Dynamical model of birdsong maintenance and control

Intrinsic plasticity and birdsong learning

Although information processing and storage in the brain is thought to be primarily orchestrated by synaptic plasticity, other neural mechanisms such as intrinsic plasticity are available. While a number of recent studies have described the plasticity of intrinsic excitability in several types of neurons, the significance of non-synaptic mechanisms in memory and learning remains elusive. After reviewing plasticity of intrinsic excitation in relation to learning and homeostatic mechanisms, we focus on the intrinsic properties of a class of basal-ganglia projecting song system neurons in zebra finch, how these related to each bird's unique learned song, how these properties change over development, and how they are maintained dynamically to rapidly change in response to auditory feedback perturbations. We place these results in the broader theme of learning and changes in intrinsic properties, emphasizing the computational implications of this form of plasticity, which are distinct from synaptic plasticity. The results suggest that exploring reciprocal interactions between intrinsic and network properties will be a fruitful avenue for understanding mechanisms of birdsong learning.

The Adjustment of Anterior Forebrain Pathway (AFP) to Birdsong Is Phased during Song Learning and Maintenance

Anterior forebrain pathway (AFP), a basal ganglia-dorsal forebrain circuit, significantly impacts birdsong, specifically in juvenile or deaf birds. Despite many physiological experiments supporting AFP's role in song production, the mechanism underlying it remains poorly understood. Using a computational model of the anterior forebrain pathway and song premotor pathway, we examined the dynamic process and exact role of AFP during song learning and distorted auditory feedback (DAF). Our simulation suggests that AFP can adjust the premotor pathway structure and syllables based on its delayed input to the robust nucleus of the archistriatum (RA). It is also indicated that the adjustment to the synaptic conductance in the song premotor pathway has two phases: normal phases where the adjustment decreases with an increasing number of trials and abnormal phases where the adjustment remains stable or even increases. These two phases alternate and impel a specific effect on birdsong based on AFP's specific structures, which may be associated with auditory feedback. Furthermore, our model captured some characteristics shown in birdsong experiments, such as similarities in pitch, intensity, and duration to real birds and the highly abnormal features of syllables during DAF.

Electrophysiological characterization and computational models of HVC neurons in the zebra finch

The nucleus HVC (proper name) within the avian analog of mammal premotor cortex produces stereotyped instructions through the motor pathway leading to precise, learned vocalization by songbirds. Electrophysiological characterization of component HVC neurons is an important requirement in building a model to understand HVC function. The HVC contains three neural populations: neurons that project to the RA (robust nucleus of arcopallium), neurons that project to Area X (of the avian basal ganglia), and interneurons. These three populations are interconnected with specific patterns of excitatory and inhibitory connectivity, and they fire with characteristic patterns both in vivo and in vitro. We performed whole cell current-clamp recordings on HVC neurons within brain slices to examine their intrinsic firing properties and determine which ionic currents are responsible for their characteristic firing patterns. We also developed conductance-based models for the different neurons and calibrated the models using data from our brain slice work. These models were then used to generate predictions about the makeup of the ionic currents that are responsible for the different responses to stimuli. These predictions were then tested and verified in the slice using pharmacological manipulations. The model and the slice work highlight roles of a hyperpolarization-activated inward current (Ih), a low-threshold T-type Ca(2+) current (ICa-T), an A-type K(+) current (IA), a Ca(2+)-activated K(+) current (ISK), and a Na(+)-dependent K(+) current (IKNa) in driving the characteristic neural patterns observed in the three HVC neuronal populations. The result is an improved characterization of the HVC neurons responsible for song production in the songbird.

New perspectives on the physics of birdsong

In this work, we revisit the path that has been travelled during the last few years towards the modelling of the avian vocal organ, the syrinx, using numerical and theoretical techniques from bifurcation theory as analysing tools and present experimental support for the models. This fruitful perspective allowed the retracing of many acoustic features of syllables to intrinsic properties of the syrinx, thereby relocating the bird phonatory organ from the role of a mere vocal instrument of the nervous system to a central source of complex acoustical behaviour.

Inhibition and recurrent excitation in a computational model of sparse bursting in song nucleus HVC

The telencephalic premotor nucleus HVC is situated at a critical point in the pattern-generating premotor circuitry of oscine songbirds. A striking feature of HVC's premotor activity is that its projection neurons burst extremely sparsely. Here we present a computational model of HVC embodying several central hypotheses: 1) sparse bursting is generated in bistable groups of recurrently connected robust nucleus of the arcopallium (RA)-projecting (HVCRA) neurons; 2) inhibitory interneurons terminate bursts in the HVCRA groups; and 3) sparse sequences of bursts are generated by the propagation of waves of bursting activity along networks of HVCRA neurons. Our model of sparse bursting places HVC in the context of central pattern generators and cortical networks using inhibition, recurrent excitation, and bistability. Importantly, the unintuitive result that inhibitory interneurons can precisely terminate the bursts of HVCRA groups while showing relatively sustained activity throughout the song is made possible by a specific constraint on their connectivity. We use the model to make novel predictions that can be tested experimentally.

Temporal spike pattern learning

Sensory systems pass information about an animal's environment to higher nervous system units through sequences of action potentials. When these action potentials have essentially equivalent wave forms, all information is contained in the interspike intervals (ISIs) of the spike sequence. How do neural circuits recognize and read these ISI sequences? We address this issue of temporal sequence learning by a neuronal system utilizing spike timing dependent plasticity (STDP). We present a general architecture of neural circuitry that can perform the task of ISI recognition. The essential ingredients of this neural circuit, which we refer to as "interspike interval recognition unit" (IRU) are (i) a spike selection unit, the function of which is to selectively distribute input spikes to downstream IRU circuitry; (ii) a time-delay unit that can be tuned by STDP; and (iii) a detection unit, which is the output of the IRU and a spike from which indicates successful ISI recognition by the IRU. We present two distinct configurations for the time-delay circuit within the IRU using excitatory and inhibitory synapses, respectively, to produce a delayed output spike at time t_{0}+tau(R) in response to the input spike received at time t_{0} . R is the tunable parameter of the time-delay circuit that controls the timing of the delayed output spike. We discuss the forms of STDP rules for excitatory and inhibitory synapses, respectively, that allow for modulation of R for the IRU to perform its task of ISI recognition. We then present two specific implementations for the IRU circuitry, derived from the general architecture that can both learn the ISIs of a training sequence and then recognize the same ISI sequence when it is presented on subsequent occasions.

Neural circuitry for recognizing interspike interval sequences

Sensory systems present environmental information to central nervous system as sequences of action potentials or spikes. How do animals recognize these sequences carrying information about their world? We present a biologically inspired neural circuit designed to enable spike pattern recognition. This circuit is capable of training itself on a given interspike interval (ISI) sequence and is then able to respond to presentations of the same sequence. The essential ingredients of the recognition circuit are (a) a tunable time delay circuit, (b) a spike selection unit, and (c) a tuning mechanism using spike timing dependent plasticity of inhibitory synapses. We have investigated this circuit using Hodgkin-Huxley neuron models connected by realistic excitatory and inhibitory synapses. It is robust in the presence of noise represented as jitter in the spike times of the ISI sequence.

Physiological insights into the social-context-dependent changes in the rhythm of the song motor program

Precisely timed behaviors are central to the survival of almost all organisms. Song is an example of a learned behavior under exquisite temporal control. Song tempo in zebra finches (Taeniopygia guttata) is systematically modified depending on social context. When male zebra finches sing to females (directed), it is produced with a faster motor pattern compared with when they sing in isolation (undirected). We measured heart rate and air sac pressure during directed and undirected singing to quantify motivation levels and respiratory timing. Heart rate was significantly higher when male birds sang to females and was negatively correlated with song duration. The change in song tempo between directed and undirected song was accounted for by varying the duration of vocal expiratory events, whereas the duration of silent inspirations was unchanged. Song duration increased with repeated singing during directed song bouts, which was caused by a uniform increase in the duration of both expirations and inspirations. These results illustrate the importance of motivational state in regulating song tempo and demonstrate that multiple timing oscillators are necessary to control the rhythm of song. At least two different neural oscillators are required to control context-dependent changes in song tempo. One oscillator controlling expiratory duration varies as function of social context and another controlling inspiratory duration is fixed. In contrast, the song tempo change affecting expiratory and inspiratory duration within a directed bout of song could be achieved by slowing the output of a single oscillator.

Synaptic interactions underlying song-selectivity in the avian nucleus HVC revealed by dual intracellular recordings

Stimulus-dependent synaptic interactions underlying selective sensory representations in neural circuits specialized for sensory processing and sensorimotor integration remain poorly understood. The songbird telencephalic nucleus HVC is a sensorimotor area essential to learned vocal control with one projection neuron (PN) type (HVC(RA)) innervating a song premotor pathway, another PN (HVC(X)) innervating a basal ganglia pathway essential to vocal plasticity, and interneurons (HVC(Int)). Playback of the bird's own song (BOS), but not other songs, evokes action potential bursts from both PNs, but HVC(RA) and HVC(X) display distinct BOS-evoked subthreshold responses. To characterize synaptic interactions underlying HVC's BOS-selective responses and assess stimulus-evoked changes in functional interactions between HVC neurons, we made simultaneous in vivo intracellular recordings from various HVC neuron pairs in urethan-anesthetized zebra finches. Spike-triggered averaging revealed that all HVC neuron types receive common excitation and that the onset of this excitation occurs during a narrower time window in projection neurons during BOS playback. To distinguish local from extrinsic contributions to HVC subthreshold response patterns, we inactivated the HVC local circuit with GABA or occluded inhibition in single HVC(X) cells. After either treatment, BOS-evoked responses in HVC(X) neurons became purely depolarizing and subthreshold responses of HVC(X) and HVC(RA) cells became remarkably similar to one another while retaining BOS selectivity. Therefore both PN types receive a common extrinsic source of BOS-selective excitation, and local inhibition specifically alters processing of auditory information in HVC(X) cells. In HVC, excitatory and inhibitory synaptic interactions are recruited in a stimulus-dependent fashion, affecting auditory representations of the BOS locally and in other song nuclei important to song learning and production.

Synaptic plasticity with discrete state synapses

Experimental observations on synaptic plasticity at individual glutamatergic synapses from the CA3 Shaffer collateral pathway onto CA1 pyramidal cells in the hippocampus suggest that the transitions in synaptic strength occur among discrete levels at individual synapses [C. C. H. Petersen, Proc. Natl. Acad. Sci. USA 85, 4732 (1998); O'Connor, Wittenberg, and Wang, D. H. O'Connor, Proc. Natl. Acad. Sci. USA (to be published); J. M. Montgomery and D. V. Madison, Trends Neurosci. 27, 744 (2004)]. This happens for both long term potentiation (LTP) and long term depression (LTD) induction protocols. O'Connor, Wittenberg, and Wang have argued that three states would account for their observations on individual synapses in the CA3-CA1 pathway. We develop a quantitative model of this three-state system with transitions among the states determined by a competition between kinases and phosphatases shown by D. H. O'Connor, to be determinant of LTP and LTD, respectively. Specific predictions for various plasticity protocols are given by coupling this description of discrete synaptic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor ligand gated ion channel conductance changes to a model of postsynaptic membrane potential and associated intracellular calcium fluxes to yield the transition rates among the states. We then present various LTP and LTD induction protocols to the model system and report the resulting whole cell changes in AMPA conductance. We also examine the effect of our discrete state synaptic plasticity model on the synchronization of realistic oscillating neurons. We show that one-to-one synchronization is enhanced by the plasticity we discuss here and the presynaptic and postsynaptic oscillations are in phase. Synaptic strength saturates naturally in this model and does not require artificial upper or lower cutoffs, in contrast to earlier models of plasticity.

Dynamics of learning in coupled oscillators tutored with delayed reinforcements

In this work we analyze the solutions of a simple system of coupled phase oscillators in which the connectivity is learned dynamically. The model is inspired by the process of learning of birdsongs by oscine birds. An oscillator acts as the generator of a basic rhythm and drives slave oscillators which are responsible for different motor actions. The driving signal arrives at each driven oscillator through two different pathways. One of them is a direct pathway. The other one is a reinforcement pathway, through which the signal arrives delayed. The coupling coefficients between the driving oscillator and the slave ones evolve in time following a Hebbian-like rule. We discuss the conditions under which a driven oscillator is capable of learning to lock to the driver. The resulting phase difference and connectivity are a function of the delay of the reinforcement. Around some specific delays, the system is capable of generating dramatic changes in the phase difference between the driver and the driven systems. We discuss the dynamical mechanism responsible for this effect and possible applications of this learning scheme.