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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.

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.

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.

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.

1. The procerebral (PC) lobe of the terrestrial mollusk Limax maximus contains a highly interconnected network of local olfactory interneurons that receives ipsilateral axonal projections from superior and inferior noses. This network exhibits an approximately 0.7-Hz intrinsic oscillation in its local field potential (LFP). 2. Intracellular recordings show that the lobe contains at least two classes of neurons with activity phase locked to the oscillation. Neurons in one class produce periodic bursts of spikes, followed by a period of hyperpolarization and subsequently a depolarizing afterpotential. There is a small but significant chance for a second burst to occur during the depolarizing afterpotential; this leads to a double event in the LFP. Bursting neurons constitute approximately 10% of the neurons in the lobe. 3. Neurons in the other class fire infrequently and do not produce periodic bursts of action potentials. However, they receive strong, periodic inhibitory input during every event in the LFP. These nonbursting cells constitute the major fraction of neurons in the lobe. There is a clear correlation between the periodic burst of action potentials in the bursting neurons and the hyperpolarization seen in nonbursting neurons. 4. Optical techniques are used to image the spatially averaged transmembrane potentials in preparations stained with voltage-sensitive dyes. The results of simultaneous optical and electrical measurements show that the major part of the optical signal can be interpreted as a superposition of the intracellular signals arising from the bursting and nonbursting neurons. 5. Successive images of the entire PC lobe show waves of electrical activity that span the width of the lobe and travel its full length along a longitudinal axis. The direction of propagation in the unperturbed lobe is always from the distal to the proximal end. The wavelength varies between preparations but is on the order of the length of the preparation. 6. One-dimensional images along the longitudinal axis of the lobe are used to construct a space-time map of the optical activity, from which we calculate the absolute contribution of bursting and nonbursting neurons to the optical signal. The contribution of the intracellular signals from the two cell types appears to vary systematically across the lobe; bursting cells dominate at middle and proximal locations, and nonbursting cells dominate at distal locations. 7. The direction and form of the waves can be perturbed either by microsurgical manipulation of the preparation or by chemical modulation of its synaptic and neuronal properties.(ABSTRACT TRUNCATED AT 400 WORDS)

High vocal center (HVC) is part of the premotor pathway necessary for song production and is also a primary source of input to the anterior forebrain pathway (AFP), a basal ganglia-related circuit essential for vocal learning. We have examined the activity of identified HVC neurons of zebra finches during singing. Antidromic activation was used to identify three classes of HVC cells: neurons projecting to the premotor nucleus RA, neurons projecting to area X in the AFP, and putative HVC interneurons. HVC interneurons are active throughout the song and display tonic patterns of activity. Projection neurons exhibit highly phasic stereotyped firing patterns. X-projecting (HVC(X)) neurons burst zero to four times per motif, whereas RA-projecting neurons burst extremely sparsely—at most once per motif. The bursts of HVC projection neurons are tightly locked to the song and typically have a jitter of (X)neurons are auditory sensitive during singing. We recorded the activity of these neurons in juvenile birds during singing and found that firing patterns of these neurons are not altered by distorted auditory feedback, which is known to disrupt learning or to cause degradation of song already learned.

During singing, neurons in premotor nucleus RA (robust nucleus of the arcopallium) of the zebra finch produce complex temporal sequences of bursts that are recapitulated during sleep. RA receives input from nucleus HVC via the premotor pathway, and also from the lateral magnocellular nucleus of the anterior nidopallium (LMAN), part of a basal ganglia-related circuit essential for vocal learning. We explore the propagation of sleep-related spike patterns in these two pathways and their influences on RA activity. We promote sleep in head-fixed birds by injections of melatonin and make single-neuron recordings from the three major classes of neurons in HVC: RA-projecting neurons, Area X-projecting neurons, and interneurons. We also record LMAN neurons that project to RA. In paired recordings, spike trains from identified HVC neuron types are strongly coherent with spike trains in RA neurons, whereas LMAN projection neurons on average exhibit only a weak coherency with neurons in HVC and RA. We further examine the relative roles of HVC and LMAN in generating RA burst sequences with reversible inactivation. Lidocaine inactivation of HVC completely abolishes bursting in RA, whereas inactivation of LMAN has no effect on burst rates in RA. In combination, our data suggest that in adult birds, RA burst sequences in sleep are driven via the premotor pathway from HVC. We present a simple generative model of spike trains in HVC, RA, and LMAN neurons that is able to qualitatively reproduce observed coherency functions. We propose that commonly observed coherency peaks at positive and negative time lags are caused by sequentially correlated HVC activity.

Fee, Michale S., Partha P. Mitra, and David Kleinfeld. Central versus peripheral determinants of patterned spike activity in rat vibrissa cortex during whisking. J. Neurophysiol. 78: 1144–1149, 1997. We report on the relationship between single-unit activity in primary somatosensory vibrissa cortex of rat and the rhythmic movement of vibrissae. Animals were trained to whisk freely in air in search of food. Electromyographic (EMG) recordings from the mystatial pads served as a reference for the position of the vibrissae. A fast, oscillatory component in single-unit spike trains is correlated with vibrissa position within the whisk cycle. The phase of the correlation for different units is broadly distributed. A second, slowly varying component of spike activity correlates with the amplitude of the whisk cycle. For some units, the phase and amplitude correlations were of sufficient strength to allow the position of the whiskers to be accurately predicted from a single spike train. To determine whether the observed patterned spike activity was driven by motion of the vibrissae, as opposed to central pathways, we reversibly blocked the contralateral facial motor nerve during the behavioral task so that the rat whisked only on the ipsilateral side. The ipsilateral EMG served as a reliable reference signal. The fast, oscillatory component of the spike-EMG correlation disappears when the facial motor nerve is blocked. This implies that the position of vibrissae within a cycle is encoded through direct sensory activation. The slowly varying component of the spike-EMG correlation is unaffected by the block. This implies that the amplitude of whisking is likely to be mediated by corollary discharge. Our results suggest that motor cortex does not relay a reference signal to sensory cortex for positional information of the vibrissae during whisking.