Synaptic short-term plasticity in auditory cortical circuits

Shift in excitation-inhibition balance underlies perceptual learning of temporal discrimination

Temporal perceptual learning (TPL) constitutes a unique and profound demonstration of neural plasticity within the brain. Our understanding for the neurometabolic changes associated with TPL on the other hand has been limited in part by the use of traditional fMRI approaches. Since plasticity in the visual cortex has been shown to underlie perceptual learning of visual information, we tested the hypothesis that TPL of an auditory interval involves a similar change in plasticity of the auditory pathway and if so, whether these changes take place in a lower-order sensory-specific brain area such as the primary auditory cortex (A1), or a higher-order modality-independent brain area such as the inferior parietal cortex (IPC). This distinction will inform us of the mechanisms underlying perceptual learning as well as the locus of change as it relates to TPL. In the present study, we took advantage of a new technique: proton magnetic resonance spectroscopy (MRS) in combination with psychophysical measures to provide the first evidence of changes in neurometabolic processing following 5 days of temporal discrimination training. We measured the (E)xcitation-to-(I)nhibition ratio as an index of learning in the right IPC and left A1 while participants learned an auditory two-tone discrimination task. During the first day of training, we found a significant task-related increase in functional E/I ratio within the IPC. While the A1 exhibited the opposite pattern of neurochemical activity, this relationship did not reach statistical significance. After timing performance has reached a plateau, there were no further changes to functional E/I. These findings support the hypothesis that improvements in temporal discrimination relies on neuroplastic changes in the IPC, but it is possible that both areas work synergistically to acquire a temporal interval.

Repetition plasticity in primary auditory cortex occurs across long timescales for spectrotemporally randomized pure-tones

Repetition plasticity is a ubiquitous property of sensory systems in which repetitive sensation causes either a decrease ("repetition suppression", i.e. "adaptation") or increase ("repetition enhancement", i.e. "facilitation") in the amplitude of neural responses. Timescales of repetition plasticity for sensory neurons typically span milliseconds to tens of seconds, with longer durations for cortical vs subcortical regions. Here, we used 2-photon (2P) imaging to study repetition plasticity in mouse primary auditory cortex (A1) layer 2/3 (L2/3) during the presentation of spectrotemporally randomized pure-tone frequencies. Our study revealed subpopulations of neurons with repetition plasticity for equiprobable frequencies spaced minutes apart over a 20-minute period. We found both repetition suppression and enhancement in individual neurons and on average across populations. Each neuron tended to show repetition plasticity for 1-2 pure-tone frequencies near the neuron's best frequency. Moreover, we found correlated changes in neural response amplitude and latency across stimulus repetitions. Together, our results highlight cortical specialization for pattern recognition over long timescales in complex acoustic sequences.

Recurrent activity in neuronal avalanches

A new statistical analysis of large neuronal avalanches observed in mouse and rat brain tissues reveals a substantial degree of recurrent activity and cyclic patterns of activation not seen in smaller avalanches. To explain these observations, we adapted a model of structural weakening in materials. In this model, dynamical weakening of neuron firing thresholds closely replicates experimental avalanche size distributions, firing number distributions, and patterns of cyclic activity. This agreement between model and data suggests that a mechanism like dynamical weakening plays a key role in recurrent activity found in large neuronal avalanches. We expect these results to illuminate the causes and dynamics of large avalanches, like those seen in seizures.

Developmental depression-to-facilitation shift controls excitation-inhibition balance

Changes in the short-term dynamics of excitatory synapses over development have been observed throughout cortex, but their purpose and consequences remain unclear. Here, we propose that developmental changes in synaptic dynamics buffer the effect of slow inhibitory long-term plasticity, allowing for continuously stable neural activity. Using computational modeling we demonstrate that early in development excitatory short-term depression quickly stabilises neural activity, even in the face of strong, unbalanced excitation. We introduce a model of the commonly observed developmental shift from depression to facilitation and show that neural activity remains stable throughout development, while inhibitory synaptic plasticity slowly balances excitation, consistent with experimental observations. Our model predicts changes in the input responses from phasic to phasic-and-tonic and more precise spike timings. We also observe a gradual emergence of short-lasting memory traces governed by short-term plasticity development. We conclude that the developmental depression-to-facilitation shift may control excitation-inhibition balance throughout development with important functional consequences.

Using computational modelling this study proposes that the commonly observed depression-to-facilitation shift across development controls excitation-inhibition balance in the brain.

Mathematical framework for place coding in the auditory system

In the auditory system, tonotopy is postulated to be the substrate for a place code, where sound frequency is encoded by the location of the neurons that fire during the stimulus. Though conceptually simple, the computations that allow for the representation of intensity and complex sounds are poorly understood. Here, a mathematical framework is developed in order to define clearly the conditions that support a place code. To accommodate both frequency and intensity information, the neural network is described as a space with elements that represent individual neurons and clusters of neurons. A mapping is then constructed from acoustic space to neural space so that frequency and intensity are encoded, respectively, by the location and size of the clusters. Algebraic operations -addition and multiplication- are derived to elucidate the rules for representing, assembling, and modulating multi-frequency sound in networks. The resulting outcomes of these operations are consistent with network simulations as well as with electrophysiological and psychophysical data. The analyses show how both frequency and intensity can be encoded with a purely place code, without the need for rate or temporal coding schemes. The algebraic operations are used to describe loudness summation and suggest a mechanism for the critical band. The mathematical approach complements experimental and computational approaches and provides a foundation for interpreting data and constructing models.

Sensitivity to Stimulus Irregularity Is Inherent in Neural Networks

Behavior is controlled by complex neural networks in which neurons process thousands of inputs. However, even short spike trains evoked in a single cortical neuron were demonstrated to be sufficient to influence behavior in vivo. Specifically, irregular sequences of interspike intervals (ISIs) had a more reliable influence on behavior despite their resemblance to stochastic activity. Similarly, irregular tactile stimulation led to higher rates of behavioral responses. In this study, we identify the mechanisms enabling this sensitivity to stimulus irregularity (SSI) on the neuronal and network levels using simulated spiking neural networks. Matching in vivo experiments, we find that irregular stimulation elicits more detectable network events (bursts) than regular stimulation. Dissecting the stimuli, we identify short ISIs-occurring more frequently in irregular stimulations-as the main drivers of SSI rather than complex irregularity per se. In addition, we find that short-term plasticity modulates SSI. We subsequently eliminate the different mechanisms in turn to assess their role in generating SSI. Removing inhibitory interneurons, we find that SSI is retained, suggesting that SSI is not dependent on inhibition. Removing recurrency, we find that SSI is retained due to the ability of individual neurons to integrate activity over short timescales ("cell memory"). Removing single-neuron dynamics, we find that SSI is retained based on the short-term retention of activity within the recurrent network structure ("network memory"). Finally, using a further simplified probabilistic model, we find that local network structure is not required for SSI. Hence, SSI is identified as a general property that we hypothesize to be ubiquitous in neural networks with different structures and biophysical properties. Irregular sequences contain shorter ISIs, which are the main drivers underlying SSI. The experimentally observed SSI should thus generalize to other systems, suggesting a functional role for irregular activity in cortex.

Short-Term Synaptic Plasticity as a Mechanism for Sensory Timing

The ability to detect time intervals and temporal patterns is critical to some of the most fundamental computations the brain performs, including the ability to communicate and appraise a dynamically changing environment. Many of these computations take place on the scale of tens to hundreds of milliseconds. Electrophysiological evidence shows that some neurons respond selectively to duration, interval, rate, or order. Because the time constants of many time-varying neural and synaptic properties, including short-term synaptic plasticity (STP), are also in the range of tens to hundreds of milliseconds, they are strong candidates to underlie the formation of temporally selective neurons. Neurophysiological studies indicate that STP is indeed one of the mechanisms that contributes to temporal selectivity, and computational models demonstrate that neurons embedded in local microcircuits exhibit temporal selectivity if their synapses undergo STP. Converging evidence suggests that some forms of temporal selectivity emerge from the dynamic changes in the balance of excitation and inhibition imposed by STP.

Describing complex cells in primary visual cortex: a comparison of context and multifilter LN models

Receptive field (RF) models are an important tool for deciphering neural responses to sensory stimuli. The two currently popular RF models are multifilter linear-nonlinear (LN) models and context models. Models are, however, never correct, and they rely on assumptions to keep them simple enough to be interpretable. As a consequence, different models describe different stimulus-response mappings, which may or may not be good approximations of real neural behavior. In the current study, we take up two tasks: 1) we introduce new ways to estimate context models with realistic nonlinearities, that is, with logistic and exponential functions, and 2) we evaluate context models and multifilter LN models in terms of how well they describe recorded data from complex cells in cat primary visual cortex. Our results, based on single-spike information and correlation coefficients, indicate that context models outperform corresponding multifilter LN models of equal complexity (measured in terms of number of parameters), with the best increase in performance being achieved by the novel context models. Consequently, our results suggest that the multifilter LN-model framework is suboptimal for describing the behavior of complex cells: the context-model framework is clearly superior while still providing interpretable quantizations of neural behavior. NEW & NOTEWORTHY We used data from complex cells in primary visual cortex to estimate a wide variety of receptive field models from two frameworks that have previously not been compared with each other. The models included traditionally used multifilter linear-nonlinear models and novel variants of context models. Using mutual information and correlation coefficients as performance measures, we showed that context models are superior for describing complex cells and that the novel context models performed the best.

Comparison of the Upper Marginal Neurons of Cortical Layer 2 with Layer 2/3 Pyramidal Neurons in Mouse Temporal Cortex

Layer 2/3 (L2/3) excitatory neurons in the neocortex make major contributions to corticocortical connections and therefore function to integrate information across cortical areas and hemispheres. Recent evidence suggests that excitatory neurons in L2/3 can have different properties. Sparse evidence from previous studies suggests that L2 neurons located at the border between L1 and L2 (referred to as L2 marginal neurons, L2MNs), have a morphology distinct from a typical pyramidal neuron. However, whether the membrane properties and input/output properties of L2MNs are different from those of typical pyramidal neurons in L2/3 is unknown. Here we addressed these questions in a slice preparation of mouse temporal cortex. We found that L2MNs were homogeneous in intrinsic membrane properties but appeared diverse in morphology. In agreement with previous studies, L2MNs either had oblique apical dendrites or had no obvious apical dendrites. The tufts of both apical and basal dendrites of these neurons invaded L1 extensively. All L2MNs showed a regular firing pattern with moderate adaptation. Compared with typical L2/3 pyramidal neurons that showed regular spiking (RS) activity (neurons), L2MNs showed a higher firing rate, larger sag ratio, and higher input resistance. No difference in the amplitude of excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs, respectively), evoked by stimulation of L1, was found between the two types of neurons, but the IPSPs in L2MNs had a slower time course than those in L2/3 RS cells. In paired recordings, unitary EPSPs showed no significant differences between synapses formed by L2MNs and those formed by L2/3 RS neurons. However, short-term synaptic depression (STSD) examined with a L2MN as the presynaptic neuron was greater when another L2MN was the postsynaptic neuron than when a L2/3 RS neuron was the postsynaptic neuron. The distinct morphological features of L2MNs found here have developmental implications, and the differences in electrophysiological properties between L2MNs and other L2/3 pyramidal neurons suggest that they play different functional roles in cortical networks.

Short-term depression and transient memory in sensory cortex

Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient. (ii) As we show, those positive feedback models which decay to baseline lose their persistence when their recurrent connections are subject to short-term depression, a common property of excitatory connections in early sensory areas. Dual time constant network behavior has also been implemented by nonlinear afferents producing a large transient input followed by much smaller steady state input. We show that such networks require unphysiologically large onset transients to produce the rise and decay observed in sensory areas. Our study explores how memory and persistence can be implemented in another model class, derivative feedback networks. We show that these networks can operate with two vastly different time courses, changing their state quickly when new information is coming in but retaining it for a long time, and that these capabilities are robust to short-term depression. Specifically, derivative feedback networks with short-term depression that acts differentially on positive and negative feedback projections are capable of dynamically changing their time constant, thus allowing fast onset and slow decay of responses without requiring unrealistically large input transients.

Probing neural mechanisms underlying auditory stream segregation in humans by transcranial direct current stimulation (tDCS)

One hypothesis concerning the neural underpinnings of auditory streaming states that frequency tuning of tonotopically organized neurons in primary auditory fields in combination with physiological forward suppression is necessary for the separation of representations of high-frequency A and low-frequency B tones. The extent of spatial overlap between the tonotopic activations of A and B tones is thought to underlie the perceptual organization of streaming sequences into one coherent or two separate streams. The present study attempts to interfere with these mechanisms by transcranial direct current stimulation (tDCS) and to probe behavioral outcomes reflecting the perception of ABAB streaming sequences. We hypothesized that tDCS by modulating cortical excitability causes a change in the separateness of the representations of A and B tones, which leads to a change in the proportions of one-stream and two-stream percepts. To test this, 22 subjects were presented with ambiguous ABAB sequences of three different frequency separations (∆F) and had to decide on their current percept after receiving sham, anodal, or cathodal tDCS over the left auditory cortex. We could confirm our hypothesis at the most ambiguous ∆F condition of 6 semitones. For anodal compared with sham and cathodal stimulation, we found a significant decrease in the proportion of two-stream perception and an increase in the proportion of one-stream perception. The results demonstrate the feasibility of using tDCS to probe mechanisms underlying auditory streaming through the use of various behavioral measures. Moreover, this approach allows one to probe the functions of auditory regions and their interactions with other processing stages.

Auditory cortical field coding long-lasting tonal offsets in mice

Although temporal information processing is important in auditory perception, the mechanisms for coding tonal offsets are unknown. We investigated cortical responses elicited at the offset of tonal stimuli using flavoprotein fluorescence imaging in mice. Off-responses were clearly observed at the offset of tonal stimuli lasting for 7 s, but not after stimuli lasting for 1 s. Off-responses to the short stimuli appeared in a similar cortical region, when conditioning tonal stimuli lasting for 5–20 s preceded the stimuli. MK-801, an inhibitor of NMDA receptors, suppressed the two types of off-responses, suggesting that disinhibition produced by NMDA receptor-dependent synaptic depression might be involved in the off-responses. The peak off-responses were localized in a small region adjacent to the primary auditory cortex, and no frequency-dependent shift of the response peaks was found. Frequency matching of preceding tonal stimuli with short test stimuli was not required for inducing off-responses to short stimuli. Two-photon calcium imaging demonstrated significantly larger neuronal off-responses to stimuli lasting for 7 s in this field, compared with off-responses to stimuli lasting for 1 s. The present results indicate the presence of an auditory cortical field responding to long-lasting tonal offsets, possibly for temporal information processing.

Capturing contextual effects in spectro-temporal receptive fields

Spectro-temporal receptive fields (STRFs) are thought to provide descriptive images of the computations performed by neurons along the auditory pathway. However, their validity can be questioned because they rely on a set of assumptions that are probably not fulfilled by real neurons exhibiting contextual effects, that is, nonlinear interactions in the time or frequency dimension that cannot be described with a linear filter. We used a novel approach to investigate how a variety of contextual effects, due to facilitating nonlinear interactions and synaptic depression, affect different STRF models, and if these effects can be captured with a context field (CF). Contextual effects were incorporated in simulated networks of spiking neurons, allowing one to define the true STRFs of the neurons. This, in turn, made it possible to evaluate the performance of each STRF model by comparing the estimations with the true STRFs. We found that currently used STRF models are particularly poor at estimating inhibitory regions. Specifically, contextual effects make estimated STRFs dependent on stimulus density in a contrasting fashion: inhibitory regions are underestimated at lower densities while artificial inhibitory regions emerge at higher densities. The CF was found to provide a solution to this dilemma, but only when it is used together with a generalized linear model. Our results therefore highlight the limitations of the traditional STRF approach and provide useful recipes for how different STRF models and stimuli can be used to arrive at reliable quantifications of neural computations in the presence of contextual effects. The results therefore push the purpose of STRF analysis from simply finding an optimal stimulus toward describing context-dependent computations of neurons along the auditory pathway.

Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields

Mammals perceive a wide range of temporal cues in natural sounds, and the auditory cortex is essential for their detection and discrimination. The rat primary (A1), ventral (VAF), and caudal suprarhinal (cSRAF) auditory cortical fields have separate thalamocortical pathways that may support unique temporal cue sensitivities. To explore this, we record responses of single neurons in the three fields to variations in envelope shape and modulation frequency of periodic noise sequences. Spike rate, relative synchrony, and first-spike latency metrics have previously been used to quantify neural sensitivities to temporal sound cues; however, such metrics do not measure absolute spike timing of sustained responses to sound shape. To address this, in this study we quantify two forms of spike-timing precision, jitter, and reliability. In all three fields, we find that jitter decreases logarithmically with increase in the basis spline (B-spline) cutoff frequency used to shape the sound envelope. In contrast, reliability decreases logarithmically with increase in sound envelope modulation frequency. In A1, jitter and reliability vary independently, whereas in ventral cortical fields, jitter and reliability covary. Jitter time scales increase (A1 < VAF < cSRAF) and modulation frequency upper cutoffs decrease (A1 > VAF > cSRAF) with ventral progression from A1. These results suggest a transition from independent encoding of shape and periodicity sound cues on short time scales in A1 to a joint encoding of these same cues on longer time scales in ventral nonprimary cortices.

Memory Stacking in Hierarchical Networks

Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to study how such hierarchical structures affect temporal binding in neural networks. We equipped individual units in different types of feedforward networks with local memory mechanisms storing recent inputs and observed how this affected the ability of the networks to process stimuli context dependently. Our findings illustrate that these local memories stack up in hierarchical structures and hence allow network units to exhibit selectivity to spectral sequences longer than the time spans of the local memories. We also illustrate that short-term synaptic plasticity is a potential local memory mechanism within the auditory cortex, and we show that it can bring robustness to context dependence against variation in the temporal rate of stimuli, while introducing nonlinearities to response profiles that are not well captured by standard linear spectrotemporal receptive field models. The results therefore indicate that short-term synaptic plasticity might provide hierarchically structured auditory cortex with computational capabilities important for robust representations of spectrotemporal patterns.

Immediate manifestation of acoustic trauma in the auditory cortex is layer specific and cell type dependent

Exposure to loud sounds damages the auditory periphery and induces maladaptive changes in central parts of the auditory system. Diminished peripheral afferentation and altered inhibition influence the processing of sounds in the auditory cortex. It is unclear, however, which types of inhibitory interneurons are affected by acoustic trauma. Here we used single-unit electrophysiological recording and two-photon calcium imaging in anesthetized mice to evaluate the effects of acute acoustic trauma (125 dB SPL, white noise, 5 min) on the response properties of neurons in the core auditory cortex. Electrophysiological measurements suggested the selective impact of acoustic trauma on inhibitory interneurons in the auditory cortex. To further investigate which interneuronal types were affected, we used two-photon calcium imaging to record the activity of neurons in cortical layers 2/3 and 4, specifically focusing on parvalbumin-positive (PV+) and somatostatin-positive (SST+) interneurons. Spontaneous and pure-tone-evoked firing rates of SST+ interneurons increased in layer 4 immediately after acoustic trauma and remained almost unchanged in layer 2/3. Furthermore, PV+ interneurons with high best frequencies increased their evoked-to-spontaneous firing rate ratios only in layer 2/3 and did not change in layer 4. Finally, acoustic trauma unmasked low-frequency excitatory inputs only in layer 2/3. Our results demonstrate layer-specific changes in the activity of auditory cortical inhibitory interneurons within minutes after acoustic trauma.

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.

Experience Changes How Emotion in Music Is Judged: Evidence from Children Listening with Bilateral Cochlear Implants, Bimodal Devices, and Normal Hearing

Children using unilateral cochlear implants abnormally rely on tempo rather than mode cues to distinguish whether a musical piece is happy or sad. This led us to question how this judgment is affected by the type of experience in early auditory development. We hypothesized that judgments of the emotional content of music would vary by the type and duration of access to sound in early life due to deafness, altered perception of musical cues through new ways of using auditory prostheses bilaterally, and formal music training during childhood. Seventy-five participants completed the Montreal Emotion Identification Test. Thirty-three had normal hearing (aged 6.6 to 40.0 years) and 42 children had hearing loss and used bilateral auditory prostheses (31 bilaterally implanted and 11 unilaterally implanted with contralateral hearing aid use). Reaction time and accuracy were measured. Accurate judgment of emotion in music was achieved across ages and musical experience. Musical training accentuated the reliance on mode cues which developed with age in the normal hearing group. Degrading pitch cues through cochlear implant-mediated hearing induced greater reliance on tempo cues, but mode cues grew in salience when at least partial acoustic information was available through some residual hearing in the contralateral ear. Finally, when pitch cues were experimentally distorted to represent cochlear implant hearing, individuals with normal hearing (including those with musical training) switched to an abnormal dependence on tempo cues. The data indicate that, in a western culture, access to acoustic hearing in early life promotes a preference for mode rather than tempo cues which is enhanced by musical training. The challenge to these preferred strategies during cochlear implant hearing (simulated and real), regardless of musical training, suggests that access to pitch cues for children with hearing loss must be improved by preservation of residual hearing and improvements in cochlear implant technology.

A neuronal network model for context-dependence of pitch change perception

Many natural stimuli have perceptual ambiguities that can be cognitively resolved by the surrounding context. In audition, preceding context can bias the perception of speech and non-speech stimuli. Here, we develop a neuronal network model that can account for how context affects the perception of pitch change between a pair of successive complex tones. We focus especially on an ambiguous comparison—listeners experience opposite percepts (either ascending or descending) for an ambiguous tone pair depending on the spectral location of preceding context tones. We developed a recurrent, firing-rate network model, which detects frequency-change-direction of successively played stimuli and successfully accounts for the context-dependent perception demonstrated in behavioral experiments. The model consists of two tonotopically organized, excitatory populations, E_up and E_down, that respond preferentially to ascending or descending stimuli in pitch, respectively. These preferences are generated by an inhibitory population that provides inhibition asymmetric in frequency to the two populations; context dependence arises from slow facilitation of inhibition. We show that contextual influence depends on the spectral distribution of preceding tones and the tuning width of inhibitory neurons. Further, we demonstrate, using phase-space analysis, how the facilitated inhibition from previous stimuli and the waning inhibition from the just-preceding tone shape the competition between the E_up and E_down populations. In sum, our model accounts for contextual influences on the pitch change perception of an ambiguous tone pair by introducing a novel decoding strategy based on direction-selective units. The model's network architecture and slow facilitating inhibition emerge as predictions of neuronal mechanisms for these perceptual dynamics. Since the model structure does not depend on the specific stimuli, we show that it generalizes to other contextual effects and stimulus types.

Cell-specific activity-dependent fractionation of layer 2/3→5B excitatory signaling in mouse auditory cortex

Auditory cortex (AC) layer 5B (L5B) contains both corticocollicular neurons, a type of pyramidal-tract neuron projecting to the inferior colliculus, and corticocallosal neurons, a type of intratelencephalic neuron projecting to contralateral AC. Although it is known that these neuronal types have distinct roles in auditory processing and different response properties to sound, the synaptic and intrinsic mechanisms shaping their input-output functions remain less understood. Here, we recorded in brain slices of mouse AC from retrogradely labeled corticocollicular and neighboring corticocallosal neurons in L5B. Corticocollicular neurons had, on average, lower input resistance, greater hyperpolarization-activated current (Ih), depolarized resting membrane potential, faster action potentials, initial spike doublets, and less spike-frequency adaptation. In paired recordings between single L2/3 and labeled L5B neurons, the probabilities of connection, amplitude, latency, rise time, and decay time constant of the unitary EPSC were not different for L2/3→corticocollicular and L2/3→corticocallosal connections. However, short trains of unitary EPSCs showed no synaptic depression in L2/3→corticocollicular connections, but substantial depression in L2/3→corticocallosal connections. Synaptic potentials in L2/3→corticocollicular connections decayed faster and showed less temporal summation, consistent with increased Ih in corticocollicular neurons, whereas synaptic potentials in L2/3→corticocallosal connections showed more temporal summation. Extracellular L2/3 stimulation at two different rates resulted in spiking in L5B neurons; for corticocallosal neurons the spike rate was frequency dependent, but for corticocollicular neurons it was not. Together, these findings identify cell-specific intrinsic and synaptic mechanisms that divide intracortical synaptic excitation from L2/3 to L5B into two functionally distinct pathways with different input-output functions.

Dynamic representation of spectral edges in guinea pig primary auditory cortex

The central representation of a given acoustic motif is thought to be strongly context dependent, i.e., to rely on the spectrotemporal past and present of the acoustic mixture in which it is embedded. The present study investigated the cortical representation of spectral edges (i.e., where stimulus energy changes abruptly over frequency) and its dependence on stimulus duration and depth of the spectral contrast in guinea pig. We devised a stimulus ensemble composed of random tone pips with or without an attenuated frequency band (AFB) of variable depth. Additionally, the multitone ensemble with AFB was interleaved with periods of silence or with multitone ensembles without AFB. We have shown that the representation of the frequencies near but outside the AFB is greatly enhanced, whereas the representation of frequencies near and inside the AFB is strongly suppressed. These cortical changes depend on the depth of the AFB: although they are maximal for the largest depth of the AFB, they are also statistically significant for depths as small as 10 dB. Finally, the cortical changes are quick, occurring within a few seconds of stimulus ensemble presentation with AFB, and are very labile, disappearing within a few seconds after the presentation without AFB. Overall, this study demonstrates that the representation of spectral edges is dynamically enhanced in the auditory centers. These central changes may have important functional implications, particularly in noisy environments where they could contribute to preserving the central representation of spectral edges.

Presynaptic cholinergic neuromodulation alters the temporal dynamics of short-term depression at parvalbumin-positive basket cell synapses from juvenile CA1 mouse hippocampus

Parvalbumin-positive basket cells (PV BCs) of the CA1 hippocampus are active participants in theta (5-12 Hz) and gamma (20-80 Hz) oscillations in vivo. When PV BCs are driven at these frequencies in vitro, inhibitory postsynaptic currents (IPSCs) in synaptically connected CA1 pyramidal cells exhibit paired-pulse depression (PPD) and multiple-pulse depression (MPD). Moreover, PV BCs express presynaptic muscarinic acetylcholine receptors (mAChRs) that may be activated by synaptically released acetylcholine during learning behaviors in vivo. Using acute hippocampal slices from the CA1 hippocampus of juvenile PV-GFP mice, we performed whole cell recordings from synaptically connected PV BC-CA1 pyramidal cell pairs to investigate how bath application of 10 μM muscarine impacts PPD and MPD at CA1 PV BC-pyramidal cell synapses. In accordance with previous studies, PPD and MPD magnitude increased with stimulation frequency. mAChR activation reduced IPSC amplitude and transiently reduced PPD, but MPD was largely maintained. Consistent with a reduction in release probability (pr), MPD and mAChR activation increased both the coefficient of variation of IPSC amplitudes and the fraction of failures. Using variance-mean analysis, we converted MPD trains to pr functions and developed a kinetic model that optimally fit six distinct pr conditions. The model revealed that vesicular depletion caused MPD and that recovery from depression was dependent on calcium. mAChR activation reduced the presynaptic calcium transient fourfold and initial pr twofold, thereby reducing PPD. However, mAChR activation slowed calcium-dependent recovery from depression during sustained repetitive activity, thereby preserving MPD. Thus the activation of presynaptic mAChRs optimally protects PV BCs from vesicular depletion during short bursts of high-frequency activity.

A model of order-selectivity based on dynamic changes in the balance of excitation and inhibition produced by short-term synaptic plasticity

Determining the order of sensory events separated by a few hundred milliseconds is critical to many forms of sensory processing, including vocalization and speech discrimination. Although many experimental studies have recorded from auditory order-sensitive and order-selective neurons, the underlying mechanisms are poorly understood. Here we demonstrate that universal properties of cortical synapses-short-term synaptic plasticity of excitatory and inhibitory synapses-are well suited for the generation of order-selective neural responses. Using computational models of canonical disynaptic circuits, we show that the dynamic changes in the balance of excitation and inhibition imposed by short-term plasticity lead to the generation of order-selective responses. Parametric analyses predict that among the forms of short-term plasticity expressed at excitatory-to-excitatory, excitatory-to-inhibitory, and inhibitory-to-excitatory synapses, the single most important contributor to order-selectivity is the paired-pulse depression of inhibitory postsynaptic potentials (IPSPs). A topographic model of the auditory cortex that incorporates short-term plasticity accounts for both context-dependent suppression and enhancement in response to paired tones. Together these results provide a framework to account for an important computational problem based on ubiquitous synaptic properties that did not yet have a clearly established computational function. Additionally, these studies suggest that disynaptic circuits represent a fundamental computational unit that is capable of processing both spatial and temporal information.

Poisson-like spiking in circuits with probabilistic synapses

Neuronal activity in cortex is variable both spontaneously and during stimulation, and it has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms underlying cortical-like spiking variability over such a broad continuum of rates are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generate Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. Other sources of variability, such as random synaptic delays or spike generation jittering, do not lead to Poisson-like variability at high rates because they cannot be sufficiently amplified by recurrent neuronal networks. We also show that probabilistic synapses predict Fano factor constancy of synaptic conductances. Our results suggest that synaptic noise is a robust and sufficient mechanism for the type of variability found in cortex.

Neurons in cortex fire irregularly and in an irreproducible way under repeated presentations of an identical stimulus. Where is this spiking variability coming from? One unexplored possibility is that cortical variability originates from the amplification of a particular type of noise that is present throughout cortex: synaptic failures. In this paper we found that probabilistic synapses are sufficient to lead to cortical-like firing for several orders of magnitude in firing rate. Moreover, the resulting variability displays the property that variance of the spike counts is proportional to the mean for every cell in the network, the so-called Poisson-like firing, a well-known property of sensory cortical firing responses. We finally argue that far from being harmful, probabilistic synapses allow networks to sample neuronal states and sustain probabilistic population codes. Therefore, synaptic noise is not only a robust mechanism for the type of variability found in cortex, but it also provides cortical circuits with computational properties to perform probabilistic inference under noisy and ambiguous stimulation.

Deprivation-induced strengthening of presynaptic and postsynaptic inhibitory transmission in layer 4 of visual cortex during the critical period

Inhibition from fast-spiking (FS) interneurons plays a crucial role in shaping cortical response properties and gating developmental periods of activity-dependent plasticity, yet the expression mechanisms underlying FS inhibitory plasticity remain largely unexplored. In layer 4 of visual cortex (V1), monocular deprivation (MD) induces either depression or potentiation of FS to star pyramidal neuron (FS→SP) synapses, depending on the age of onset (Maffei et al., 2004, 2006). This reversal in the sign (- to +) of plasticity occurs on the cusp of the canonical critical period (CP). To investigate the expression locus behind this switch in sign of inhibitory plasticity, mice underwent MD during the pre-CP [eye-opening to postnatal day (p)17] or CP (p22-p25), and FS→SP synaptic strength within layer 4 was assessed using confocal and immunoelectron microscopy, as well as optogenetic activation of FS cells to probe quantal amplitude at FS→SP synapses. Brief MD before p17 or p25 did not alter the density of FS→SP contacts. However, at the ultrastructural level, FS→SP synapses in deprived hemispheres during the CP, but not the pre-CP or in GAD65 knock-out mice, had larger synapses and increased docked vesicle density compared with synapses from the nondeprived control hemispheres. Moreover, FS→SP evoked miniature IPSCs increased in deprived hemispheres when MD was initiated during the CP, accompanied by an increase in the density of postsynaptic GABAA receptors at FS→SP synapses. These coordinated changes in FS→SP synaptic strength define an expression pathway modulating excitatory output during CP plasticity in visual cortex.

Frequency-specific adaptation in human auditory cortex depends on the spectral variance in the acoustic stimulation

In auditory cortex, activation and subsequent adaptation is strongest for regions responding best to a stimulated tone frequency and less for regions responding best to other frequencies. Previous attempts to characterize the spread of neural adaptation in humans investigated the auditory cortex N1 component of the event-related potentials. Importantly, however, more recent studies in animals show that neural response properties are not independent of the stimulation context. To link these findings in animals to human scalp potentials, we investigated whether contextual factors of the acoustic stimulation, namely, spectral variance, affect the spread of neural adaptation. Electroencephalograms were recorded while human participants listened to random tone sequences varying in spectral variance (narrow vs. wide). Spread of adaptation was investigated by modeling single-trial neural adaptation and subsequent recovery based on the spectro-temporal stimulation history. Frequency-specific neural responses were largest on the N1 component, and the modeled neural adaptation indices were strongly predictive of trial-by-trial amplitude variations. Yet the spread of adaption varied depending on the spectral variance in the stimulation, such that adaptation spread was broadened for tone sequences with wide spectral variance. Thus the present findings reveal context-dependent auditory cortex adaptation and point toward a flexibly adjusting auditory system that changes its response properties with the spectral requirements of the acoustic environment.

M-channels modulate the intrinsic excitability and synaptic responses of layer 2/3 pyramidal neurons in auditory cortex

Neurons in the auditory cortex are believed to utilize temporal patterns of neural activity to accurately process auditory information but the intrinsic neuronal mechanism underlying the control of auditory neural activity is not known. The slowly activating, persistent K(+) channel, also called M-channel that belongs to the Kv7 family, is already known to be important in regulating subthreshold neural excitability and synaptic summation in neocortical and hippocampal pyramidal neurons. However, its functional role in the primary auditory cortex (A1) has never been characterized. In this study, we investigated the roles of M-channels on neuronal excitability, short-term plasticity, and synaptic summation of A1 layer 2/3 regular spiking pyramidal neurons with whole-cell current-clamp recordings in vitro. We found that blocking M-channels with a selective M-channel blocker, XE991, significantly increased neural excitability of A1 layer 2/3 pyramidal neurons. Furthermore, M-channels controled synaptic responses of intralaminar-evoked excitatory postsynaptic potentials (EPSPs); XE991 significantly increased EPSP amplitude, decreased the rate of short-term depression, and increased the synaptic summation. These results suggest that M-channels are involved in controlling spike output patterns and synaptic responses of A1 layer 2/3 pyramidal neurons, which would have important implications in auditory information processing.