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.

Intrinsic neuronal properties represent song and error in zebra finch vocal learning.

Neurons regulate their intrinsic physiological properties, which could influence network properties and contribute to behavioral plasticity. Recording from adult zebra finch brain slices we show that within each bird basal ganglia Area X-projecting (HVCX) neurons share similar spike waveform morphology and timing of spike trains, with modeling indicating similar magnitudes of five principal ion currents. These properties vary among birds in lawful relation to acoustic similarity of the birds' songs, with adult sibling pairs (same songs) sharing similar waveforms and spiking characteristics. The properties are maintained dynamically: HVCX within juveniles learning to sing show variable properties, whereas the uniformity rapidly degrades within hours in adults singing while exposed to abnormal (delayed) auditory feedback. Thus, within individual birds the population of current magnitudes covary over the arc of development, while rapidly responding to changes in feedback (in adults). This identifies network interactions with intrinsic properties that affect information storage and processing of learned vocalizations.

Development of Microplatforms to Mimic the In Vivo Architecture of CNS and PNS Physiology and Their Diseases.

Understanding the mechanisms that govern nervous tissues function remains a challenge. In vitro two-dimensional (2D) cell culture systems provide a simplistic platform to evaluate systematic investigations but often result in unreliable responses that cannot be translated to pathophysiological settings. Recently, microplatforms have emerged to provide a better approximation of the in vivo scenario with better control over the microenvironment, stimuli and structure. Advances in biomaterials enable the construction of three-dimensional (3D) scaffolds, which combined with microfabrication, allow enhanced biomimicry through precise control of the architecture, cell positioning, fluid flows and electrochemical stimuli. This manuscript reviews, compares and contrasts advances in nervous tissues-on-a-chip models and their applications in neural physiology and disease. Microplatforms used for neuro-glia interactions, neuromuscular junctions (NMJs), blood-brain barrier (BBB) and studies on brain cancer, metastasis and neurodegenerative diseases are addressed. Finally, we highlight challenges that can be addressed with interdisciplinary efforts to achieve a higher degree of biomimicry. Nervous tissue microplatforms provide a powerful tool that is destined to provide a better understanding of neural health and disease.

Nonlinear statistical data assimilation for HVC[Formula: see text] neurons in the avian song system.

With the goal of building a model of the HVC nucleus in the avian song system, we discuss in detail a model of HVC[Formula: see text] projection neurons comprised of a somatic compartment with fast Na[Formula: see text] and K[Formula: see text] currents and a dendritic compartment with slower Ca[Formula: see text] dynamics. We show this model qualitatively exhibits many observed electrophysiological behaviors. We then show in numerical procedures how one can design and analyze feasible laboratory experiments that allow the estimation of all of the many parameters and unmeasured dynamical variables, given observations of the somatic voltage [Formula: see text] alone. A key to this procedure is to initially estimate the slow dynamics associated with Ca, blocking the fast Na and K variations, and then with the Ca parameters fixed estimate the fast Na and K dynamics. This separation of time scales provides a numerically robust method for completing the full neuron model, and the efficacy of the method is tested by prediction when observations are complete. The simulation provides a framework for the slice preparation experiments and illustrates the use of data assimilation methods for the design of those experiments.

Estimating the biophysical properties of neurons with intracellular calcium dynamics.

We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V(t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

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.

A computational tool for automated large-scale analysis and measurement of bird-song syntax.

We present computer software for automated, high throughput, quantitative syllable-level analysis of bird song syntax. The primary advantage of our tool is the ease and effectiveness it provides in quantifying syllable sequence and performing a comparison of syllable sequence from one day of singing with one or more other days of singing. The software utilizes the output of the Feature Batch module in Sound Analysis Pro (Tchernichovski et al., 2000) that can be used to measure the temporal and spectral features of each syllable produced during a day of singing. We use these measurements to identify individual syllables based on their temporal and spectral properties and then identify transition probabilities among syllables to determine changes in syntax. This quantifies the ordering of syllables in songs and the frequency with which subsequences appear. Moreover, the software computes the linearity, consistency, and stereotypy scores for every bout presented as well as descriptive statistics for each of these measures for each day of singing. We also report statistical measures that the software utilizes (the Kullback-Leibler distance and the sequence entropy) to quantify the degree of dissimilarity between sequences of syllable transitions. Our tool is useful for comparing the syntactic structure of songs produced by a bird prior to and after a manipulation such as ablation of part of the vocal motor pathway or infusion of pharmacological agents, or for assessing the degree of individual variation in syntactic structure across populations of birds.

Synchronization of mouse islets of Langerhans by glucose waveforms.

Pancreatic islets secrete insulin in a pulsatile manner, and the individual islets are synchronized, producing in vivo oscillations. In this report, the ability of imposed glucose waveforms to synchronize a population of islets was investigated. A microfluidic system was used to deliver glucose waveforms to ∼20 islets while fura 2 fluorescence was imaged. All islets were entrained to a sinusoidal waveform of glucose (11 mM median, 1 mM amplitude, and a 5-min period), producing synchronized oscillations of fura 2 fluorescence. During perfusion with constant 11 mM glucose, oscillations of fura 2 fluorescence were observed in individual islets, but the average signal was nonoscillatory. Spectral analysis and a synchronization index (λ) were used to measure the period of fura 2 fluorescence oscillations and evaluate synchronization of islets, respectively. During perfusion with glucose waveforms, spectral analysis revealed a dominant frequency at 5 min, and λ, which can range from 0 (unsynchronized) to 1 (perfect synchronization), was 0.78 ± 0.15. In contrast, during perfusion with constant 11 mM glucose, the main peak in the spectral analysis corresponded to a period of 5 min but was substantially smaller than during perfusion with oscillatory glucose, and the average λ was 0.52 ± 0.09, significantly lower than during perfusion with sinusoidal glucose. These results indicated that an oscillatory glucose level synchronized the activity of a heterogeneous islet population, serving as preliminary evidence that islets could be synchronized in vivo through oscillatory glucose levels produced by a liver-pancreas feedback loop.