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Chronic calcium imaging has become a powerful and indispensable tool for analyzing the long-term stability and plasticity of neuronal activity. One crucial step of the data processing pipeline is to register individual neurons across imaging sessions, which usually extend over a few days or even months, and show various levels of spatial deformation of the imaged field of view (FOV). Previous solutions align FOVs of all sessions first and then register the same neurons according to their shapes and locations [1, 2]. However, the FOV registration is computational intensive, especially in the case of nonrigid case.Here we propose a cell tracking method that does not require FOV image registration. Specifically, the algorithmSTAT(short forStayTogether,AlignTogether, and forSpatialTrackingAcrossTime) represents neurons from two sessions as two sets of neuronal centroids, uses point set registration (PSR) to find a spatially smooth transformation to align them while assigning correspondences. The optimization method iteratively updates between the general motion and individual neuron identity tracking, an idea seen in the computer vision literatures [3, 4]. Our method can be thought of as a specialization and simplification of these more general methods to calcium imaging neuron tracking.We validate STAT on datasets with simulated nonrigid motion that is hard to motion correct without extensive manual intervention. Next, we test STAT on experimental data from singing birds collected on three different days, and observe stable song-locked activity across days. An example use case of this package is reference [5].

The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries.

Behaviors emerge via a combination of experience and innate predis-positions. As the brain matures, it undergoes major changes in cellular, network and functional properties that can be due to sensory experience as well as developmental processes. In normal birdsong learning, neural sequences emerge to control song syllables learned from a tutor. Here, we disambiguate the role of experience and development in neural sequence formation by delaying exposure to a tutor. Using functional calcium imaging, we observe neural sequences in the absence of tutoring, demonstrating that experience is not necessary for the formation of sequences. However, after exposure to a tutor, pre-existing sequences can become tightly associated with new song syllables. Since we delayed tutoring, only half our birds learned new syllables following tutor exposure. The birds that failed to learn were the birds in which pre-tutoring neural sequences were most ‘crystallized’, that is, already tightly associated with their (untutored) song.

Learning turns experience into better decisions. A key problem in learning is credit assignment—knowing how to change parameters, such as synaptic weights deep within a neural network, in order to improve behavioral performance. Artificial intelligence owes its recent bloom largely to the error-backpropagation algorithm1, which estimates the contribution of every synapse to output errors and allows rapid weight adjustment. Biological systems, however, lack an obvious mechanism to backpropagate errors. Here we show, by combining high-throughput volume electron microscopy2and automated connectomic analysis3–5, that the synaptic architecture of songbird basal ganglia supports local credit assignment using a variant of the node perturbation algorithm proposed in a model of songbird reinforcement learning6, 7. We find that key predictions of the model hold true: first, cortical axons that encode exploratory motor variability terminate predominantly on dendritic shafts of striatal spiny neurons, while cortical axons that encode song timing terminate almost exclusively on spines. Second, synapse pairs that share a presynaptic cortical timing axon and a postsynaptic spiny dendrite are substantially more similar in size than expected, indicating Hebbian plasticity8, 9. Combined with numerical simulations, these findings provide strong evidence for a biologically plausible credit assignment mechanism6.

Neuroscience research has become increasingly reliant upon quantitative and computational data analysis and modeling techniques. However, the vast majority of neuroscientists are still trained within the traditional biology curriculum, in which computational and quantitative approaches beyond elementary statistics may be given little emphasis. Here we provide the results of an informal poll of computational and other neuroscientists that sought to identify critical needs, areas for improvement, and educational resources for computational neuroscience training. Motivated by this survey, we suggest steps to facilitate quantitative and computational training for future neuroscientists.

How are brain circuits constructed to achieve complex goals? The brains of young songbirds develop motor circuits that achieve the goal of imitating a specific tutor song to which they are exposed. Here, we set out to examine how song-generating circuits may be influenced early in song learning by a cortical region (NIf) at the interface between auditory and motor systems. Single-unit recordings reveal that, during juvenile babbling, NIf neurons burst at syllable onsets, with some neurons exhibiting selectivity for particular emerging syllable types. When juvenile birds listen to their tutor, NIf neurons are also activated at tutor syllable onsets, and are often selective for particular syllable types. We examine a simple computational model in which tutor exposure imprints the correct number of syllable patterns as ensembles in an interconnected NIf network. These ensembles are then reactivated during singing to train a set of syllable sequences in the motor network., Young songbirds learn to imitate their parents’ songs. Here, the authors find that, in baby birds, neurons in a brain region at the interface of auditory and motor circuits signal the onsets of song syllables during both tutoring and babbling, suggesting a specific neural mechanism for vocal imitation.

Songbirds learn and produce complex sequences of vocal gestures. Adult birdsong requires premotor nucleus HVC, in which projection neurons (PNs) burst sparsely at stereotyped times in the song. It has been hypothesized that PN bursts, as a population, form a continuous sequence, while a different model of HVC function proposes that both HVC PN and interneuron activity is tightly organized around motor gestures. Using a large dataset of PNs and interneurons recorded in singing birds, we test several predictions of these models. We find that PN bursts in adult birds are continuously and nearly uniformly distributed throughout song. However, we also find that PN and interneuron firing rates exhibit significant 10-Hz rhythmicity locked to song syllables, peaking prior to syllable onsets and suppressed prior to offsets-a pattern that predominates PN and interneuron activity in HVC during early stages of vocal learning.

Neural sequences are a fundamental feature of brain dynamics underlying diverse behaviours, but the mechanisms by which they develop during learning remain unknown. Songbirds learn vocalizations composed of syllables; in adult birds, each syllable is produced by a different sequence of action potential bursts in the premotor cortical area HVC. Here we carried out recordings of large populations of HVC neurons in singing juvenile birds throughout learning to examine the emergence of neural sequences. Early in vocal development, HVC neurons begin producing rhythmic bursts, temporally locked to a prototype syllable. Different neurons are active at different latencies relative to syllable onset to form a continuous sequence. Through development, as new syllables emerge from the prototype syllable, initially highly overlapping burst sequences become increasingly distinct. We propose a mechanistic model in which multiple neural sequences can emerge from the growth and splitting of a commo n precursor sequence., National Institutes of Health (U.S.) (Grant R01DC009183), National Science Foundation (U.S.) (Grant DGE-114747)

The songbird system has shed light on how the brain produces precisely timed behavioral sequences, and how the brain implements reinforcement learning (RL). RL is a powerful strategy for learning what action to produce in each state, but requires a unique representation of the states involved in the task. Songbird RL circuitry is thought to operate using a representation of each moment within song syllables, consistent with the sparse sequential bursting of neurons in premotor cortical nucleus HVC. However, such sparse sequences are not present in very young birds, which sing highly variable syllables of random lengths. Here, we review and expand upon a model for how the songbird brain could construct latent sequences to support RL, in light of new data elucidating connections between HVC and auditory cortical areas. We hypothesize that learning occurs via four distinct plasticity processes: 1) formation of ‘tutor memory’ sequences in auditory areas; 2) formation of appropriately-timed latent HVC sequences, seeded by inputs from auditory areas spontaneously replaying the tutor song; 3) strengthening, during spontaneous replay, of connections from HVC to auditory neurons of corresponding timing in the ‘tutor memory’ sequence, aligning auditory and motor representations for subsequent song evaluation; and 4) strengthening of connections from premotor neurons to motor output neurons that produce the desired sounds, via well-described song RL circuitry.

Reinforcement learning requires the convergence of signals representing context, action, and reward. While models of basal ganglia function have well-founded hypotheses about the neural origin of signals representing context and reward, the function and origin of signals representing action are less clear. Recent findings suggest that exploratory or variable behaviors are initiated by a wide array of ‘action-generating’ circuits in the midbrain, brainstem, and cortex. Thus, in order to learn, the striatum must incorporate an efference copy of action decisions made in these action-generating circuits. Here we review several recent neural models of reinforcement learning that emphasize the role of efference copy signals. Also described are ideas about how these signals might be integrated with inputs signaling context and reward., National Institutes of Health (U.S.) (Grant R01MH067105)

Most non-mammalian vertebrate species add new neurons to existing brain circuits throughout life, a process thought to be essential for tissue maintenance, repair, and learning. How these new neurons migrate through the mature brain and which cues trigger their integration within a functioning circuit is not known. To address these questions, we used two-photon microscopy to image the addition of genetically labeled newly generated neurons into the brain of juvenile zebra finches. Time-lapsein vivoimaging revealed that the majority of migratory new neurons exhibited a multipolar morphology and moved in a nonlinear manner for hundreds of micrometers. Young neurons did not use radial glia or blood vessels as a migratory scaffold; instead, cells extended several motile processes in different directions and moved by somal translocation along an existing process. Neurons were observed migrating for ∼2 weeks after labeling injection. New neurons were observed to integrate in close proximity to the soma of mature neurons, a behavior that may explain the emergence of clusters of neuronal cell bodies in the adult songbird brain. These results provide direct,in vivoevidence for a wandering form of neuronal migration involved in the addition of new neurons in the postnatal brain.

Accurate timing is a critical aspect of motor control, yet the temporal structure of many mature behaviors emerges during learning from highly variable exploratory actions. How does a developing brain acquire the precise control of timing in behavioral sequences? To investigate the development of timing, we analyzed the songs of young juvenile zebra finches. These highly variable vocalizations, akin to human babbling, gradually develop into temporally stereotyped adult songs. We find that the durations of syllables and silences in juvenile singing are formed by a mixture of two distinct modes of timing: a random mode producing broadly distributed durations early in development, and a stereotyped mode underlying the gradual emergence of stereotyped durations. Using lesions, inactivations, and localized brain cooling, we investigated the roles of neural dynamics within two premotor cortical areas in the production of these temporal modes. We find that LMAN (lateral magnocellular nucleus of the nidopallium) is required specifically for the generation of the random mode of timing and that mild cooling of LMAN causes an increase in the durations produced by this mode. On the contrary, HVC (used as a proper name) is required specifically for producing the stereotyped mode of timing, and its cooling causes a slowing of all stereotyped components. These results show that two neural pathways contribute to the timing of juvenile songs and suggest an interesting organization in the forebrain, whereby different brain areas are specialized for the production of distinct forms of neural dynamics.

In songbirds, the remarkable temporal precision of song is generated by a sparse sequence of bursts in the premotor nucleus HVC. To distinguish between two possible classes of models of neural sequence generation, we carried out intracellular recordings of HVC neurons in singing zebra finches (Taeniopygia guttata). We found that the subthreshold membrane potential is characterized by a large, rapid depolarization 5-10 ms before burst onset, consistent with a synaptically connected chain of neurons in HVC. We found no evidence for the slow membrane potential modulation predicted by models in which burst timing is controlled by subthreshold dynamics. Furthermore, bursts ride on an underlying depolarization of ∼10-ms duration, probably the result of a regenerative calcium spike within HVC neurons that could facilitate the propagation of activity through a chain network with high temporal precision. Our results provide insight into the fundamental mechanisms by which neural circuits can generate complex sequential behaviours.

The striatum—the primary input nucleus of the basal ganglia—plays a major role in motor control and learning. Four main classes of striatal neuron are thought to be essential for normal striatal function: medium spiny neurons, fast-spiking interneurons, cholinergic tonically active neurons, and low-threshold spiking interneurons. However, the nature of the interaction of these neurons during behavior is poorly understood. The songbird area X is a specialized striato-pallidal basal ganglia nucleus that contains two pallidal cell types as well as the same four cell types found in the mammalian striatum. We recorded 185 single units in Area X of singing juvenile birds and, based on singing-related firing patterns and spike waveforms, find six distinct cell classes—two classes of putative pallidal neuron that exhibited a high spontaneous firing rate (>60 Hz), and four cell classes that exhibited low spontaneous firing rates characteristic of striatal neurons. In this study, we examine in detail the four putative striatal cell classes. Type-1 neurons were the most frequently encountered and exhibited sparse temporally precise singing-related activity. Type-2 neurons were distinguished by their narrow spike waveforms and exhibited brief, high-frequency bursts during singing. Type-3 neurons were tonically active and did not burst, whereas type-4 neurons were inactive outside of singing and during singing generated long high-frequency bursts that could reach firing rates over 1 kHz. Based on comparison to the mammalian literature, we suggest that these four putative striatal cell classes correspond, respectively, to the medium spiny neurons, fast-spiking interneurons, tonically active neurons, and low-threshold spiking interneurons that are known to reside in area X.

In songbirds, as in mammals, basal ganglia-forebrain circuits are necessary for the learning and production of complex motor behaviors; however, the precise role of these circuits remains unknown. It has recently been shown that a basal ganglia-forebrain circuit in the songbird, which projects directly to vocal–motor circuitry, has a premotor function driving exploration necessary for vocal learning. It has also been hypothesized that this circuit, known as the anterior forebrain pathway (AFP), may generate an instructive signal that improves performance in the motor pathway. Here, we show that the output of the AFP directly implements a motor correction that reduces vocal errors. We use disruptive auditory feedback, contingent on song pitch, to induce learned changes in song structure over the course of hours and find that reversible inactivation of the output of the AFP produces an immediate regression of these learned changes. Thus, the AFP is involved in generating an error-reducing bias, which could increase the efficiency of vocal exploration and instruct synaptic changes in the motor pathway. We also find that learned changes in the song generated by the AFP are incorporated into the motor pathway within 1 day. Our observations support a view that basal ganglia-related circuits directly implement behavioral adaptations that minimize errors and subsequently stabilize these adaptations by training premotor cortical areas.

We introduce a novel wireless, low-power neural stimulation system for use in freely behaving animals. The system consists of an external transmitter and a miniature, implantable wireless receiver–stimulator. The implant uses a custom integrated chip to deliver biphasic current pulses to four addressable bipolar electrodes at 32 selectable current levels (10 μA to 1 mA). To achieve maximal battery life, the chip enters a sleep mode when not needed and can be awakened remotely when required. To test our device, we implanted bipolar stimulating electrodes into the songbird motor nucleus HVC (formerly called the high vocal center) of zebra finches. Single-neuron recordings revealed that wireless stimulation of HVC led to a strong increase of spiking activity in its downstream target, the robust nucleus of the arcopallium. When we used this device to deliver biphasic pulses of current randomly during singing, singing activity was prematurely terminated in all birds tested. Thus our device is highly effective for remotely modulating a neural circuit and its corresponding behavior in an untethered, freely behaving animal.

Many complex behaviours, like speech or music, have a hierarchical organization with structure on many timescales, but it is not known how the brain controls the timing of behavioural sequences, or whether different circuits control different timescales of the behaviour. Here we address these issues by using temperature to manipulate the biophysical dynamics in different regions of the songbird forebrain involved in song production. We find that cooling the premotor nucleus HVC (formerly known as the high vocal centre) slows song speed across all timescales by up to 45 per cent but only slightly alters the acoustic structure, whereas cooling the downstream motor nucleus RA (robust nucleus of the arcopallium) has no observable effect on song timing. Our observations suggest that dynamics within HVC are involved in the control of song timing, perhaps through a chain-like organization. Local manipulation of brain temperature should be broadly applicable to the identification of neural circuitry that controls the timing of behavioural sequences and, more generally, to the study of the origin and role of oscillatory and other forms of brain dynamics in neural systems.

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode- recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented.

Young animals engage in variable exploratory behaviors essential for the development of neural circuitry and adult motor control, yet the neural basis of these behaviors is largely unknown. Juvenile songbirds produce subsong—a succession of primitive vocalizations akin to human babbling. We found that subsong production in zebra finches does not require HVC (high vocal center), a key premotor area for singing in adult birds, but does require LMAN (lateral magnocellular nucleus of the nidopallium), a forebrain nucleus involved in learning but not in adult singing. During babbling, neurons in LMAN exhibited premotor correlations to vocal output on a fast time scale. Thus, juvenile singing is driven by a circuit distinct from that which produces the adult behavior—a separation possibly general to other developing motor systems.

We propose a model of songbird learning that focuses on avian brain areas HVC and RA, involved in song production, and area LMAN, important for generating song variability. Plasticity at HVC → RA synapses is driven by hypothetical “rules” depending on three signals: activation of HVC → RA synapses, activation of LMAN → RA synapses, and reinforcement from an internal critic that compares the bird's own song with a memorized template of an adult tutor's song. Fluctuating glutamatergic input to RA from LMAN generates behavioral variability for trial-and-error learning. The plasticity rules perform gradient-based reinforcement learning in a spiking neural network model of song production. Although the reinforcement signal is delayed, temporally imprecise, and binarized, the model learns in a reasonable amount of time in numerical simulations. Varying the number of neurons in HVC and RA has little effect on learning time. The model makes specific predictions for the induction of bidirectional long-term plasticity at HVC → RA synapses.

The function and the origin of replay of motor activity during sleep are currently unknown. Spontaneous activity patterns in the nucleus robustus of the arcopallium (RA) and in HVC (high vocal center) of the sleeping songbird resemble premotor patterns in these areas observed during singing. We test the hypothesis that the nucleus interface of the nidopallium (NIf) has an important role for initiating and shaping these sleep-related activity patterns. In head-fixed, sleeping zebra finches we find that injections of the GABAA-agonist muscimol into NIf lead to transient abolishment of premotor-like bursting activity in HVC neurons. Using antidromic activation of NIf neurons by electrical stimulation in HVC, we are able to distinguish a class of HVC-projecting NIf neurons from a second class of NIf neurons. Paired extracellular recordings in NIf and HVC show that NIf neurons provide a strong bursting drive to HVC. In contrast to HVC neurons, whose bursting activity waxes and wanes in burst epochs, individual NIf projection neurons are nearly continuously bursting and tend to burst only once on the timescale of song syllables. Two types of HVC projection neurons—premotor and striatal projecting—respond differently to the NIf drive, in agreement with notions of HVC relaying premotor signals to RA and an anticipatory copy thereof to areas of a basal ganglia pathway.

Zebra finches (Taenopygia guttata, formerly Poephila guttata) are small, colorful songbirds that have been favored by bird fanciers since the nineteenth century. In captivity, zebra finches are prolific breeders and robust ‘easy keepers’; these characteristics, along with a diurnal activity pattern and the singing prowess of males, makes them an attractive model for biomedical researchers. Their increasing popularity resides especially in the fields of neurobiology, with a majority of investigations in the United States focusing on male vocal development, and behavior, such as the basis for mate preference and aggression. However, many other research applications, as well as the production of transgenic birds, have also been pursued. The zebra finch genome is the second avian species to be sequenced (Warren et al., 2010), after that of the chicken (Gallus gallus domesticus). Importantly, a high-resolution digital atlas of the zebra finch brain was recently published (Karten et al., 2013) and detailed; current protocols for using zebra finches in research can be accessed online (Cold Spring Harbor Press, 2014). One review article determined there were considerably more articles about zebra finches in 2008 relative to other passerine species (Bateson and Feenders, 2010), while a tally of PubMed entries for the subject ‘zebra finch’ reveals a steady annual increase in publications that started to escalate during the 1980s.

Zebra finch song is represented in the high-level motor control nucleus high vocal center (HVC) (Reiner et al., 2004) as a sparse sequence of spike bursts. In contrast, the vocal organ is driven continuously by smoothly varying muscle control signals. To investigate how the sparse HVC code is transformed into continuous vocal patterns, we recorded in the singing zebra finch from populations of neurons in the robust nucleus of arcopallium (RA), a premotor area intermediate between HVC and the motor neurons. We found that highly similar song elements are typically produced by different RA ensembles. Furthermore, although the song is modulated on a wide range of time scales (10-100 ms), patterns of neural activity in RA change only on a short time scale (5-10 ms). We suggest that song is driven by a dynamic circuit that operates on a single underlying clock, and that the large convergence of RA neurons to vocal control muscles results in a many-to-one mapping of RA activity to song structure. This permits rapidly changing RA ensembles to drive both fast and slow acoustic modulations, thereby transforming the sparse HVC code into a continuous vocal pattern.

Sparse neural codes have been widely observed in cortical sensory and motor areas. A striking example of sparse temporal coding is in the song-related premotor area high vocal center (HVC) of songbirds: The motor neurons innervating avian vocal muscles are driven by premotor nucleus robustus archistriatalis (RA), which is in turn driven by nucleus HVC. Recent experiments reveal that RA-projecting HVC neurons fire just one burst per song motif. However, the function of this remarkable temporal sparseness has remained unclear. Because birdsong is a clear example of a learned complex motor behavior, we explore in a neural network model with the help of numerical and analytical techniques the possible role of sparse premotor neural codes in song-related motor learning. In numerical simulations with nonlinear neurons, as HVC activity is made progressively less sparse, the minimum learning time increases significantly. Heuristically, this slowdown arises from increasing interference in the weight updates for different synapses. If activity in HVC is sparse, synaptic interference is reduced, and is minimized if each synapse from HVC to RA is used only once in the motif, which is the situation observed experimentally. Our numerical results are corroborated by a theoretical analysis of learning in linear networks, for which we derive a relationship between sparse activity, synaptic interference, and learning time. If songbirds acquire their songs under significant pressure to learn quickly, this study predicts that HVC activity, currently measured only in adults, should also be sparse during the sensorimotor phase in the juvenile bird. We discuss the relevance of these results, linking sparse codes and learning speed, to other multilayered sensory and motor systems.

Many mammalian species rely on pheromones—semiochemicals produced by other members of the same species—to communicate social status and reproductive readiness. To assess how the central nervous system integrates the complex repertoire of pheromones, we recorded from single neurons in the accessory olfactory bulb, a nucleus that processes pheromonal signals, of male mice engaged in natural behaviors. Neuronal firing was robustly modulated by physical contact with male and female conspecifics, with individual neurons activated selectively by specific combinations of the sex and strain of conspecifics. We infer that mammals encode social and reproductive information by integrating vomeronasal sensory activity specific to sex and genetic makeup.

Birdsong is a complex behavior that exhibits hierarchical organization. While the representation of singing behavior and its hierarchical organization has been studied in some detail in avian cortical premotor circuits, our understanding of the role of the thalamus in adult birdsong is incomplete. Using a combination of behavioral and electrophysiological studies, we seek to expand on earlier work showing that the thalamic nucleus Uvaeformis (Uva) is necessary for the production of stereotyped, adult song in zebra finch (Taeniopygia guttata). We confirm that complete bilateral lesions of Uva abolish singing in the 'directed' social context, but find that in the 'undirected' social context, such lesions result in highly variable vocalizations similar to early babbling song in juvenile birds. Recordings of neural activity in Uva reveal strong syllable-related modulation, maximally active prior to syllable onsets and minimally active prior to syllable offsets. Furthermore, both song and Uva activity exhibit a pronounced coherent modulation at 10Hz-a pattern observed in downstream premotor areas in adult and, even more prominently, in juvenile birds. These findings are broadly consistent with the idea that Uva is critical in the sequential activation of behavioral modules in HVC., National Institutes of Health (U.S.) (Grant R01DC009183)

The use of chronically implanted electrodes for neural recordings in small, freely behaving animals poses several unique technical challenges. Because of the need for an extremely lightweight apparatus, chronic recording technology has been limited to manually operated microdrives, despite the advantage of motorized manipulators for positioning electrodes. Here we describe a motorized, miniature chronically implantable microdrive for independently positioning three electrodes in the brain. The electrodes are controlled remotely, avoiding the need to disturb the animal during electrode positioning. The microdrive is approximately 6 mm in diameter, 17 mm high and weighs only 1.5 g, including the headstage preamplifier. Use of the motorized microdrive has produced a ten-fold increase in our data yield compared to those experiments done using a manually operated drive. In addition, we are able to record from multiple single neurons in the behaving animal with signal quality comparable to that seen in a head-fixed anesthetized animal. We also describe a motorized commutator that actively tracks animal rotation based on a measurement of torque in the tether.

Two-photon microscopy has enabled anatomical and functional fluorescence imaging in the intact brain of rats. Here, we extend two-photon imaging from anesthetized, head-stabilized to awake, freely moving animals by using a miniaturized head-mounted microscope. Excitation light is conducted to the microscope in a single-mode optical fiber, and images are scanned using vibrations of the fiber tip. Microscope performance was first characterized in the neocortex of anesthetized rats. We readily obtained images of vasculature filled with fluorescently labeled blood and of layer 2/3 pyramidal neurons filled with a calcium indicator. Capillary blood flow and dendritic calcium transients were measured with high time resolution using line scans. In awake, freely moving rats, stable imaging was possible except during sudden head movements.

Birdsong is characterized by the modulation of sound properties over a wide image of timescales. Understanding the mechanisms by which the brain organizes this complex temporal behaviour is a central motivation in the study of the song control and learning system. Here we present evidence that, in addition to central neural control, a further level of temporal organization is provided by nonlinear oscillatory dynamics that are intrinsic to the avian vocal organ. A detailed temporal and spectral examination of song of the zebra finch (Taeniopygia guttata) reveals a class of rapid song modulations that are consistent with transitions in the dynamical state of the syrinx. Furthermore, in vitro experiments show that the syrinx can produce a sequence of oscillatory states that are both spectrally and temporally complex in response to the slow variation of respiratory or syringeal parameters. As a consequence, simple variations in a small number of neural signals can result in a complex acoustic sequence.

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)