Combining ultra-high-field fMRI adaptation and computational modelling to estimate neuronal frequency selectivity in human auditory cortex

Frequency selectivity is a ubiquitous property of auditory neurons. Measuring it in human auditory cortex may be crucial for understanding common auditory deficits, but current non-invasive neuroimaging techniques can only measure the aggregate response of large populations of cells, thereby overestimating tuning width. Here we attempted to estimate neuronal frequency tuning in human auditory cortex using a combination of fMRI-adaptation paradigm at 7T and computational modelling. We measured the BOLD response in the auditory cortex of eleven participants to a high frequency (3.8 kHz) probe presented alone or preceded by adaptors at different frequencies (0.5 to 3.8 kHz). From these data, we derived both the response tuning curves (the BOLD response to adaptors alone as a function of adaptor frequency) and adaptation tuning curves (the degree of response suppression to the probe as a function of adaptor frequency, assumed to reflect neuronal tuning) in primary and secondary auditory cortical areas, delineated in each participant. Results suggested the existence of both frequency-independent and frequency-specific adaptation components, with the latter being more frequency-tuned than response tuning curves. Using a computational model of neuronal adaptation and BOLD non-linearity in topographically-organized cortex, we demonstrate both that the frequency-specific adaptation component overestimates the underlying neuronal frequency tuning and that frequency-specific and frequency-independent adaptation component cannot easily be disentangled from the adaptation tuning curve. By fitting our model directly to the response and adaptation tuning curves, we derive a range of plausible values for neuronal frequency tuning. Our results suggest that fMRI adaptation is suitable for measuring neuronal frequency tuning properties in human auditory cortex, provided population effects and the non-linearity of BOLD response are taken into account.

Random Compressed Coding with Neurons

Classical models of efficient coding in neurons assume simple mean responses--'tuning curves'--such as bell shaped or monotonic functions of a stimulus feature. Real neurons, however, can be more complex: grid cells, for example, exhibit periodic responses which impart the neural population code with high accuracy. But do highly accurate codes require fine tuning of the response properties? We address this question with the use of a benchmark model: a neural network with random synaptic weights which result in output cells with irregular tuning curves. Irregularity enhances the local resolution of the code but gives rise to catastrophic, global errors. For optimal smoothness of the tuning curves, when local and global errors balance out, the neural network compresses information from a high-dimensional representation to a low-dimensional one, and the resulting distributed code achieves exponential accuracy. An analysis of recordings from monkey motor cortex points to such 'compressed efficient coding'. Efficient codes do not require a finely tuned design--they emerge robustly from irregularity or randomness.

Different excitation-inhibition correlations between spontaneous and tone-evoked activity in primary auditory cortex neurons

Tone-evoked synaptic excitation and inhibition are highly correlated in many neurons with V-shaped tuning curves in the primary auditory cortex of pentobarbital-anesthetized rats. In contrast, there is less correlation between spontaneous excitation and inhibition in visual cortex neurons under the same anesthetic conditions. However, it was not known whether the primary auditory cortex resembles visual cortex in having spontaneous excitation and inhibition that is less correlated than tone-evoked excitation and inhibition. Here we report whole-cell voltage-clamp measurements of spontaneous excitation and inhibition in primary auditory cortex neurons of pentobarbital-anesthetized rats. The larger excursions of both spontaneous excitatory and inhibitory currents appeared to consist of distinct events, with the inhibitory event rate typically lower than the excitatory event rate. We use the ratio of the excitatory event rate to the inhibitory event rate, and the assumption that the excitatory and inhibitory synaptic currents can each be reasonably described as a filtered Poisson process, to estimate the maximum spontaneous excitatory-inhibitory correlation for each neuron. In a subset of neurons, we also measured tone-evoked excitation and inhibition. In neurons with V-shaped tuning curves, although tone-evoked excitation and inhibition were highly correlated, the spontaneous inhibitory event rate was typically sufficiently lower than the spontaneous excitatory event rate to indicate a lower excitatory-inhibitory correlation for spontaneous activity than for tone-evoked responses.

An Extended k-Surface Framework for Electromagnetic Fields in Artificial Media

The complete understanding of the electromagnetic field characteristics in artificially created bulk or thin media is essential to the efficient harnessing of the multitude of linear and nonlinear effects resulting from it. Due to the fact that recently developed artificial metastructures exhibit controllable electric and magnetic properties that are completely different from natural ones, the spectrum of behavior resulting from subjecting such media to electromagnetic fields has to be revisited. In this paper, we introduce a k-surface framework that offers complete information on the dispersion properties of media with designer electric and magnetic responses with positive and negative values, as well as for the coupling between the two. The extension from the classic k-surface case resides in the consideration of magnetic and bianisotropic materials with positive and negative permittivity and permeability values, as well as the introduction of the chirality coefficient.To illustrate the applicability of our framework, we have investigated the conditions to obtain collinear second harmonic generation in the case of artificial media with positively and negatively valued electric and magnetic responses. As expected, the phase matching tuning curves, defined as the intersections between the k-surfaces at both frequencies, are significantly modified with respect to the classic ones.

Parallel Processing of Olfactory and Mechanosensory Information in the Honey Bee Antennal Lobe

In insects, neuronal responses to clean air have so far been reported only episodically in moths. Here we present results obtained by fast two-photon calcium imaging in the honey bee Apis mellifera, indicating a substantial involvement of the antennal lobe, the first olfactory neuropil, in the processing of mechanical stimuli. Clean air pulses generate a complex pattern of glomerular activation that provides a code for stimulus intensity and dynamics with a similar level of stereotypy as observed for the olfactory code. Overlapping the air pulses with odor stimuli reveals a superposition of mechanosensory and odor response codes with high contrast. On the mechanosensitive signal, modulations were observed in the same frequency regime as the oscillatory motion of the antennae, suggesting a possible way to detect odorless airflow directions. The transduction of mechanosensory information via the insect antennae has so far been attributed primarily to Johnston’s organ in the pedicel of the antenna. The possibility that the antennal lobe activation by clean air originates from Johnston’s organ could be ruled out, as the signal is suppressed by covering the surfaces of the otherwise freely moving and bending antennae, which should leave Johnston’s organ unaffected. The tuning curves of individual glomeruli indicate increased sensitivity at low-frequency mechanical oscillations as produced by the abdominal motion in waggle dance communication, suggesting a further potential function of this mechanosensory code. The discovery that the olfactory system can sense both odors and mechanical stimuli has recently been made also in mammals. The results presented here give hope that studies on insects can make a fundamental contribution to the cross-taxa understanding of this dual function, as only a few thousand neurons are involved in their brains, all of which are accessible by in vivo optical imaging.

Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.

Does the brain care about averages? A simple test.

Trial-averaged metrics, e.g. in the form of tuning curves and population response vectors, are a basic and widely accepted way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing in a specific context. The test probes two assumptions inherent in the usage of average neuronal metrics: 1) Reliability: Neuronal responses repeat consistently enough across single stimulus instances that the average response template they relate to remains recognizable to downstream regions. 2) Behavioural relevance: If a single-trial response is more similar to the average template, this should make it easier for the animal to identify the correct stimulus or action. We apply this test to a large publicly available data set featuring electrophysiological recordings from 42 cortical areas in behaving mice. In this data set, we show that single-trial responses were less correlated to the average response template than one would expect if they simply represented discrete versions of the template, down-sampled to a finite number of spikes. Moreover, single-trial responses were barely stimulus-specific — they could not be clearly assigned to the average response template of one stimulus. Most importantly, better-matched single-trial responses did not predict accurate behaviour for any of the recorded cortical areas. We conclude that in this data set, average responses do not seem particularly relevant to neuronal computation in a majority of brain areas, and we encourage other researchers to apply similar tests when using trial-averaged neuronal metrics.

Optical parametric generation in biaxial KTA crystal under pumping by YAG:Nd laser in arbitrary directions

Herein, on the basis of expressions for the refractive indices of isonormal waves, the possibility of performing collinear phase matching for optical parametric generation in arbitrary directions of a biaxial KTA crystal under pumping by radiation of a YAG:Nd laser is analyzed. The tuning curves that determine the tuning range of the signal and idler for type-I and II-type phase-matching and arbitrary angles θ and φ in cases where the tuning is carried out along the angle θ at a fixed angle φ and vice versa are calculated. The effective nonlinear coefficient is determined. It is shown that their maximum value is achieved аt a polar angle θ = 90° and type-II phase-matching. For the case of generation of eye-safe radiation the spectral and angular phase matching widths were estimated, as well as gain widths of KTA-OPO under monochromatic pumping.

Task-specific roles of local interneurons for inter- and intraglomerular signaling in the insect antennal lobe

Local interneurons (LNs) mediate complex interactions within the antennal lobe, the primary olfactory system of insects, and the functional analog of the vertebrate olfactory bulb. In the cockroach Periplaneta americana, as in other insects, several types of LNs with distinctive physiological and morphological properties can be defined. Here, we combined whole-cell patch-clamp recordings and Ca2+ imaging of individual LNs to analyze the role of spiking and nonspiking LNs in inter- and intraglomerular signaling during olfactory information processing. Spiking GABAergic LNs reacted to odorant stimulation with a uniform rise in [Ca2+]i in the ramifications of all innervated glomeruli. In contrast, in nonspiking LNs, glomerular Ca2+ signals were odorant specific and varied between glomeruli, resulting in distinct, glomerulus-specific tuning curves. The cell type-specific differences in Ca2+ dynamics support the idea that spiking LNs play a primary role in interglomerular signaling, while they assign nonspiking LNs an essential role in intraglomerular signaling.