Spectrotemporal Processing in Spectral Tuning Modules of Cat Primary Auditory Cortex

Spectral integration properties show topographical order in cat primary auditory cortex (AI). Along the iso-frequency domain, regions with predominantly narrowly tuned (NT) neurons are segregated from regions with more broadly tuned (BT) neurons, forming distinct processing modules. Despite their prominent spatial segregation, spectrotemporal processing has not been compared for these regions. We identified these NT and BT regions with broad-band ripple stimuli and characterized processing differences between them using both spectrotemporal receptive fields (STRFs) and nonlinear stimulus/firing rate transformations. The durations of STRF excitatory and inhibitory subfields were shorter and the best temporal modulation frequencies were higher for BT neurons than for NT neurons. For NT neurons, the bandwidth of excitatory and inhibitory subfields was matched, whereas for BT neurons it was not. Phase locking and feature selectivity were higher for NT neurons. Properties of the nonlinearities showed only slight differences across the bandwidth modules. These results indicate fundamental differences in spectrotemporal preferences - and thus distinct physiological functions - for neurons in BT and NT spectral integration modules. However, some global processing aspects, such as spectrotemporal interactions and nonlinear input/output behavior, appear to be similar for both neuronal subgroups. The findings suggest that spectral integration modules in AI differ in what specific stimulus aspects are processed, but they are similar in the manner in which stimulus information is processed

Direct Measurement of Perchlorate Exposure Biomarkers in a Highly Exposed Population: A Pilot Study

Exposure to perchlorate is ubiquitous in the United States and has been found to be widespread in food and drinking water. People living in the lower Colorado River region may have perchlorate exposure because of perchlorate in ground water and locally-grown produce. Relatively high doses of perchlorate can inhibit iodine uptake and impair thyroid function, and thus could impair neurological development in utero. We examined human exposures to perchlorate in the Imperial Valley among individuals consuming locally grown produce and compared perchlorate exposure doses to state and federal reference doses. We collected 24-hour urine specimen from a convenience sample of 31 individuals and measured urinary excretion rates of perchlorate, thiocyanate, nitrate, and iodide. In addition, drinking water and local produce were also sampled for perchlorate. All but two of the water samples tested negative for perchlorate. Perchlorate levels in 79 produce samples ranged from non-detect to 1816 ppb. Estimated perchlorate doses ranged from 0.02 to 0.51 µg/kg of body weight/day. Perchlorate dose increased with the number of servings of dairy products consumed and with estimated perchlorate levels in produce consumed. The geometric mean perchlorate dose was 70% higher than for the NHANES reference population. Our sample of 31 Imperial Valley residents had higher perchlorate dose levels compared with national reference ranges. Although none of our exposure estimates exceeded the U. S. EPA reference dose, three participants exceeded the acceptable daily dose as defined by bench mark dose methods used by the California Office of Environmental Health Hazard Assessment

A Corticothalamic Circuit Model for Sound Identification in Complex Scenes

The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal

Repetition Enhancement for Frequency-Modulated but Not Unmodulated Sounds: A Human MEG Study

BACKGROUND: Decoding of frequency-modulated (FM) sounds is essential for phoneme identification. This study investigates selectivity to FM direction in the human auditory system. METHODOLOGY/PRINCIPAL FINDINGS: Magnetoencephalography was recorded in 10 adults during a two-tone adaptation paradigm with a 200-ms interstimulus-interval. Stimuli were pairs of either same or different frequency modulation direction. To control that FM repetition effects cannot be accounted for by their on- and offset properties, we additionally assessed responses to pairs of unmodulated tones with either same or different frequency composition. For the FM sweeps, N1m event-related magnetic field components were found at 103 and 130 ms after onset of the first (S1) and second stimulus (S2), respectively. This was followed by a sustained component starting at about 200 ms after S2. The sustained response was significantly stronger for stimulation with the same compared to different FM direction. This effect was not observed for the non-modulated control stimuli. CONCLUSIONS/SIGNIFICANCE: Low-level processing of FM sounds was characterized by repetition enhancement to stimulus pairs with same versus different FM directions. This effect was FM-specific; it did not occur for unmodulated tones. The present findings may reflect specific interactions between frequency separation and temporal distance in the processing of consecutive FM sweeps

Prostaglandin E2 Prevents Hyperosmolar-Induced Human Mast Cell Activation through Prostanoid Receptors EP2 and EP4

Background: Mast cells play a critical role in allergic and inflammatory diseases, including exercise-induced bronchoconstriction (EIB) in asthma. The mechanism underlying EIB is probably related to increased airway fluid osmolarity that activates mast cells to the release inflammatory mediators. These mediators then act on bronchial smooth muscle to cause bronchoconstriction. In parallel, protective substances such as prostaglandin E2 (PGE2) are probably also released and could explain the refractory period observed in patients with EIB. Objective: This study aimed to evaluate the protective effect of PGE2 on osmotically activated mast cells, as a model of exercise-induced bronchoconstriction. Methods: We used LAD2, HMC-1, CD34-positive, and human lung mast cell lines. Cells underwent a mannitol challenge, and the effects of PGE2 and prostanoid receptor (EP) antagonists for EP1-4 were assayed on the activated mast cells. Beta-hexosaminidase release, protein phosphorylation, and calcium mobilization were assessed. Results: Mannitol both induced mast cell degranulation and activated phosphatidyl inositide 3-kinase and mitogen-activated protein kinase (MAPK) pathways, thereby causing de novo eicosanoid and cytokine synthesis. The addition of PGE2 significantly reduced mannitol-induced degranulation through EP2 and EP4 receptors, as measured by beta-hexosaminidase release, and consequently calcium influx. Extracellular-signal-regulated kinase 1/2, c-Jun N-terminal kinase, and p38 phosphorylation were diminished when compared with mannitol activation alone. Conclusions:Our data show a protective role for the PGE2 receptors EP2 and EP4 following osmotic changes, through the reduction of human mast cell activity caused by calcium influx impairment and MAP kinase inhibition

Second Order Dimensionality Reduction Using Minimum and Maximum Mutual Information Models

Conventional methods used to characterize multidimensional neural feature selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MID), are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality. To overcome these issues, we propose two new dimensionality reduction methods that use minimum and maximum information models. These methods are information theoretic extensions of STC that can be used with non-Gaussian stimulus distributions to find relevant linear subspaces of arbitrary dimensionality. We compare these new methods to the conventional methods in two ways: with biologically-inspired simulated neurons responding to natural images and with recordings from macaque retinal and thalamic cells responding to naturalistic time-varying stimuli. With non-Gaussian stimuli, the minimum and maximum information methods significantly outperform STC in all cases, whereas MID performs best in the regime of low dimensional feature spaces

Millisecond-Timescale Local Network Coding in the Rat Primary Somatosensory Cortex

Correlation among neocortical neurons is thought to play an indispensable role in mediating sensory processing of external stimuli. The role of temporal precision in this correlation has been hypothesized to enhance information flow along sensory pathways. Its role in mediating the integration of information at the output of these pathways, however, remains poorly understood. Here, we examined spike timing correlation between simultaneously recorded layer V neurons within and across columns of the primary somatosensory cortex of anesthetized rats during unilateral whisker stimulation. We used Bayesian statistics and information theory to quantify the causal influence between the recorded cells with millisecond precision. For each stimulated whisker, we inferred stable, whisker-specific, dynamic Bayesian networks over many repeated trials, with network similarity of 83.3±6% within whisker, compared to only 50.3±18% across whiskers. These networks further provided information about whisker identity that was approximately 6 times higher than what was provided by the latency to first spike and 13 times higher than what was provided by the spike count of individual neurons examined separately. Furthermore, prediction of individual neurons' precise firing conditioned on knowledge of putative pre-synaptic cell firing was 3 times higher than predictions conditioned on stimulus onset alone. Taken together, these results suggest the presence of a temporally precise network coding mechanism that integrates information across neighboring columns within layer V about vibrissa position and whisking kinetics to mediate whisker movement by motor areas innervated by layer V

Learning, Memory, and the Role of Neural Network Architecture

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems

Hierarchical decision-making produces persistent differences in learning performance

Human organizations are commonly characterized by a hierarchical chain of command that facilitates division of labor and integration of effort. Higher-level employees set the strategic frame that constrains lower-level employees who carry out the detailed operations serving to implement the strategy. Typically, strategy and operational decisions are carried out by different individuals that act over different timescales and rely on different kinds of information. We hypothesize that when such decision processes are hierarchically distributed among different individuals, they produce highly heterogeneous and strongly path-dependent joint learning dynamics. To investigate this, we design laboratory experiments of human dyads facing repeated joint tasks, in which one individual is assigned the role of carrying out strategy decisions and the other operational ones. The experimental behavior generates a puzzling bimodal performance distribution-some pairs learn, some fail to learn after a few periods. We also develop a computational model that mirrors the experimental settings and predicts the heterogeneity of performance by human dyads. Comparison of experimental and simulation data suggests that self-reinforcing dynamics arising from initial choices are sufficient to explain the performance heterogeneity observed experimentally

Control of somatosensory cortical processing by thalamic posterior medial nucleus: A new role of thalamus in cortical function

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Current knowledge of thalamocortical interaction comes mainly from studying lemniscal thalamic systems. Less is known about paralemniscal thalamic nuclei function. In the vibrissae system, the posterior medial nucleus (POm) is the corresponding paralemniscal nucleus. POm neurons project to L1 and L5A of the primary somatosensory cortex (S1) in the rat brain. It is known that L1 modifies sensory-evoked responses through control of intracortical excitability suggesting that L1 exerts an influence on whisker responses. Therefore, thalamocortical pathways targeting L1 could modulate cortical firing. Here, using a combination of electrophysiology and pharmacology in vivo, we have sought to determine how POm influences cortical processing. In our experiments, single unit recordings performed in urethane- anesthetized rats showed that POm imposes precise control on the magnitude and duration of supra- and infragranular barrel cortex whisker responses. Our findings demonstrated that L1 inputs from POm imposed a time and intensity dependent regulation on cortical sensory processing. Moreover, we found that blocking L1 GABAergic inhibition or blocking P/Q-type Ca2+ channels in L1 prevents POm adjustment of whisker responses in the barrel cortex. Additionally, we found that POm was also controlling the sensory processing in S2 and this regulation was modulated by corticofugal activity from L5 in S1. Taken together, our data demonstrate the determinant role exerted by the POm in the adjustment of somatosensory cortical processing and in the regulation of cortical processing between S1 and S2. We propose that this adjustment could be a thalamocortical gain regulation mechanism also present in the processing of information between cortical areas.This work was supported by a grant from Ministerio de Economia y Competitividad (BFU2012- 36107