11,905 research outputs found

    Cortical transformation of spatial processing for solving the cocktail party problem: a computational model(1,2,3).

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    In multisource, "cocktail party" sound environments, human and animal auditory systems can use spatial cues to effectively separate and follow one source of sound over competing sources. While mechanisms to extract spatial cues such as interaural time differences (ITDs) are well understood in precortical areas, how such information is reused and transformed in higher cortical regions to represent segregated sound sources is not clear. We present a computational model describing a hypothesized neural network that spans spatial cue detection areas and the cortex. This network is based on recent physiological findings that cortical neurons selectively encode target stimuli in the presence of competing maskers based on source locations (Maddox et al., 2012). We demonstrate that key features of cortical responses can be generated by the model network, which exploits spatial interactions between inputs via lateral inhibition, enabling the spatial separation of target and interfering sources while allowing monitoring of a broader acoustic space when there is no competition. We present the model network along with testable experimental paradigms as a starting point for understanding the transformation and organization of spatial information from midbrain to cortex. This network is then extended to suggest engineering solutions that may be useful for hearing-assistive devices in solving the cocktail party problem.R01 DC000100 - NIDCD NIH HHSPublished versio

    Cortical Spike Synchrony as a Measure of Input Familiarity

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    J.G.O. was supported by the Ministerio de Economia y Competividad and FEDER (Spain, project FIS2015-66503-C3-1-P) and the ICREA Academia programme. E.U. acknowledges support from the Scottish Universities Life Sciences Alliance (SULSA) and HPC-Europa2.Peer reviewedPostprin

    Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

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    Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

    The functional organization of area V2, I: Specialization across stripes and layers

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    We used qualitative tests to assess the sensitivity of 1043 V2 neurons (predominantly multiunits) in anesthetised macaque monkeys to direction, length. orientation. and color of moving bar stimuli. Spectral sensitivity was additionally tested by noting ON or OFF responses to flashed stimuli of varied size and color. The location of 649 units was identified with respect to cycles of cytochrome oxidase stripes (thick-inter-thin-inter) and cortical layer. We used an initial 8-way stripe classification (4 stripes. and 4 "marginal" zones at interstripes boundaries), and a 9-way layer classification (5 standard layers (2-6), and 4 "marginal" Strata at layer boundaries). These classes were collapsed differently for particular analyses of functional distribution:. the main stripe-by-layer analysis was performed on 18 compartments (3 stripes X 6 layers), We found direction sensitivity only within thick stripes, orientation sensitivity mainly in thick stripes and interstripes. and spectral sensitivity mainly in thin stripes. Positive length summation was relatively more frequent in thick stripes and interstripes. and negative length/size summation in thin stripes. All these "majority" characteristics of stripes were most prominent in layers 3A and 3B. By contrast, "minority" characteristics (e.g. spectral sensitivity in thick stripes positive size summation in thin stripes) tended to be most frequent in the outer layers, that is, layers 2 and 6. In consequence, going by the four functions tested, the distinctions between stripes were maximal in layer 3, moderate in layer 2, and minimal in layer 6. Pooling all layers, there was some indication of asymmetry in the stripe cycle, in that thin stripe characteristics (spectral sensitivity, orientation insensitivity, and negative size summation) were also evident in the marginal zone and interstripe immediately lateral to a thin stripe, but less so medially. Within thin stripes, spectral and orientation selectivities were negatively correlated this was still more accentuated amongst the minority spectrally tuned cells of thick stripes. but absent from interstripes, where these two properties were randomly assorted. Directional and spectral sensitivities were each coupled to negative size summation. but not to each other. We conclude that these functional characteristics of stripes are consistent with segregated. specialized pathways ascending through their middle layers, whilst the outer layers. 1, 2, and 6, utilize feedback from higher areas to adopt a more integrative role

    An Interneuron Circuit Reproducing Essential Spectral Features of Field Potentials

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    This document is the Accepted Manuscript version of the following article: Reinoud Maex, β€˜An Interneuron Circuit Reproducing Essential Spectral Features of Field Potentials’, Neural Computation, March 2018. Under embargo until 22 June 2018. The final, definitive version of this paper is available online at doi: https://doi.org/10.1162/NECO_a_01068. Β© 2018 Massachusetts Institute of Technology. Content in the UH Research Archive is made available for personal research, educational, and non-commercial purposes only. Unless otherwise stated, all content is protected by copyright, and in the absence of an open license, permissions for further re-use should be sought from the publisher, the author, or other copyright holder.Recent advances in engineering and signal processing have renewed the interest in invasive and surface brain recordings, yet many features of cortical field potentials remain incompletely understood. In the present computational study, we show that a model circuit of interneurons, coupled via both GABA(A) receptor synapses and electrical synapses, reproduces many essential features of the power spectrum of local field potential (LFP) recordings, such as 1/f power scaling at low frequency (< 10 Hz) , power accumulation in the Ξ³-frequency band (30–100 Hz), and a robust Ξ± rhythm in the absence of stimulation. The low-frequency 1/f power scaling depends on strong reciprocal inhibition, whereas the Ξ± rhythm is generated by electrical coupling of intrinsically active neurons. As in previous studies, the Ξ³ power arises through the amplifica- tion of single-neuron spectral properties, owing to the refractory period, by parameters that favour neuronal synchrony, such as delayed inhibition. The present study also confirms that both synaptic and voltage-gated membrane currents substantially contribute to the LFP, and that high-frequency signals such as action potentials quickly taper off with distance. Given the ubiquity of electrically coupled interneuron circuits in the mammalian brain, they may be major determinants of the recorded potentials.Peer reviewe

    Coding of auditory space

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    Behavioral, anatomical, and physiological approaches can be integrated in the study of sound localization in barn owls. Space representation in owls provides a useful example for discussion of place and ensemble coding. Selectivity for space is broad and ambiguous in low-order neurons. Parallel pathways for binaural cues and for different frequency bands converge on high-order space-specific neurons, which encode space more precisely. An ensemble of broadly tuned place-coding neurons may converge on a single high-order neuron to create an improved labeled line. Thus, the two coding schemes are not alternate methods. Owls can localize sounds by using either the isomorphic map of auditory space in the midbrain or forebrain neural networks in which space is not mapped
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