Auditory cortical contrast enhancing by global winner-take-all inhibitory interactions

Sources of richness and ineffability for phenomenally conscious states

Conscious states-state that there is something it is like to be in-seem both rich or full of detail and ineffable or hard to fully describe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness. In our framework, the richness of conscious experience corresponds to the amount of information in a conscious state and ineffability corresponds to the amount of information lost at different stages of processing. We describe how attractor dynamics in working memory would induce impoverished recollections of our original experiences, how the discrete symbolic nature of language is insufficient for describing the rich and high-dimensional structure of experiences, and how similarity in the cognitive function of two individuals relates to improved communicability of their experiences to each other. While our model may not settle all questions relating to the explanatory gap, it makes progress toward a fully physicalist explanation of the richness and ineffability of conscious experience-two important aspects that seem to be part of what makes qualitative character so puzzling.

Compensating Level-Dependent Frequency Representation in Auditory Cortex by Synaptic Integration of Corticocortical Input

Robust perception of auditory objects over a large range of sound intensities is a fundamental feature of the auditory system. However, firing characteristics of single neurons across the entire auditory system, like the frequency tuning, can change significantly with stimulus intensity. Physiological correlates of level-constancy of auditory representations hence should be manifested on the level of larger neuronal assemblies or population patterns. In this study we have investigated how information of frequency and sound level is integrated on the circuit-level in the primary auditory cortex (AI) of the Mongolian gerbil. We used a combination of pharmacological silencing of corticocortically relayed activity and laminar current source density (CSD) analysis. Our data demonstrate that with increasing stimulus intensities progressively lower frequencies lead to the maximal impulse response within cortical input layers at a given cortical site inherited from thalamocortical synaptic inputs. We further identified a temporally precise intercolumnar synaptic convergence of early thalamocortical and horizontal corticocortical inputs. Later tone-evoked activity in upper layers showed a preservation of broad tonotopic tuning across sound levels without shifts towards lower frequencies. Synaptic integration within corticocortical circuits may hence contribute to a level-robust representation of auditory information on a neuronal population level in the auditory cortex.

A Readout Mechanism for Latency Codes

Response latency has been suggested as a possible source of information in the central nervous system when fast decisions are required. The accuracy of latency codes was studied in the past using a simplified readout algorithm termed the temporal-winner-take-all (tWTA). The tWTA is a competitive readout algorithm in which populations of neurons with a similar decision preference compete, and the algorithm selects according to the preference of the population that reaches the decision threshold first. It has been shown that this algorithm can account for accurate decisions among a small number of alternatives during short biologically relevant time periods. However, one of the major points of criticism of latency codes has been that it is unclear how can such a readout be implemented by the central nervous system. Here we show that the solution to this long standing puzzle may be rather simple. We suggest a mechanism that is based on reciprocal inhibition architecture, similar to that of the conventional winner-take-all, and show that under a wide range of parameters this mechanism is sufficient to implement the tWTA algorithm. This is done by first analyzing a rate toy model, and demonstrating its ability to discriminate short latency differences between its inputs. We then study the sensitivity of this mechanism to fine-tuning of its initial conditions, and show that it is robust to wide range of noise levels in the initial conditions. These results are then generalized to a Hodgkin-Huxley type of neuron model, using numerical simulations. Latency codes have been criticized for requiring a reliable stimulus-onset detection mechanism as a reference for measuring latency. Here we show that this frequent assumption does not hold, and that, an additional onset estimator is not needed to trigger this simple tWTA mechanism.

Rod-cone crossover connectome of mammalian bipolar cells

The basis of cross-suppression between rod and cone channels has long been an enigma. Using rabbit retinal connectome RC1, we show that all cone bipolar cell (BC) classes inhibit rod BCs via amacrine cell (AC) motifs (C1-6); that all cone BC classes are themselves inhibited by AC motifs (R1-5, R25) driven by rod BCs. A sparse symmetric AC motif (CR) is presynaptic and postsynaptic to both rod and cone BCs. ON cone BCs of all classes drive inhibition of rod BCs via motif C1 wide-field GABAergic ACs (γACs) and motif C2 narrow field glycinergic ON ACs (GACs). Each rod BC receives ≈10 crossover AC synapses and each ON cone BC can target ≈10 or more rod BCs via separate AC processes. OFF cone BCs mediate monosynaptic inhibition of rod BCs via motif C3 driven by OFF γACs and GACs and disynaptic inhibition via motifs C4 and C5 driven by OFF wide-field γACs and narrow-field GACs, respectively. Motifs C4 and C5 form halos of 60-100 inhibitory synapses on proximal dendrites of AI γACs. Rod BCs inhibit surrounding arrays of cone BCs through AII GAC networks that access ON and OFF cone BC patches via motifs R1, R2, R4, R5 and a unique ON AC motif R3 that collects rod BC inputs and targets ON cone BCs. Crossover synapses for motifs C1, C4, C5, and R3 are 3-4× larger than typical feedback synapses, which may be a signature for synaptic winner-take-all switches. J. Comp. Neurol. 527:87-116, 2019. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Knowledge About Sounds-Context-Specific Meaning Differently Activates Cortical Hemispheres, Auditory Cortical Fields, and Layers in House Mice

Activation of the auditory cortex (AC) by a given sound pattern is plastic, depending, in largely unknown ways, on the physiological state and the behavioral context of the receiving animal and on the receiver's experience with the sounds. Such plasticity can be inferred when house mouse mothers respond maternally to pup ultrasounds right after parturition and naïve females have to learn to respond. Here we use c-FOS immunocytochemistry to quantify highly activated neurons in the AC fields and layers of seven groups of mothers and naïve females who have different knowledge about and are differently motivated to respond to acoustic models of pup ultrasounds of different behavioral significance. Profiles of FOS-positive cells in the AC primary fields (AI, AAF), the ultrasonic field (UF), the secondary field (AII), and the dorsoposterior field (DP) suggest that activation reflects in AI, AAF, and UF the integration of sound properties with animal state-dependent factors, in the higher-order field AII the news value of a given sound in the behavioral context, and in the higher-order field DP the level of maternal motivation and, by left-hemisphere activation advantage, the recognition of the meaning of sounds in the given context. Anesthesia reduced activation in all fields, especially in cortical layers 2/3. Thus, plasticity in the AC is field-specific preparing different output of AC fields in the process of perception, recognition and responding to communication sounds. Further, the activation profiles of the auditory cortical fields suggest the differentiation between brains hormonally primed to know (mothers) and brains which acquired knowledge via implicit learning (naïve females). In this way, auditory cortical activation discriminates between instinctive (mothers) and learned (naïve females) cognition.

Modulation of human auditory spatial scene analysis by transcranial direct current stimulation

Localizing and selectively attending to the source of a sound of interest in a complex auditory environment is an important capacity of the human auditory system. The underlying neural mechanisms have, however, still not been clarified in detail. This issue was addressed by using bilateral bipolar-balanced transcranial direct current stimulation (tDCS) in combination with a task demanding free-field sound localization in the presence of multiple sound sources, thus providing a realistic simulation of the so-called "cocktail-party" situation. With left-anode/right-cathode, but not with right-anode/left-cathode, montage of bilateral electrodes, tDCS over superior temporal gyrus, including planum temporale and auditory cortices, was found to improve the accuracy of target localization in left hemispace. No effects were found for tDCS over inferior parietal lobule or with off-target active stimulation over somatosensory-motor cortex that was used to control for non-specific effects. Also, the absolute error in localization remained unaffected by tDCS, thus suggesting that general response precision was not modulated by brain polarization. This finding can be explained in the framework of a model assuming that brain polarization modulated the suppression of irrelevant sound sources, thus resulting in more effective spatial separation of the target from the interfering sound in the complex auditory scene.

Dopaminergic impact on local and global cortical circuit processing during learning

We have learned to detect, predict and behaviorally respond to important changes in our environment on short and longer time scales. Therefore, brains of humans and higher animals build upon a perceptual and semantic salience stored in their memories mainly generated by associative reinforcement learning. Functionally, the brain needs to extract and amplify a small number of features of sensory input with behavioral relevance to a particular situation in order to guide behavior. In this review, I argue that dopamine action, particularly in sensory cortex, orchestrates layer-dependent local and long-range cortical circuits integrating sensory associated bottom-up and semantically relevant top-down information, respectively. Available evidence reveals that dopamine thereby controls both the selection of perceptually or semantically salient signals as well as feedback processing from higher-order areas in the brain. Sensory cortical dopamine thereby governs the integration of selected sensory information within a behavioral context. This review proposes that dopamine enfolds this function by temporally distinct actions on particular layer-dependent local and global cortical circuits underlying the integration of sensory, and non-sensory cognitive and behavioral variables.

Computational modelling suggests that temporal integration results from synaptic adaptation in auditory cortex

Incoming sounds are represented in the context of preceding events, and this requires a memory mechanism that integrates information over time. Here, it was demonstrated that response adaptation, the suppression of neural responses due to stimulus repetition, might reflect a computational solution that auditory cortex uses for temporal integration. Adaptation is observed in single-unit measurements as two-tone forward masking effects and as stimulus-specific adaptation (SSA). In non-invasive observations, the amplitude of the auditory N1m response adapts strongly with stimulus repetition, and it is followed by response recovery (the so-called mismatch response) to rare deviant events. The current computational simulations described the serial core-belt-parabelt structure of auditory cortex, and included synaptic adaptation, the short-term, activity-dependent depression of excitatory corticocortical connections. It was found that synaptic adaptation is sufficient for columns to respond selectively to tone pairs and complex tone sequences. These responses were defined as combination sensitive, thus reflecting temporal integration, when a strong response to a stimulus sequence was coupled with weaker responses both to the time-reversed sequence and to the isolated sequence elements. The temporal complexity of the stimulus seemed to be reflected in the proportion of combination-sensitive columns across the different regions of the model. Our results suggest that while synaptic adaptation produces facilitation and suppression effects, including SSA and the modulation of the N1m response, its functional significance may actually be in its contribution to temporal integration. This integration seems to benefit from the serial structure of auditory cortex.

Quantification of mid and late evoked sinks in laminar current source density profiles of columns in the primary auditory cortex

Current source density (CSD) analysis assesses spatiotemporal synaptic activations at somatic and/or dendritic levels in the form of depolarizing current sinks. Whereas many studies have focused on the short (<50 ms) latency sinks, associated with thalamocortical projections, sinks with longer latencies have received less attention. Here, we analyzed laminar CSD patterns for the first 600 ms after stimulus onset in the primary auditory cortex of Mongolian gerbils. By applying an algorithm for contour calculation, three distinct mid and four late evoked sinks were identified in layers I, III, Va, VIa, and VIb. Our results further showed that the patterns of intracortical information-flow remained qualitatively similar for low and for high sound pressure level stimuli at the characteristic frequency (CF) as well as for stimuli ± 1 octave from CF. There were, however, differences associated with the strength, vertical extent, onset latency, and duration of the sinks for the four stimulation paradigms used. Stimuli one octave above the most sensitive frequency evoked a new, and quite reliable, sink in layer Va whereas low level stimulation led to the disappearance of the layer VIb sink. These data indicate the presence of input sources specifically activated in response to level and/or frequency parameters. Furthermore, spectral integration above vs. below the CF of neurons is asymmetric as illustrated by CSD profiles. These results are important because synaptic feedback associated with mid and late sinks—beginning at 50 ms post stimulus latency—is likely crucial for response modulation resulting from higher order processes like memory, learning or cognitive control.

Electrophysiological correlates of cocktail-party listening

Detecting, localizing, and selectively attending to a particular sound source of interest in complex auditory scenes composed of multiple competing sources is a remarkable capacity of the human auditory system. The neural basis of this so-called "cocktail-party effect" has remained largely unknown. Here, we studied the cortical network engaged in solving the "cocktail-party" problem, using event-related potentials (ERPs) in combination with two tasks demanding horizontal localization of a naturalistic target sound presented either in silence or in the presence of multiple competing sound sources. Presentation of multiple sound sources, as compared to single sources, induced an increased P1 amplitude, a reduction in N1, and a strong N2 component, resulting in a pronounced negativity in the ERP difference waveform (N2d) around 260 ms after stimulus onset. About 100 ms later, the anterior contralateral N2 subcomponent (N2ac) occurred in the multiple-sources condition, as computed from the amplitude difference for targets in the left minus right hemispaces. Cortical source analyses of the ERP modulation, resulting from the contrast of multiple vs. single sources, generally revealed an initial enhancement of electrical activity in right temporo-parietal areas, including auditory cortex, by multiple sources (at P1) that is followed by a reduction, with the primary sources shifting from right inferior parietal lobule (at N1) to left dorso-frontal cortex (at N2d). Thus, cocktail-party listening, as compared to single-source localization, appears to be based on a complex chronology of successive electrical activities within a specific cortical network involved in spatial hearing in complex situations.

Layer specific sharpening of frequency tuning by selective attention in primary auditory cortex

Recent electrophysiological and neuroimaging studies provide converging evidence that attending to sounds increases the response selectivity of neuronal ensembles even at the first cortical stage of auditory stimulus processing in primary auditory cortex (A1). This is achieved by enhancement of responses in the regions that process attended frequency content, and by suppression of responses in the surrounding regions. The goals of our study were to define the extent to which A1 neuronal ensembles are involved in this process, determine its effect on the frequency tuning of A1 neuronal ensembles, and examine the involvement of the different cortical layers. To accomplish these, we analyzed laminar profiles of synaptic activity and action potentials recorded in A1 of macaques performing a rhythmic intermodal selective attention task. We found that the frequency tuning of neuronal ensembles was sharpened due to both increased gain at the preferentially processed or best frequency and increased response suppression at all other frequencies when auditory stimuli were attended. Our results suggest that these effects are due to a frequency-specific counterphase entrainment of ongoing delta oscillations, which predictively orchestrates opposite sign excitability changes across all of A1. This results in a net suppressive effect due to the large proportion of neuronal ensembles that do not specifically process the attended frequency content. Furthermore, analysis of laminar activation profiles revealed that although attention-related suppressive effects predominate the responses of supragranular neuronal ensembles, response enhancement is dominant in the granular and infragranular layers, providing evidence for layer-specific cortical operations in attentive stimulus processing.

Unilateral auditory cortex lesions impair or improve discrimination learning of amplitude modulated sounds, depending on lesion side

A fundamental principle of brain organization is bilateral symmetry of structures and functions. For spatial sensory and motor information processing, this organization is generally plausible subserving orientation and coordination of a bilaterally symmetric body. However, breaking of the symmetry principle is often seen for functions that depend on convergent information processing and lateralized output control, e.g. left hemispheric dominance for the linguistic speech system. Conversely, a subtle splitting of functions into hemispheres may occur if peripheral information from symmetric sense organs is partly redundant, e.g. auditory pattern recognition, and therefore allows central conceptualizations of complex stimuli from different feature viewpoints, as demonstrated e.g. for hemispheric analysis of frequency modulations in auditory cortex (AC) of mammals including humans. Here we demonstrate that discrimination learning of rapidly but not of slowly amplitude modulated tones is non-uniformly distributed across both hemispheres: While unilateral ablation of left AC in gerbils leads to impairment of normal discrimination learning of rapid amplitude modulations, right side ablations lead to improvement over normal learning. These results point to a rivalry interaction between both ACs in the intact brain where the right side competes with and weakens learning capability maximally attainable by the dominant left side alone.

The harmonic organization of auditory cortex

A fundamental structure of sounds encountered in the natural environment is the harmonicity. Harmonicity is an essential component of music found in all cultures. It is also a unique feature of vocal communication sounds such as human speech and animal vocalizations. Harmonics in sounds are produced by a variety of acoustic generators and reflectors in the natural environment, including vocal apparatuses of humans and animal species as well as music instruments of many types. We live in an acoustic world full of harmonicity. Given the widespread existence of the harmonicity in many aspects of the hearing environment, it is natural to expect that it be reflected in the evolution and development of the auditory systems of both humans and animals, in particular the auditory cortex. Recent neuroimaging and neurophysiology experiments have identified regions of non-primary auditory cortex in humans and non-human primates that have selective responses to harmonic pitches. Accumulating evidence has also shown that neurons in many regions of the auditory cortex exhibit characteristic responses to harmonically related frequencies beyond the range of pitch. Together, these findings suggest that a fundamental organizational principle of auditory cortex is based on the harmonicity. Such an organization likely plays an important role in music processing by the brain. It may also form the basis of the preference for particular classes of music and voice sounds.

Mapping mammalian synaptic connectivity

Mapping mammalian synaptic connectivity has long been an important goal of neuroscientists since it is considered crucial for explaining human perception and behavior. Yet, despite enormous efforts, the overwhelming complexity of the neural circuitry and the lack of appropriate techniques to unravel it have limited the success of efforts to map connectivity. However, recent technological advances designed to overcome the limitations of conventional methods for connectivity mapping may bring about a turning point. Here, we address the promises and pitfalls of these new mapping technologies.

Temporal binding of sound emerges out of anatomical structure and synaptic dynamics of auditory cortex

The ability to represent and recognize naturally occuring sounds such as speech depends not only on spectral analysis carried out by the subcortical auditory system but also on the ability of the cortex to bind spectral information over time. In primates, these temporal binding processes are mirrored as selective responsiveness of neurons to species-specific vocalizations. Here, we used computational modeling of auditory cortex to investigate how selectivity to spectrally and temporally complex stimuli is achieved. A set of 208 microcolumns were arranged in a serial core-belt-parabelt structure documented in both humans and animals. Stimulus material comprised multiple consonant-vowel (CV) pseudowords. Selectivity to the spectral structure of the sounds was commonly found in all regions of the model (N = 122 columns out of 208), and this selectivity was only weakly affected by manipulating the structure and dynamics of the model. In contrast, temporal binding was rarer (N = 39), found mostly in the belt and parabelt regions. Thus, the serial core-belt-parabelt structure of auditory cortex is necessary for temporal binding. Further, adaptation due to synaptic depression-rendering the cortical network malleable by stimulus history-was crucial for the emergence of neurons sensitive to the temporal structure of the stimuli. Both spectral selectivity and temporal binding required that a sufficient proportion of the columns interacted in an inhibitory manner. The model and its structural modifications had a small-world structure (i.e., columns formed clusters and were within short node-to-node distances from each other). However, simulations showed that a small-world structure is not a necessary condition for spectral selectivity and temporal binding to emerge. In summary, this study suggests that temporal binding arises out of (1) the serial structure typical to the auditory cortex, (2) synaptic adaptation, and (3) inhibitory interactions between microcolumns.

Asymmetric multisensory interactions of visual and somatosensory responses in a region of the rat parietal cortex

Perception greatly benefits from integrating multiple sensory cues into a unified percept. To study the neural mechanisms of sensory integration, model systems are required that allow the simultaneous assessment of activity and the use of techniques to affect individual neural processes in behaving animals. While rodents qualify for these requirements, little is known about multisensory integration and areas involved for this purpose in the rodent. Using optical imaging combined with laminar electrophysiological recordings, the rat parietal cortex was identified as an area where visual and somatosensory inputs converge and interact. Our results reveal similar response patterns to visual and somatosensory stimuli at the level of current source density (CSD) responses and multi-unit responses within a strip in parietal cortex. Surprisingly, a selective asymmetry was observed in multisensory interactions: when the somatosensory response preceded the visual response, supra-linear summation of CSD was observed, but the reverse stimulus order resulted in sub-linear effects in the CSD. This asymmetry was not present in multi-unit activity however, which showed consistently sub-linear interactions. These interactions were restricted to a specific temporal window, and pharmacological tests revealed significant local intra-cortical contributions to this phenomenon. Our results highlight the rodent parietal cortex as a system to model the neural underpinnings of multisensory processing in behaving animals and at the cellular level.

Selective positive-negative feedback produces the winner-take-all competition in recurrent neural networks

The winner-take-all (WTA) competition is widely observed in both inanimate and biological media and society. Many mathematical models are proposed to describe the phenomena discovered in different fields. These models are capable of demonstrating the WTA competition. However, they are often very complicated due to the compromise with experimental realities in the particular fields; it is often difficult to explain the underlying mechanism of such a competition from the perspective of feedback based on those sophisticate models. In this paper, we make steps in that direction and present a simple model, which produces the WTA competition by taking advantage of selective positive-negative feedback through the interaction of neurons via p-norm. Compared to existing models, this model has an explicit explanation of the competition mechanism. The ultimate convergence behavior of this model is proven analytically. The convergence rate is discussed and simulations are conducted in both static and dynamic competition scenarios. Both theoretical and numerical results validate the effectiveness of the dynamic equation in describing the nonlinear phenomena of WTA competition.

Discrete neocortical dynamics predict behavioral categorization of sounds

The ability to group stimuli into perceptual categories is essential for efficient interaction with the environment. Discrete dynamics that emerge in brain networks are believed to be the neuronal correlate of category formation. Observations of such dynamics have recently been made; however, it is still unresolved if they actually match perceptual categories. Using in vivo two-photon calcium imaging in the auditory cortex of mice, we show that local network activity evoked by sounds is constrained to few response modes. Transitions between response modes are characterized by an abrupt switch, indicating attractor-like, discrete dynamics. Moreover, we show that local cortical responses quantitatively predict discrimination performance and spontaneous categorization of sounds in behaving mice. Our results therefore demonstrate that local nonlinear dynamics in the auditory cortex generate spontaneous sound categories which can be selected for behavioral or perceptual decisions.

Predisposition for and prevention of subjective tinnitus development

Dysfunction of the inner ear as caused by presbyacusis, injuries or noise traumata may result in subjective tinnitus, but not everyone suffering from one of these diseases develops a tinnitus percept and vice versa. The reasons for these individual differences are still unclear and may explain why different treatments of the disease are beneficial for some patients but not for others. Here we for the first time compare behavioral and neurophysiological data from hearing impaired Mongolian gerbils with (T) and without (NT) a tinnitus percept that may elucidate why some specimen do develop subjective tinnitus after noise trauma while others do not. Although noise trauma induced a similar permanent hearing loss in all animals, tinnitus did develop only in about three quarters of these animals. NT animals showed higher overall cortical and auditory brainstem activity before noise trauma compared to T animals; that is, animals with low overall neuronal activity in the auditory system seem to be prone to develop tinnitus after noise trauma. Furthermore, T animals showed increased activity of cortical neurons representing the tinnitus frequencies after acoustic trauma, whereas NT animals exhibited an activity decrease at moderate sound intensities by that time. Spontaneous activity was generally increased in T but decreased in NT animals. Plastic changes of tonotopic organization were transient, only seen in T animals and vanished by the time the tinnitus percept became chronic. We propose a model for tinnitus prevention that points to a global inhibitory mechanism in auditory cortex that may prevent tinnitus genesis in animals with high overall activity in the auditory system, whereas this mechanism seems not potent enough for tinnitus prevention in animals with low overall activity.

Dual mechanism of neuronal ensemble inhibition in primary auditory cortex

Inhibition plays an essential role in shaping and refining the brain's representation of sensory stimulus attributes. In primary auditory cortex (A1), so-called "sideband" inhibition helps to sharpen the tuning of local neuronal responses. Several distinct types of anatomical circuitry could underlie sideband inhibition, including direct thalamocortical (TC) afferents, as well as indirect intracortical mechanisms. The goal of the present study was to characterize sideband inhibition in A1 and to determine its mechanism by analyzing laminar profiles of neuronal ensemble activity. Our results indicate that both lemniscal and nonlemniscal TC afferents play a role in inhibitory responses via feedforward inhibition and oscillatory phase reset, respectively. We propose that the dynamic modulation of excitability in A1 due to the phase reset of ongoing oscillations may alter the tuning of local neuronal ensembles and can be regarded as a flexible overlay on the more obligatory system of lemniscal feedforward type responses.

Collective stability of networks of winner-take-all circuits

The neocortex has a remarkably uniform neuronal organization, suggesting that common principles of processing are employed throughout its extent. In particular, the patterns of connectivity observed in the superficial layers of the visual cortex are consistent with the recurrent excitation and inhibitory feedback required for cooperative-competitive circuits such as the soft winner-take-all (WTA). WTA circuits offer interesting computational properties such as selective amplification, signal restoration, and decision making. But these properties depend on the signal gain derived from positive feedback, and so there is a critical trade-off between providing feedback strong enough to support the sophisticated computations while maintaining overall circuit stability. The issue of stability is all the more intriguing when one considers that the WTAs are expected to be densely distributed through the superficial layers and that they are at least partially interconnected. We consider how to reason about stability in very large distributed networks of such circuits. We approach this problem by approximating the regular cortical architecture as many interconnected cooperative-competitive modules. We demonstrate that by properly understanding the behavior of this small computational module, one can reason over the stability and convergence of very large networks composed of these modules. We obtain parameter ranges in which the WTA circuit operates in a high-gain regime, is stable, and can be aggregated arbitrarily to form large, stable networks. We use nonlinear contraction theory to establish conditions for stability in the fully nonlinear case and verify these solutions using numerical simulations. The derived bounds allow modes of operation in which the WTA network is multistable and exhibits state-dependent persistent activities. Our approach is sufficiently general to reason systematically about the stability of any network, biological or technological, composed of networks of small modules that express competition through shared inhibition.

Spectral integration in primary auditory cortex attributable to temporally precise convergence of thalamocortical and intracortical input

Primary sensory cortex integrates sensory information from afferent feedforward thalamocortical projection systems and convergent intracortical microcircuits. Both input systems have been demonstrated to provide different aspects of sensory information. Here we have used high-density recordings of laminar current source density (CSD) distributions in primary auditory cortex of Mongolian gerbils in combination with pharmacological silencing of cortical activity and analysis of the residual CSD, to dissociate the feedforward thalamocortical contribution and the intracortical contribution to spectral integration. We found a temporally highly precise integration of both types of inputs when the stimulation frequency was in close spectral neighborhood of the best frequency of the measurement site, in which the overlap between both inputs is maximal. Local intracortical connections provide both directly feedforward excitatory and modulatory input from adjacent cortical sites, which determine how concurrent afferent inputs are integrated. Through separate excitatory horizontal projections, terminating in cortical layers II/III, information about stimulus energy in greater spectral distance is provided even over long cortical distances. These projections effectively broaden spectral tuning width. Based on these data, we suggest a mechanism of spectral integration in primary auditory cortex that is based on temporally precise interactions of afferent thalamocortical inputs and different short- and long-range intracortical networks. The proposed conceptual framework allows integration of different and partly controversial anatomical and physiological models of spectral integration in the literature.

Long-range effects of GABAergic inhibition in gerbil primary auditory cortex

Throughout the literature, the effects of iontophoretically applied neurotransmitter agonists or antagonists on the local activity of neurons are typically studied at the site of drug application. Recently, we have demonstrated long-range inhibitory interactions within the primary auditory cortex (AI) that are effective in complex acoustic situations. To further characterize this long-range functional connectivity, we here report the effects of the inhibitory neurotransmitter gamma-aminobutyric acid (GABA) and the GABA(A) antagonist gabazine (SR 95531) on neuronal activity as a function of distance from the application site reaching beyond the diffusion radius of the applied drug. Neuronal responses to pure tone stimulation were simultaneously recorded at the application site and four additional sites, at distances between 300 and 1350 microm from the application site. We found that whereas application of GABA during best frequency (BF) stimulation in general led to a decrease, and gabazine to an increase, in neuronal activity at the application site, a considerable number of units at remote recording sites showed effects opposite to these local, drug-induced effects. These effects were seen both in spiking activity and in amplitudes of local field potentials. At all locations, the effects varied as a function of pure tone stimulation frequency, pointing to a Mexican-hat-like input function resulting from thalamic inputs to the BF region of the cortical neurons and intracortical interconnections projecting to off-BF regions of the neurons. These data demonstrate the existence of long-range, inhibitory interactions within the gerbil AI, realized either by long-range inhibitory projections or by long-range excitatory projections to local inhibitory interneurons.

State-dependent computation using coupled recurrent networks

Although conditional branching between possible behavioral states is a hallmark of intelligent behavior, very little is known about the neuronal mechanisms that support this processing. In a step toward solving this problem, we demonstrate by theoretical analysis and simulation how networks of richly interconnected neurons, such as those observed in the superficial layers of the neocortex, can embed reliable, robust finite state machines. We show how a multistable neuronal network containing a number of states can be created very simply by coupling two recurrent networks whose synaptic weights have been configured for soft winner-take-all (sWTA) performance. These two sWTAs have simple, homogeneous, locally recurrent connectivity except for a small fraction of recurrent cross-connections between them, which are used to embed the required states. This coupling between the maps allows the network to continue to express the current state even after the input that elicited that state is withdrawn. In addition, a small number of transition neurons implement the necessary input-driven transitions between the embedded states. We provide simple rules to systematically design and construct neuronal state machines of this kind. The significance of our finding is that it offers a method whereby the cortex could construct networks supporting a broad range of sophisticated processing by applying only small specializations to the same generic neuronal circuit.

Frequency-specific modulation of population-level frequency tuning in human auditory cortex

Background

Under natural circumstances, attention plays an important role in extracting relevant auditory signals from simultaneously present, irrelevant noises. Excitatory and inhibitory neural activity, enhanced by attentional processes, seems to sharpen frequency tuning, contributing to improved auditory performance especially in noisy environments. In the present study, we investigated auditory magnetic fields in humans that were evoked by pure tones embedded in band-eliminated noises during two different stimulus sequencing conditions (constant vs. random) under auditory focused attention by means of magnetoencephalography (MEG).

Results

In total, we used identical auditory stimuli between conditions, but presented them in a different order, thereby manipulating the neural processing and the auditory performance of the listeners. Constant stimulus sequencing blocks were characterized by the simultaneous presentation of pure tones of identical frequency with band-eliminated noises, whereas random sequencing blocks were characterized by the simultaneous presentation of pure tones of random frequencies and band-eliminated noises. We demonstrated that auditory evoked neural responses were larger in the constant sequencing compared to the random sequencing condition, particularly when the simultaneously presented noises contained narrow stop-bands.

Conclusion

The present study confirmed that population-level frequency tuning in human auditory cortex can be sharpened in a frequency-specific manner. This frequency-specific sharpening may contribute to improved auditory performance during detection and processing of relevant sound inputs characterized by specific frequency distributions in noisy environments.