Network architecture, receptive fields, and neuromodulation: computational and functional implications of cholinergic modulation in primary auditory cortex

AIM: A network model of attention in auditory cortex

Attentional modulation of cortical networks is critical for the cognitive flexibility required to process complex scenes. Current theoretical frameworks for attention are based almost exclusively on studies in visual cortex, where attentional effects are typically modest and excitatory. In contrast, attentional effects in auditory cortex can be large and suppressive. A theoretical framework for explaining attentional effects in auditory cortex is lacking, preventing a broader understanding of cortical mechanisms underlying attention. Here, we present a cortical network model of attention in primary auditory cortex (A1). A key mechanism in our network is attentional inhibitory modulation (AIM) of cortical inhibitory neurons. In this mechanism, top-down inhibitory neurons disinhibit bottom-up cortical circuits, a prominent circuit motif observed in sensory cortex. Our results reveal that the same underlying mechanisms in the AIM network can explain diverse attentional effects on both spatial and frequency tuning in A1. We find that a dominant effect of disinhibition on cortical tuning is suppressive, consistent with experimental observations. Functionally, the AIM network may play a key role in solving the cocktail party problem. We demonstrate how attention can guide the AIM network to monitor an acoustic scene, select a specific target, or switch to a different target, providing flexible outputs for solving the cocktail party problem.

Selective attention plays a key role in how we navigate our everyday lives. For example, at a cocktail party, we can attend to friend’s speech amidst other speakers, music, and background noise. In stark contrast, hundreds of millions of people with hearing impairment and other disorders find such environments overwhelming and debilitating. Understanding the mechanisms underlying selective attention may lead to breakthroughs in improving the quality of life for those negatively affected. Here, we propose a mechanistic network model of attention in primary auditory cortex based on attentional inhibitory modulation (AIM). In the AIM model, attention targets specific cortical inhibitory neurons, which then modulate local cortical circuits to emphasize a particular feature of sounds and suppress competing features. We show that the AIM model can account for experimental observations across different species and stimulus domains. We also demonstrate that the same mechanisms can enable listeners to flexibly switch between attending to specific targets sounds and monitoring the environment in complex acoustic scenes, such as a cocktail party. The AIM network provides a theoretical framework which can work in tandem with new experiments to help unravel cortical circuits underlying attention.

Cellular and Network Mechanisms for Temporal Signal Propagation in a Cortical Network Model

The mechanisms underlying an effective propagation of high intensity information over a background of irregular firing and response latency in cognitive processes remain unclear. Here we propose a SSCCPI circuit to address this issue. We hypothesize that when a high-intensity thalamic input triggers synchronous spike events (SSEs), dense spikes are scattered to many receiving neurons within a cortical column in layer IV, many sparse spike trains are propagated in parallel along minicolumns at a substantially high speed and finally integrated into an output spike train toward or in layer Va. We derive the sufficient conditions for an effective (fast, reliable, and precise) SSCCPI circuit: (i) SSEs are asynchronous (near synchronous); (ii) cortical columns prevent both repeatedly triggering SSEs and incorrectly synaptic connections between adjacent columns; and (iii) the propagator in interneurons is temporally complete fidelity and reliable. We encode the membrane potential responses to stimuli using the non-linear autoregressive integrated process derived by applying Newton's second law to stochastic resilience systems. We introduce a multithreshold decoder to correct encoding errors. Evidence supporting an effective SSCCPI circuit includes that for the condition, (i) time delay enhances SSEs, suggesting that response latency induces SSEs in high-intensity stimuli; irregular firing causes asynchronous SSEs; asynchronous SSEs relate to healthy neurons; and rigorous SSEs relate to brain disorders. For the condition (ii) neurons within a given minicolumn are stereotypically interconnected in the vertical dimension, which prevents repeated triggering SSEs and ensures signal parallel propagation; columnar segregation avoids incorrect synaptic connections between adjacent columns; and signal propagation across layers overwhelmingly prefers columnar direction. For the condition (iii), accumulating experimental evidence supports temporal transfer precision with millisecond fidelity and reliability in interneurons; homeostasis supports a stable fixed-point encoder by regulating changes to synaptic size, synaptic strength, and ion channel function in the membrane; together all-or-none modulation, active backpropagation, additive effects of graded potentials, and response variability functionally support the multithreshold decoder; our simulations demonstrate that the encoder-decoder is temporally complete fidelity and reliable in special intervals contained within the stable fixed-point range. Hence, the SSCCPI circuit provides a possible mechanism of effective signal propagation in cortical networks.

Computational Neural Modeling of Auditory Cortical Receptive Fields

Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive between cortical neurons and/or bottom-up synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of behaviorally salient sounds.

Modulation of auditory deviance detection by acute nicotine is baseline and deviant dependent in healthy nonsmokers: a mismatch negativity study

OBJECTIVE

Cognitive enhancement resulting from nicotinic acetylcholine receptor stimulation may be evidenced by increased efficiency of the auditory-frontal cortex network of auditory discrimination, which is impaired in schizophrenia, a cognitive disorder associated with excessive tobacco use. Investigating automatic (preattentive) detection of acoustic change with the mismatch negativity (MMN) brain event-related potential in response to nicotine in individuals with varying baseline levels of auditory discrimination may provide useful insight into the cholinergic regulation of this neural network and its potential amelioration with novel nicotinic agents.

METHODS

Sixty healthy, non-smoking male volunteers were presented with an 'optimal' multi-feature MMN paradigm in a randomized, placebo controlled double-blind design with 6 mg of nicotine gum.

RESULTS

Participants with low, medium, and high baseline amplitudes responded differently to nicotine (vs. placebo), and nicotine response was feature specific. Whereas MMN in individuals with high amplitudes was diminished by nicotine, MMN increased in those with low amplitudes. Nicotine effects were not shown in medium amplitude participants.

CONCLUSIONS

These findings provide preliminary support for the role of nicotinic neurotransmission in sensory memory processing of auditory change and suggest that nicotinic receptor modulation can both enhance and diminish change detection, depending on baseline MMN and its eliciting stimulus feature.

Nicotine, Auditory Sensory Memory, and sustained Attention in a Human Ketamine Model of Schizophrenia: Moderating Influence of a Hallucinatory Trait

BACKGROUND

The procognitive actions of the nicotinic acetylcholine receptor (nAChR) agonist nicotine are believed, in part, to motivate the excessive cigarette smoking in schizophrenia, a disorder associated with deficits in multiple cognitive domains, including low-level auditory sensory processes and higher-order attention-dependent operations.

OBJECTIVES

As N-methyl-d-aspartate receptor (NMDAR) hypofunction has been shown to contribute to these cognitive impairments, the primary aims of this healthy volunteer study were to: (a) to shed light on the separate and interactive roles of nAChR and NMDAR systems in the modulation of auditory sensory memory (and sustained attention), as indexed by the auditory event-related brain potential - mismatch negativity (MMN), and (b) to examine how these effects are moderated by a predisposition to auditory hallucinations/delusions (HD).

METHODS

In a randomized, double-blind, placebo-controlled design involving a low intravenous dose of ketamine (0.04 mg/kg) and a 4 mg dose of nicotine gum, MMN, and performance on a rapid visual information processing (RVIP) task of sustained attention were examined in 24 healthy controls psychometrically stratified as being lower (L-HD, n = 12) or higher (H-HD) for HD propensity.

RESULTS

Ketamine significantly slowed MMN, and reduced MMN in H-HD, with amplitude attenuation being blocked by the co-administration of nicotine. Nicotine significantly enhanced response speed [reaction time (RT)] and accuracy (increased % hits and d' and reduced false alarms) on the RVIP, with improved performance accuracy being prevented when nicotine was administered with ketamine. Both % hits and d', as well as RT were poorer in H-HD (vs. L-HD) and while hit rate and d' was increased by nicotine in H-HD, RT was slowed by ketamine in L-HD.

CONCLUSIONS

Nicotine alleviated ketamine-induced sensory memory impairment and improved attention, particularly in individuals prone to HD.

Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM). Theorems in the 1970's showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP) currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh) can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners when the network stabilizes.

After-hyperpolarization currents and acetylcholine control sigmoid transfer functions in a spiking cortical model

Recurrent networks are ubiquitous in the brain, where they enable a diverse set of transformations during perception, cognition, emotion, and action. It has been known since the 1970's how, in rate-based recurrent on-center off-surround networks, the choice of feedback signal function can control the transformation of input patterns into activity patterns that are stored in short term memory. A sigmoid signal function may, in particular, control a quenching threshold below which inputs are suppressed as noise and above which they may be contrast enhanced before the resulting activity pattern is stored. The threshold and slope of the sigmoid signal function determine the degree of noise suppression and of contrast enhancement. This article analyses how sigmoid signal functions and their shape may be determined in biophysically realistic spiking neurons. Combinations of fast, medium, and slow after-hyperpolarization (AHP) currents, and their modulation by acetylcholine (ACh), can control sigmoid signal threshold and slope. Instead of a simple gain in excitability that was previously attributed to ACh, cholinergic modulation may cause translation of the sigmoid threshold. This property clarifies how activation of ACh by basal forebrain circuits, notably the nucleus basalis of Meynert, may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract information, as predicted by Adaptive Resonance Theory.

Cortical gamma rhythms modulate NMDAR-mediated spike timing dependent plasticity in a biophysical model

Spike timing dependent plasticity (STDP) has been observed experimentally in vitro and is a widely studied neural algorithm for synaptic modification. While the functional role of STDP has been investigated extensively, the effect of rhythms on the precise timing of STDP has not been characterized as well. We use a simplified biophysical model of a cortical network that generates pyramidal interneuronal gamma rhythms (PING). Plasticity via STDP is investigated at the excitatory pyramidal cell synapse from a gamma frequency (30–90 Hz) input independent of the network gamma rhythm. The input may represent a corticocortical or an information-specific thalamocortical connection. This synapse is mediated by N-methyl-D-aspartate receptor mediated (NMDAR) currents. For distinct network and input frequencies, the model shows robust frequency regimes of potentiation and depression, providing a mechanism by which responses to certain inputs can potentiate while responses to other inputs depress. For potentiating regimes, the model suggests an optimal amount and duration of plasticity that can occur, which depends on the time course for the decay of the postsynaptic NMDAR current. Prolonging the duration of the input beyond this optimal time results in depression. Inserting pauses in the input can increase the total potentiation. The optimal pause length corresponds to the decay time of the NMDAR current. Thus, STDP in this model provides a mechanism for potentiation and depression depending on input frequency and suggests that the slow NMDAR current decay helps to regulate the optimal amplitude and duration of the plasticity. The optimal pause length is comparable to the time scale of the negative phase of a modulatory theta rhythm, which may pause gamma rhythm spiking. Our pause results may suggest a novel role for this theta rhythm in plasticity. Finally, we discuss our results in the context of auditory thalamocortical plasticity.

Rhythms are well studied phenomena in many animal species. Brain rhythms in the gamma frequency range (30–90 Hz) are thought to play a role in attention and memory. In this paper, we are interested in how cortical gamma rhythms interact with information specific inputs that also have a significant gamma frequency component. The results from our computational model show that plasticity associated with learning depends on the specific frequencies of the input and cortical gamma rhythms. The results show a mechanism by which both increases and decreases in the strength of the input connection can occur, depending on the specific frequency of the input. A current mediated by NMDA receptors may be responsible for the temporal course of the plasticity seen in these brain regions. We discuss the implications of our results for conditioning paradigms applied to auditory learning.

A simple model of cortical dynamics explains variability and state dependence of sensory responses in urethane-anesthetized auditory cortex

The responses of neocortical cells to sensory stimuli are variable and state dependent. It has been hypothesized that intrinsic cortical dynamics play an important role in trial-to-trial variability; the precise nature of this dependence, however, is poorly understood. We show here that in auditory cortex of urethane-anesthetized rats, population responses to click stimuli can be quantitatively predicted on a trial-by-trial basis by a simple dynamical system model estimated from spontaneous activity immediately preceding stimulus presentation. Changes in cortical state correspond consistently to changes in model dynamics, reflecting a nonlinear, self-exciting system in synchronized states and an approximately linear system in desynchronized states. We propose that the complex and state-dependent pattern of trial-to-trial variability can be explained by a simple principle: sensory responses are shaped by the same intrinsic dynamics that govern ongoing spontaneous activity.

A computational model of rapid task-related plasticity of auditory cortical receptive fields

Receptive field properties of neurons in A1 can rapidly adapt their shapes during task performance in accord with specific task demands and salient sensory cues (Fritz et al., Hearing Research, 206:159-176, 2005a, Nature Neuroscience, 6: 1216-1223, 2003). Such modulatory changes selectively enhance overall cortical responsiveness to target (foreground) sounds and thus increase the likelihood of detection against the background of reference sounds. In this study, we develop a mathematical model to describe how enhancing discrimination between two arbitrary classes of sounds can lead to the observed receptive field changes in a variety of spectral and temporal discrimination tasks. Cortical receptive fields are modeled as filters that change their spectro-temporal tuning properties so as to respond best to the discriminatory acoustic features between foreground and background stimuli. We also illustrate how biologically plausible constraints on the spectro-temporal tuning of the receptive fields can be used to optimize the plasticity. Results of the model simulations are compared to published data from a variety of experimental paradigms.

Balanced tone-evoked synaptic excitation and inhibition in mouse auditory cortex

The recent characterization of excitatory and inhibitory synaptic receptive fields in rat auditory cortex laid the basis for further investigation of the roles of synaptic excitation and inhibition in cortical computation and plasticity. The mouse is an increasingly important model system because of the wide range of genetic tools available for it. Here we present the first in vivo whole-cell voltage-clamp measurements of synaptic excitation and inhibition in the mouse cortex. We find that a substantial population of auditory cortical neurons receives balanced synaptic excitation and inhibition, whose amplitude ratios and relative time courses remain approximately constant across tone frequency. We conclude that the synaptic mechanisms underlying tone-evoked auditory cortical responses in mice closely resemble those in rats, supporting the mouse as a suitable model for synaptic processing in auditory cortex.

Linking the response properties of cells in auditory cortex with network architecture: cotuning versus lateral inhibition

The frequency-intensity receptive fields (RF) of neurons in primary auditory cortex (AI) are heterogeneous. Some neurons have V-shaped RFs, whereas others have enclosed ovoid RFs. Moreover, there is a wide range of temporal response profiles ranging from phasic to tonic firing. The mechanisms underlying this diversity of receptive field properties are yet unknown. Here we study the characteristics of thalamocortical (TC) and intracortical connectivity that give rise to the individual cell responses. Using a mouse auditory TC slice preparation, we found that the amplitude of synaptic responses in AI varies non-monotonically with the intensity of the stimulation in the medial geniculate nucleus (MGv). We constructed a network model of MGv and AI that was simulated using either rate model cells or in vitro neurons through an iterative procedure that used the recorded neural responses to reconstruct network activity. We compared the receptive fields and firing profiles obtained with networks configured to have either cotuned excitatory and inhibitory inputs or relatively broad, lateral inhibitory inputs. Each of these networks yielded distinct response properties consistent with those documented in vivo with natural stimuli. The cotuned network produced V-shaped RFs, phasic-tonic firing profiles, and predominantly monotonic rate-level functions. The lateral inhibitory network produced enclosed RFs with narrow frequency tuning, a variety of firing profiles, and robust non-monotonic rate-level functions. We conclude that both types of circuits must be present to account for the wide variety of responses observed in vivo.

Nicotinic modulation of tone-evoked responses in auditory cortex reflects the strength of prior auditory learning

Nicotinic acetylcholine receptors (nAChRs) contribute to sensory-cognitive function, as demonstrated by evidence that nAChR activation enhances, and nAChR blockade impairs, neural processing of sensory stimuli and sensory-cognitive behavior. To better understand the relationship between nAChR function and behavior, here we compare the strength of nAChR-mediated physiology in individual animals to their prior auditory behavioral performance. Adult rats were trained on an auditory-cued, active avoidance task over 4 days and classified as "good," "intermediate" or "poor" performers based on their initial rate of learning and eventual level of performance. Animals were then anesthetized, and tone-evoked local field potentials (LFPs) recorded in layer 4 of auditory cortex (ACx) before and after a test dose of nicotine (0.7mg/kg, s.c.) or saline. In "good" performers, nicotine enhanced LFP amplitude and decreased response threshold to characteristic frequency (CF) stimuli, yet had opposite effects (decreased amplitude, increased threshold) on responses to spectrally distant stimuli; i.e., cortical receptive fields became more selective for CF stimuli. In contrast, nicotine had little effect on LFP amplitude in "intermediate" or "poor" performing animals. Nicotine did, however, reduce LFP onset latency in all three groups, indicating that all received an effective dose of the drug. Our findings suggest that nicotinic regulation of cortical receptive fields may be a distinguishing feature of the best-performing animals, and may facilitate sensory-related learning by enhancing receptive field selectivity.

Processing of sounds by population spikes in a model of primary auditory cortex

We propose a model of the primary auditory cortex (A1), in which each iso-frequency column is represented by a recurrent neural network with short-term synaptic depression. Such networks can emit Population Spikes, in which most of the neurons fire synchronously for a short time period. Different columns are interconnected in a way that reflects the tonotopic map in A1, and population spikes can propagate along the map from one column to the next, in a temporally precise manner that depends on the specific input presented to the network. The network, therefore, processes incoming sounds by precise sequences of population spikes that are embedded in a continuous asynchronous activity, with both of these response components carrying information about the inputs and interacting with each other. With these basic characteristics, the model can account for a wide range of experimental findings. We reproduce neuronal frequency tuning curves, whose width depends on the strength of the intracortical inhibitory and excitatory connections. Non-simultaneous two-tone stimuli show forward masking depending on their temporal separation, as well as on the duration of the first stimulus. The model also exhibits non-linear suppressive interactions between sub-threshold tones and broad-band noise inputs, similar to the hypersensitive locking suppression recently demonstrated in auditory cortex. We derive several predictions from the model. In particular, we predict that spontaneous activity in primary auditory cortex gates the temporally locked responses of A1 neurons to auditory stimuli. Spontaneous activity could, therefore, be a mechanism for rapid and reversible modulation of cortical processing.

Adaptive changes in cortical receptive fields induced by attention to complex sounds

Receptive fields in primary auditory cortex (A1) can be rapidly and adaptively reshaped to enhance responses to salient frequency cues when using single tones as targets. To explore receptive field changes to more complex spectral patterns, we trained ferrets to detect variable, multitone targets in the context of background, rippled noise. Recordings from A1 of behaving ferrets showed a consistent pattern of plasticity, at both the single-neuron level and the population level, with enhancement for each component tone frequency and suppression for intertone frequencies. Plasticity was strongest near neuronal best frequency, rapid in onset, and slow to fade. Although attention may trigger cortical plasticity, the receptive field changes persisted after the behavioral task was completed. The observed comb filter plasticity is an example of an adaptive contrast matched filter, which may generally improve discriminability between foreground and background sounds and, we conjecture, may predict A1 cortical plasticity for any complex spectral target.