Heterogeneity of intrinsic biophysical properties among cochlear nucleus neurons improves the population coding of temporal information

Efficient information coding and degeneracy in the nervous system

Efficient information coding (EIC) is a universal biological framework rooted in the fundamental principle that system responses should match their natural stimulus statistics for maximizing environmental information. Quantitatively assessed through information theory, such adaptation to the environment occurs at all biological levels and timescales. The context dependence of environmental stimuli and the need for stable adaptations make EIC a daunting task. We argue that biological complexity is the principal architect that subserves deft execution of stable EIC. Complexity in a system is characterized by several functionally segregated subsystems that show a high degree of functional integration when they interact with each other. Complex biological systems manifest heterogeneities and degeneracy, wherein structurally different subsystems could interact to yield the same functional outcome. We argue that complex systems offer several choices that effectively implement EIC and homeostasis for each of the different contexts encountered by the system.

Kv1 channels regulate variations in spike patterning and temporal reliability in the avian cochlear nucleus angularis

Diverse physiological phenotypes in a neuronal population can broaden the range of computational capabilities within a brain region. The avian cochlear nucleus angularis (NA) contains a heterogeneous population of neurons whose variation in intrinsic properties results in electrophysiological phenotypes with a range of sensitivities to temporally modulated input. The low-threshold potassium conductance (G_KLT) is a key feature of neurons involved in fine temporal structure coding for sound localization, but a role for these channels in intensity or spectrotemporal coding has not been established. To determine whether G_KLT affects the phenotypical variation and temporal properties of NA neurons, we applied dendrotoxin-I (DTX), a potent antagonist of Kv1-type potassium channels, to chick brain stem slices in vitro during whole cell patch-clamp recordings. We found a cell-type specific subset of NA neurons that was sensitive to DTX: single-spiking NA neurons were most profoundly affected, as well as a subset of tonic-firing neurons. Both tonic I (phasic onset bursting) and tonic II (delayed firing) neurons showed DTX sensitivity in their firing rate and phenotypical firing pattern. Tonic III neurons were unaffected. Spike time reliability and fluctuation sensitivity measured in DTX-sensitive NA neurons was also reduced with DTX. Finally, DTX reduced spike threshold adaptation in these neurons, suggesting that G_KLT contributes to the temporal properties that allow coding of rapid changes in the inputs to NA neurons. These results suggest that variation in Kv1 channel expression may be a key factor in functional diversity in the avian cochlear nucleus.NEW & NOTEWORTHY The dendrotoxin-sensitive voltage-gated potassium conductance typically associated with neuronal coincidence detection in the timing pathway for sound localization is demonstrated to affect spiking patterns and temporal input sensitivity in the intensity pathway in the avian auditory brain stem. The Kv1-family channels appear to be present in a subset of cochlear nucleus angularis neurons, regulate spike threshold dynamics underlying high-pass membrane filtering, and contribute to intrinsic firing diversity.

Ion Channel Degeneracy, Variability, and Covariation in Neuron and Circuit Resilience

The large number of ion channels found in all nervous systems poses fundamental questions concerning how the characteristic intrinsic properties of single neurons are determined by the specific subsets of channels they express. All neurons display many different ion channels with overlapping voltage- and time-dependent properties. We speculate that these overlapping properties promote resilience in neuronal function. Individual neurons of the same cell type show variability in ion channel conductance densities even though they can generate reliable and similar behavior. This complicates a simple assignment of function to any conductance and is associated with variable responses of neurons of the same cell type to perturbations, deletions, and pharmacological manipulation. Ion channel genes often show strong positively correlated expression, which may result from the molecular and developmental rules that determine which ion channels are expressed in a given cell type.

Statistical Analysis of Decoding Performances of Diverse Populations of Neurons

A central theme in computational neuroscience is determining the neural correlates of efficient and accurate coding of sensory signals. Diversity, or heterogeneity, of intrinsic neural attributes is known to exist in many brain areas and is thought to significantly affect neural coding. Recent theoretical and experimental work has argued that in uncoupled networks, coding is most accurate at intermediate levels of heterogeneity. Here we consider this question with data from in vivo recordings of neurons in the electrosensory system of weakly electric fish subject to the same realization of noisy stimuli; we use a generalized linear model (GLM) to assess the accuracy of (Bayesian) decoding of stimulus given a population spiking response. The long recordings enable us to consider many uncoupled networks and a relatively wide range of heterogeneity, as well as many instances of the stimuli, thus enabling us to address this question with statistical power. The GLM decoding is performed on a single long time series of data to mimic realistic conditions rather than using trial-averaged data for better model fits. For a variety of fixed network sizes, we generally find that the optimal levels of heterogeneity are at intermediate values, and this holds in all core components of GLM. These results are robust to several measures of decoding performance, including the absolute value of the error, error weighted by the uncertainty of the estimated stimulus, and the correlation between the actual and estimated stimulus. Although a quadratic fit to decoding performance as a function of heterogeneity is statistically significant, the result is highly variable with low R2 values. Taken together, intermediate levels of neural heterogeneity are indeed a prominent attribute for efficient coding even within a single time series, but the performance is highly variable.

Spike threshold adaptation diversifies neuronal operating modes in the auditory brain stem

Single neurons function along a spectrum of neuronal operating modes whose properties determine how the output firing activity is generated from synaptic input. The auditory brain stem contains a diversity of neurons, from pure coincidence detectors to pure integrators and those with intermediate properties. We investigated how intrinsic spike initiation mechanisms regulate neuronal operating mode in the avian cochlear nucleus. Although the neurons in one division of the avian cochlear nucleus, nucleus magnocellularis, have been studied in depth, the spike threshold dynamics of the tonically firing neurons of a second division of cochlear nucleus, nucleus angularis (NA), remained unexplained. The input-output functions of tonically firing NA neurons were interrogated with directly injected in vivo-like current stimuli during whole cell patch-clamp recordings in vitro. Increasing the amplitude of the noise fluctuations in the current stimulus enhanced the firing rates in one subset of tonically firing neurons ("differentiators") but not another ("integrators"). We found that spike thresholds showed significantly greater adaptation and variability in the differentiator neurons. A leaky integrate-and-fire neuronal model with an adaptive spike initiation process derived from sodium channel dynamics was fit to the firing responses and could recapitulate >80% of the precise temporal firing across a range of fluctuation and mean current levels. Greater threshold adaptation explained the frequency-current curve changes due to a hyperpolarized shift in the effective adaptation voltage range and longer-lasting threshold adaptation in differentiators. The fine-tuning of the intrinsic properties of different NA neurons suggests they may have specialized roles in spectrotemporal processing.NEW & NOTEWORTHY Avian cochlear nucleus angularis (NA) neurons are responsible for encoding sound intensity for sound localization and spectrotemporal processing. An adaptive spike threshold mechanism fine-tunes a subset of repetitive-spiking neurons in NA to confer coincidence detector-like properties. A model based on sodium channel inactivation properties reproduced the activity via a hyperpolarized shift in adaptation conferring fluctuation sensitivity.

Intrinsic physiological properties underlie auditory response diversity in the avian cochlear nucleus

Sensory systems exploit parallel processing of stimulus features to enable rapid, simultaneous extraction of information. Mechanisms that facilitate this differential extraction of stimulus features can be intrinsic or synaptic in origin. A subdivision of the avian cochlear nucleus, nucleus angularis (NA), extracts sound intensity information from the auditory nerve and contains neurons that exhibit diverse responses to sound and current injection. NA neurons project to multiple regions ascending the auditory brain stem including the superior olivary nucleus, lateral lemniscus, and avian inferior colliculus, with functional implications for inhibitory gain control and sound localization. Here we investigated whether the diversity of auditory response patterns in NA can be accounted for by variation in intrinsic physiological features. Modeled sound-evoked auditory nerve input was applied to NA neurons with dynamic clamp during in vitro whole cell recording at room temperature. Temporal responses to auditory nerve input depended on variation in intrinsic properties, and the low-threshold K⁺ current was implicated as a major contributor to temporal response diversity and neuronal input-output functions. An auditory nerve model of acoustic amplitude modulation produced synchrony coding of modulation frequency that depended on the intrinsic physiology of the individual neuron. In Primary-Like neurons, varying low-threshold K⁺ conductance with dynamic clamp altered temporal modulation tuning bidirectionally. Taken together, these data suggest that intrinsic physiological properties play a key role in shaping auditory response diversity to both simple and more naturalistic auditory stimuli in the avian cochlear nucleus. NEW & NOTEWORTHY This article addresses the question of how the nervous system extracts different information in sounds. Neurons in the cochlear nucleus show diverse responses to acoustic stimuli that may allow for parallel processing of acoustic features. The present studies suggest that diversity in intrinsic physiological features of individual neurons, including levels of a low voltage-activated K⁺ current, play a major role in regulating the diversity of auditory responses.

Systematic Analysis of Transmitter Coexpression Reveals Organizing Principles of Local Interneuron Heterogeneity

Broad neuronal classes are surprisingly heterogeneous across many parameters, and subclasses often exhibit partially overlapping traits including transmitter coexpression. However, the extent to which transmitter coexpression occurs in predictable, consistent patterns is unknown. Here, we demonstrate that pairwise coexpression of GABA and multiple neuropeptide families by olfactory local interneurons (LNs) of the moth Manduca sexta is highly heterogeneous, with a single LN capable of expressing neuropeptides from at least four peptide families and few instances in which neuropeptides are consistently coexpressed. Using computational modeling, we demonstrate that observed coexpression patterns cannot be explained by independent probabilities of expression of each neuropeptide. Our analyses point to three organizing principles that, once taken into consideration, allow replication of overall coexpression structure: (1) peptidergic neurons are highly likely to coexpress GABA; (2) expression probability of allatotropin depends on myoinhibitory peptide expression; and (3) the all-or-none coexpression patterns of tachykinin neurons with several other neuropeptides. For other peptide pairs, the presence of one peptide was not predictive of the presence of the other, and coexpression probability could be replicated by independent probabilities. The stochastic nature of these coexpression patterns highlights the heterogeneity of transmitter content among LNs and argues against clear-cut definition of subpopulation types based on the presence of single neuropeptides. Furthermore, the receptors for all neuropeptides and GABA were expressed within each population of principal neuron type in the antennal lobe (AL). Thus, activation of any given LN results in a dynamic cocktail of modulators that have the potential to influence every level of olfactory processing within the AL.

A biophysical modelling platform of the cochlear nucleus and other auditory circuits: From channels to networks

Models of the auditory brainstem have been an invaluable tool for testing hypotheses about auditory information processing and for highlighting the most important gaps in the experimental literature. Due to the complexity of the auditory brainstem, and indeed most brain circuits, the dynamic behavior of the system may be difficult to predict without a detailed, biologically realistic computational model. Despite the sensitivity of models to their exact construction and parameters, most prior models of the cochlear nucleus have incorporated only a small subset of the known biological properties. This confounds the interpretation of modelling results and also limits the potential future uses of these models, which require a large effort to develop. To address these issues, we have developed a general purpose, biophysically detailed model of the cochlear nucleus for use both in testing hypotheses about cochlear nucleus function and also as an input to models of downstream auditory nuclei. The model implements conductance-based Hodgkin-Huxley representations of cells using a Python-based interface to the NEURON simulator. Our model incorporates most of the quantitatively characterized intrinsic cell properties, synaptic properties, and connectivity available in the literature, and also aims to reproduce the known response properties of the canonical cochlear nucleus cell types. Although we currently lack the empirical data to completely constrain this model, our intent is for the model to continue to incorporate new experimental results as they become available.

Stable encoding of sounds over a broad range of statistical parameters in the auditory cortex

Natural auditory scenes possess highly structured statistical regularities, which are dictated by the physics of sound production in nature, such as scale‐invariance. We recently identified that natural water sounds exhibit a particular type of scale invariance, in which the temporal modulation within spectral bands scales with the centre frequency of the band. Here, we tested how neurons in the mammalian primary auditory cortex encode sounds that exhibit this property, but differ in their statistical parameters. The stimuli varied in spectro‐temporal density and cyclo‐temporal statistics over several orders of magnitude, corresponding to a range of water‐like percepts, from pattering of rain to a slow stream. We recorded neuronal activity in the primary auditory cortex of awake rats presented with these stimuli. The responses of the majority of individual neurons were selective for a subset of stimuli with specific statistics. However, as a neuronal population, the responses were remarkably stable over large changes in stimulus statistics, exhibiting a similar range in firing rate, response strength, variability and information rate, and only minor variation in receptive field parameters. This pattern of neuronal responses suggests a potentially general principle for cortical encoding of complex acoustic scenes: while individual cortical neurons exhibit selectivity for specific statistical features, a neuronal population preserves a constant response structure across a broad range of statistical parameters.