Noise reduction of coincidence detector output by the inferior colliculus of the barn owl

A recurring theme in theoretical work is that integration over populations of similarly tuned neurons can reduce neural noise. However, there are relatively few demonstrations of an explicit noise reduction mechanism in a neural network. Here we demonstrate that the brainstem of the barn owl includes a stage of processing apparently devoted to increasing the signal-to-noise ratio in the encoding of the interaural time difference (ITD), one of two primary binaural cues used to compute the position of a sound source in space. In the barn owl, the ITD is processed in a dedicated neural pathway that terminates at the core of the inferior colliculus (ICcc). The actual locus of the computation of the ITD is before ICcc in the nucleus laminaris (NL), and ICcc receives no inputs carrying information that did not originate in NL. Unlike in NL, the rate-ITD functions of ICcc neurons require as little as a single stimulus presentation per ITD to show coherent ITD tuning. ICcc neurons also displayed a greater dynamic range with a maximal difference in ITD response rates approximately double that seen in NL. These results indicate that ICcc neurons perform a computation functionally analogous to averaging across a population of similarly tuned NL neurons

A common periodic representation of interaural time differences in mammalian cortex

Binaural hearing, the ability to detect small differences in the timing and level of sounds at the two ears, underpins the ability to localize sound sources along the horizontal plane, and is important for decoding complex spatial listening environments into separate objects - a critical factor in 'cocktail-party listening'. For human listeners, the most important spatial cue is the interaural time difference (ITD). Despite many decades of neurophysiological investigations of ITD sensitivity in small mammals, and computational models aimed at accounting for human perception, a lack of concordance between these studies has hampered our understanding of how the human brain represents and processes ITDs. Further, neural coding of spatial cues might depend on factors such as head-size or hearing range, which differ considerably between humans and commonly used experimental animals. Here, using magnetoencephalography (MEG) in human listeners, and electro-corticography (ECoG) recordings in guinea pig-a small mammal representative of a range of animals in which ITD coding has been assessed at the level of single-neuron recordings-we tested whether processing of ITDs in human auditory cortex accords with a frequency-dependent periodic code of ITD reported in small mammals, or whether alternative or additional processing stages implemented in psychoacoustic models of human binaural hearing must be assumed. Our data were well accounted for by a model consisting of periodically tuned ITD-detectors, and were highly consistent across the two species. The results suggest that the representation of ITD in human auditory cortex is similar to that found in other mammalian species, a representation in which neural responses to ITD are determined by phase differences relative to sound frequency rather than, for instance, the range of ITDs permitted by head size or the absolute magnitude or direction of ITD

Auditory evoked fields measured non-invasively with small-animal MEG reveal rapid repetition suppression in the guinea pig

In animal models, single-neuron response properties such as stimulus-specific adaptation (SSA) have been described as possible precursors to the mismatch negativity (MMN), a human brain response to stimulus change. Here, we attempt to bridge the gap between human and animal studies by characterising responses to changes in the frequency of repeated tone series in the anaesthetised guinea pig using small-animal magnetoencephalography (MEG). We show that: (1) auditory evoked fields (AEFs) qualitatively similar to those observed in human MEG studies can be detected non-invasively in rodents using small-animal MEG; (2) guinea-pig AEF amplitudes reduce rapidly with tone repetition, and this AEF reduction is largely complete by the second tone in a repeated series; and (3) differences between responses to the first (deviant) and later (standard) tones after a frequency transition resemble those previously observed in awake humans using a similar stimulus paradigm

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc

Effects of an H3R Antagonist on the Animal Model of Autism Induced by Prenatal Exposure to Valproic Acid

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders primarily characterized by impaired social interaction and communication, and by restricted repetitive behaviors and interests. Ligands of histamine receptor 3 (H3R) are considered potential therapeutic agents for the treatment of different brain disorders and cognitive impairments. Considering this, the aim of the present study is to evaluate the actions of ciproxifan (CPX), an H3R antagonist, on the animal model of autism induced by prenatal exposure to valproic acid (VPA). Swiss mice were prenatally exposed to VPA on embryonic day 11 and assessed for social behavior, nociceptive threshold and repetitive behavior at 50 days of life. The treatment with CPX (3 mg/kg) or saline was administered 30 minutes before each behavioral test. The VPA group presented lower sociability index compared to VPA animals that were treated with CPX. Compared to the Control group, VPA animals presented a significantly higher nociceptive threshold, and treatment with CPX was not able to modify this parameter. In the marble burying test, the number of marbles buried by VPA animals was consistent with markedly repetitive behavior. VPA animals that received CPX buried a reduced amount of marbles. In summary, we report that an acute dose of CPX is able to attenuate sociability deficits and stereotypies present in the VPA model of autism. Our findings have the potential to help the investigations of both the molecular underpinnings of ASD and of possible treatments to ameliorate the ASD symptomatology, although more research is still necessary to corroborate and expand this initial data

Estimating Receptive Fields from Responses to Natural Stimuli with Asymmetric Intensity Distributions

The reasons for using natural stimuli to study sensory function are quickly mounting, as recent studies have revealed important differences in neural responses to natural and artificial stimuli. However, natural stimuli typically contain strong correlations and are spherically asymmetric (i.e. stimulus intensities are not symmetrically distributed around the mean), and these statistical complexities can bias receptive field (RF) estimates when standard techniques such as spike-triggered averaging or reverse correlation are used. While a number of approaches have been developed to explicitly correct the bias due to stimulus correlations, there is no complementary technique to correct the bias due to stimulus asymmetries. Here, we develop a method for RF estimation that corrects reverse correlation RF estimates for the spherical asymmetries present in natural stimuli. Using simulated neural responses, we demonstrate how stimulus asymmetries can bias reverse-correlation RF estimates (even for uncorrelated stimuli) and illustrate how this bias can be removed by explicit correction. We demonstrate the utility of the asymmetry correction method under experimental conditions by estimating RFs from the responses of retinal ganglion cells to natural stimuli and using these RFs to predict responses to novel stimuli

A Generalized Linear Model for Estimating Spectrotemporal Receptive Fields from Responses to Natural Sounds

In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM). In this model, each cell's input is described by: 1) a stimulus filter (STRF); and 2) a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs) and modulation limited (ml) noise. We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons

Patterns of Reproductive Isolation in Toads

Understanding the general features of speciation is an important goal in evolutionary biology, and despite significant progress, several unresolved questions remain. We analyzed an extensive comparative dataset consisting of more than 1900 crosses between 92 species of toads to infer patterns of reproductive isolation. This unique dataset provides an opportunity to examine the strength of reproductive isolation, the development and sex ratios of hybrid offspring, patterns of fertility and infertility, and polyploidization in hybrids all in the context of genetic divergence between parental species. We found that the strength of intrinsic postzygotic isolation increases with genetic divergence, but relatively high levels of divergence are necessary before reproductive isolation is complete in toads. Fertilization rates were not correlated to genetic divergence, but hatching success, the number of larvae produced, and the percentage of tadpoles reaching metamorphosis were all inversely related with genetic divergence. Hybrids between species with lower levels of divergence developed to metamorphosis, while hybrids with higher levels of divergence stopped developing in gastrula and larval stages. Sex ratios of hybrid offspring were biased towards males in 70% of crosses and biased towards females in 30% of crosses. Hybrid females from crosses between closely related species were completely fertile, while approximately half (53%) of hybrid males were sterile, with sterility predicted by genetic divergence. The degree of abnormal ploidy in hybrids was positively related to genetic divergence between parental species, but surprisingly, polyploidization had no effect on patterns of asymmetrical inviability. We discuss explanations for these patterns, including the role of Haldane's rule in toads and anurans in general, and suggest mechanisms generating patterns of reproductive isolation in anurans

The Role of the Medial Prefrontal Cortex in Regulating Social Familiarity-Induced Anxiolysis

Overcoming specific fears and subsequent anxiety can be greatly enhanced by the presence of familiar social partners, but the neural circuitry that controls this phenomenon remains unclear. To overcome this, the social interaction (SI) habituation test was developed in this lab to systematically investigate the effects of social familiarity on anxiety-like behavior in rats. Here, we show that social familiarity selectively reduced anxiety-like behaviors induced by an ethological anxiogenic stimulus. The anxiolytic effect of social familiarity could be elicited over multiple training sessions and was specific to both the presence of the anxiogenic stimulus and the familiar social partner. In addition, socially familiar conspecifics served as a safety signal, as anxiety-like responses returned in the absence of the familiar partner. The expression of the social familiarity-induced anxiolysis (SFiA) appears dependent on the prefrontal cortex (PFC), an area associated with cortical regulation of fear and anxiety behaviors. Inhibition of the PFC, with bilateral injections of the GABAA agonist muscimol, selectively blocked the expression of SFiA while having no effect on SI with a novel partner. Finally, the effect of D-cycloserine, a cognitive enhancer that clinically enhances behavioral treatments for anxiety, was investigated with SFiA. D-cycloserine, when paired with familiarity training sessions, selectively enhanced the rate at which SFiA was acquired. Collectively, these outcomes suggest that the PFC has a pivotal role in SFiA, a complex behavior involving the integration of social cues of familiarity with contextual and emotional information to regulate anxiety-like behavior