-evoked firing pattern would allow downstream structures to distinguish coincidence-triggered spikes from other spikes and thereby permit coincidence detection and rate coding to operate in parallel in the same cell. Thus, STN cells can combine two functions—integration and coincidence detection—that are normally considered mutually exclusive. This could support rapid communication between cortex and basal ganglia targets that bypasses the striatum without disrupting slower rate coding pathways.

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 STN's 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 STN's 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 STN's 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 timing–dependent synaptic plasticity (STDP) has emerged as the preferred framework linking patterns of pre- and postsynaptic activity to changes in synaptic strength. Although synaptic plasticity is widely believed to be a major component of learning, it is unclear how STDP itself could serve as a mechanism for general purpose learning. On the other hand, algorithms for reinforcement learning work on a wide variety of problems, but lack an experimentally established neural implementation. Here, we combine these paradigms in a novel model in which a modified version of STDP achieves reinforcement learning. We build this model in stages, identifying a minimal set of conditions needed to make it work. Using a performance-modulated modification of STDP in a two-layer feedforward network, we can train output neurons to generate arbitrarily selected spike trains or population responses. Furthermore, a given network can learn distinct responses to several different input patterns. We also describe in detail how this model might be implemented biologically. Thus our model offers a novel and biologically plausible implementation of reinforcement learning that is capable of training a neural population to produce a very wide range of possible mappings between synaptic input and spiking output.

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.

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.

Recently, two quite different approaches exemplifying ‘bottom-up’ and ‘top-down’ philosophies have shed new light on basal ganglia function. In vitro work using organotypic co-cultures has implicated the subthalamic nucleus (STN) and the external segment of the globus pallidus (GP e ) as pacemakers for low-frequency bursting that is reminiscent of the activity produced in Parkinsonian tremor. A circuit essential for avian song learning has been identified as part of the basal ganglia with surprisingly well conserved cellular details; investigation of this system may help to address general issues of basal ganglia function.

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].