From sounds to words: a neurocomputational model of adaptation, inhibition and memory processes in auditory change detection

Can human brain connectivity explain verbal working memory?

The ability of humans to store spoken words in verbal working memory and build extensive vocabularies is believed to stem from evolutionary changes in cortical connectivity across primate species. However, the underlying neurobiological mechanisms remain unclear. Why can humans acquire vast vocabularies, while non-human primates cannot? This study addresses this question using brain-constrained neural networks that realize between-species differences in cortical connectivity. It investigates how these structural differences support the formation of neural representations for spoken words and the emergence of verbal working memory, crucial for human vocabulary building. We develop comparative models of frontotemporal and occipital cortices, reflecting human and non-human primate neuroanatomy. Using meanfield and spiking neural networks, we simulate auditory word recognition and examine verbal working memory function. The "human models", characterized by denser inter-area connectivity in core language areas, produced larger cell assemblies than the "monkey models", with specific topographies reflecting semantic properties of the represented words. Crucially, longer-lasting reverberant neural activity was observed in human versus monkey architectures, compatible with robust verbal working memory, a necessary condition for vocabulary building. Our findings offer insights into the structural basis of human-specific symbol learning and verbal working memory, shedding light on humans' unique capacity for large vocabulary acquisition.

Establishing neural representations for new word forms in 12-month-old infants

During the first year of life, infants start to learn the lexicon of their native language. Word learning includes the establishment of longer-term representations for the phonological form and the meaning of the word in the brain, as well as the link between them. However, it is not known how the brain processes word forms immediately after they have been learned. We familiarized 12-month-old infants (N = 52) with two pseudowords and studied their neural signatures. Specifically, we determined whether a newly learned word form elicits neural signatures similar to those observed when a known word is recognized (i.e., when a well-established word representation is activated, eliciting enhanced mismatch responses) or whether the processing of a newly learned word form shows the suppression of the neural response along with the principles of predictive coding of a learned rule (i.e., the order of the syllables of the new word form). The pattern of results obtained in the current study suggests that recognized word forms elicit a mismatch response of negative polarity, similar to newly learned and previously known words with an established representation in long-term memory. In contrast, prediction errors caused by acoustic novelty or deviation from the expected order in a sequence of (pseudo)words elicit responses of positive polarity. This suggests that electric brain activity is not fully explained by the predictive coding framework.

Distributed representations of prediction error signals across the cortical hierarchy are synergistic

A relevant question concerning inter-areal communication in the cortex is whether these interactions are synergistic. Synergy refers to the complementary effect of multiple brain signals conveying more information than the sum of each isolated signal. Redundancy, on the other hand, refers to the common information shared between brain signals. Here, we dissociated cortical interactions encoding complementary information (synergy) from those sharing common information (redundancy) during prediction error (PE) processing. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded synergistic and redundant information about PE processing. The information conveyed by ERPs and BB signals was synergistic even at lower stages of the hierarchy in the auditory cortex and between auditory and frontal regions. Using a brain-constrained neural network, we simulated the synergy and redundancy observed in the experimental results and demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback, and feedforward connections. These results indicate that distributed representations of PE signals across the cortical hierarchy can be highly synergistic.

Predicting and coding sound into action translation in spinal cord injured people

Motor activation in response to perception of action-related stimuli may depend on a resonance mechanism subserving action understanding. The extent to which this mechanism is innate or learned from sensorimotor experience is still unclear. Here, we recorded EEG while people with paraplegia or tetraplegia consequent to spinal cord injury (SCI) and healthy control participants were presented with action sounds produced by body parts (mouth, hands or feet) that were or were not affected by SCI. Non-action sounds were used as further control. We observed reduced brain activation in subjects affected by SCI at both pre- and post-stimulus latencies specifically for those actions whose effector was disconnected by the spinal lesion (i.e., hand sound for tetraplegia and leg sound for both paraplegia and tetraplegia). Correlation analyses showed that these modulations were functionally linked with the chronicity of the lesion, indicating that the longer the time the lesion- EEG data acquisition interval and/or the more the lesion occurred at a young age, the weaker was the cortical activity in response to these action sounds. Tellingly, source estimations confirmed that these modulations originated from a deficit in the motor resonance mechanism, by showing diminished activity in premotor (during prediction and perception) and near the primary motor (during perception) areas. Such dissociation along the cortical hierarchy is consistent with both previous reports in healthy subjects and with hierarchical predictive coding accounts. Overall, these data expand on the notion that sensorimotor experience maintains the cortical representations relevant to anticipate and perceive action-related stimuli.

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models' ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

Predictions about prosody facilitate lexical access: Evidence from P50/N100 and MMN components

Research into the neural foundation of perception asserts a model where top-down predictions modulate the bottom-up processing of sensory input. Despite becoming increasingly influential in cognitive neuroscience, the precise account of this predictive coding framework remains debated. In this study, we aim to contribute to this debate by investigating how predictions about prosody facilitate speech perception, and to shed light especially on lexical access influenced by simultaneous predictions in different domains, inter alia, prosodic and semantic. Using a passive auditory oddball paradigm, we examined neural responses to prosodic changes, leading to a semantic change as in Dutch nouns canon ['kaːnɔn] 'canon' vs kanon [kaː'nɔn] 'cannon', and used acoustically identical pseudowords as controls. Results from twenty-eight native speakers of Dutch (age range 18-32 years) indicated an enhanced P50/N100 complex to prosodic change in pseudowords as well as an MMN response to both words and pseudowords. The enhanced P50/N100 response to pseudowords is claimed to indicate that all relevant auditory information is still processed by the brain, whereas the reduced response to words might reflect the suppression of information that has already been encoded. The MMN response to pseudowords and words, on the other hand, is best justified by the unification of previously established prosodic representations with sensory and semantic input respectively. This pattern of results is in line with the predictive coding framework acting on multiple levels and is of crucial importance to indicate that predictions about linguistic prosodic information are utilized by the brain as early as 50 ms.

Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia

The neurobiological nature of semantic knowledge, i.e., the encoding and storage of conceptual information in the human brain, remains a poorly understood and hotly debated subject. Clinical data on semantic deficits and neuroimaging evidence from healthy individuals have suggested multiple cortical regions to be involved in the processing of meaning. These include semantic hubs (most notably, anterior temporal lobe, ATL) that take part in semantic processing in general as well as sensorimotor areas that process specific aspects/categories according to their modality. Biologically inspired neurocomputational models can help elucidate the exact roles of these regions in the functioning of the semantic system and, importantly, in its breakdown in neurological deficits. We used a neuroanatomically constrained computational model of frontotemporal cortices implicated in word acquisition and processing, and adapted it to simulate and explain the effects of semantic dementia (SD) on word processing abilities. SD is a devastating, yet insufficiently understood progressive neurodegenerative disease, characterised by semantic knowledge deterioration that is hypothesised to be specifically related to neural damage in the ATL. The behaviour of our brain-based model is in full accordance with clinical data-namely, word comprehension performance decreases as SD lesions in ATL progress, whereas word repetition abilities remain less affected. Furthermore, our model makes predictions about lesion- and category-specific effects of SD: our simulation results indicate that word processing should be more impaired for object- than for action-related words, and that degradation of white matter should produce more severe consequences than the same proportion of grey matter decay. In sum, the present results provide a neuromechanistic explanatory account of cortical-level language impairments observed during the onset and progress of semantic dementia.

Neurobiological mechanisms for language, symbols and concepts: Clues from brain-constrained deep neural networks

Neural networks are successfully used to imitate and model cognitive processes. However, to provide clues about the neurobiological mechanisms enabling human cognition, these models need to mimic the structure and function of real brains. Brain-constrained networks differ from classic neural networks by implementing brain similarities at different scales, ranging from the micro- and mesoscopic levels of neuronal function, local neuronal links and circuit interaction to large-scale anatomical structure and between-area connectivity. This review shows how brain-constrained neural networks can be applied to study in silico the formation of mechanisms for symbol and concept processing and to work towards neurobiological explanations of specifically human cognitive abilities. These include verbal working memory and learning of large vocabularies of symbols, semantic binding carried by specific areas of cortex, attention focusing and modulation driven by symbol type, and the acquisition of concrete and abstract concepts partly influenced by symbols. Neuronal assembly activity in the networks is analyzed to deliver putative mechanistic correlates of higher cognitive processes and to develop candidate explanations founded in established neurobiological principles.

Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks

Sensory areas of cortex respond more strongly to infrequent stimuli when these violate previously established regularities, a phenomenon known as deviance detection (DD). Previous modeling work has mainly attempted to explain DD on the basis of synaptic plasticity. However, a large fraction of cortical neurons also exhibit firing rate adaptation, an underexplored potential mechanism. Here, we investigate DD in a spiking neuronal network model with two types of short-term plasticity, fast synaptic short-term depression (STD) and slower threshold adaptation (TA). We probe the model with an oddball stimulation paradigm and assess DD by evaluating the network responses. We find that TA is sufficient to elicit DD. It achieves this by habituating neurons near the stimulation site that respond earliest to the frequently presented standard stimulus (local fatigue), which diminishes the response and promotes the recovery (global fatigue) of the wider network. Further, we find a synergy effect between STD and TA, where they interact with each other to achieve greater DD than the sum of their individual effects. We show that this synergy is caused by the local fatigue added by STD, which inhibits the global response to the frequently presented stimulus, allowing greater recovery of TA-mediated global fatigue and making the network more responsive to the deviant stimulus. Finally, we show that the magnitude of DD strongly depends on the timescale of stimulation. We conclude that highly predictable information can be encoded in strong local fatigue, which allows greater global recovery and subsequent heightened sensitivity for DD.

Modelling concrete and abstract concepts using brain-constrained deep neural networks

A neurobiologically constrained deep neural network mimicking cortical areas relevant for sensorimotor, linguistic and conceptual processing was used to investigate the putative biological mechanisms underlying conceptual category formation and semantic feature extraction. Networks were trained to learn neural patterns representing specific objects and actions relevant to semantically 'ground' concrete and abstract concepts. Grounding sets consisted of three grounding patterns with neurons representing specific perceptual or action-related features; neurons were either unique to one pattern or shared between patterns of the same set. Concrete categories were modelled as pattern triplets overlapping in their 'shared neurons', thus implementing semantic feature sharing of all instances of a category. In contrast, abstract concepts had partially shared feature neurons common to only pairs of category instances, thus, exhibiting family resemblance, but lacking full feature overlap. Stimulation with concrete and abstract conceptual patterns and biologically realistic unsupervised learning caused formation of strongly connected cell assemblies (CAs) specific to individual grounding patterns, whose neurons were spread out across all areas of the deep network. After learning, the shared neurons of the instances of concrete concepts were more prominent in central areas when compared with peripheral sensorimotor ones, whereas for abstract concepts the converse pattern of results was observed, with central areas exhibiting relatively fewer neurons shared between pairs of category members. We interpret these results in light of the current knowledge about the relative difficulty children show when learning abstract words. Implications for future neurocomputational modelling experiments as well as neurobiological theories of semantic representation are discussed.

A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity

One of the most controversial debates in cognitive neuroscience concerns the cortical locus of semantic knowledge and processing in the human brain. Experimental data revealed the existence of various cortical regions relevant for meaning processing, ranging from semantic hubs generally involved in semantic processing to modality-preferential sensorimotor areas involved in the processing of specific conceptual categories. Why and how the brain uses such complex organization for conceptualization can be investigated using biologically constrained neurocomputational models. Here, we improve pre-existing neurocomputational models of semantics by incorporating spiking neurons and a rich connectivity structure between the model ‘areas’ to mimic important features of the underlying neural substrate. Semantic learning and symbol grounding in action and perception were simulated by associative learning between co-activated neuron populations in frontal, temporal and occipital areas. As a result of Hebbian learning of the correlation structure of symbol, perception and action information, distributed cell assembly circuits emerged across various cortices of the network. These semantic circuits showed category-specific topographical distributions, reaching into motor and visual areas for action- and visually-related words, respectively. All types of semantic circuits included large numbers of neurons in multimodal connector hub areas, which is explained by cortical connectivity structure and the resultant convergence of phonological and semantic information on these zones. Importantly, these semantic hub areas exhibited some category-specificity, which was less pronounced than that observed in primary and secondary modality-preferential cortices. The present neurocomputational model integrates seemingly divergent experimental results about conceptualization and explains both semantic hubs and category-specific areas as an emergent process causally determined by two major factors: neuroanatomical connectivity structure and correlated neuronal activation during language learning.

Following instructions in a dual-task paradigm: Evidence for a temporary motor store in working memory

Evidence from dual-task studies suggests that working memory supports the retention and implementation of verbal instructions. One key finding that is not readily accommodated by existing models of working memory is that participants are consistently more accurate at physically performing rather than verbally repeating a sequence of commands. This action advantage has no obvious source within the multi-component model of working memory and has been proposed to be driven by an as yet undetected limited-capacity store dedicated to the temporary maintenance of spatial, motoric, and temporal features of intended movements. To test this hypothesis, we sought to selectively disrupt the action advantage with concurrent motor suppression. In three dual-task experiments, young adults' immediate memory for sequences of spoken instructions was assessed by both action-based and spoken recall. In addition to classic interference tasks known to tax the phonological loop and central executive, motor suppression tasks designed to impair the encoding and retention of motoric representations were included. These required participants to produce repetitive sequences of either fine motor gestures (Experiment 1, N = 16) or more basic ones (Experiments 2, N = 16, and 3, N = 16). The benefit of action-based recall was reduced following the production of basic gestures but remained intact under all other interference conditions. These results suggest that the mnemonic advantage of enacted recall depends on a cognitive system dedicated to the temporary maintenance of motoric representations of planned action sequences.

Stimulus-specific adaptation in a recurrent network model of primary auditory cortex

Stimulus-specific adaptation (SSA) occurs when neurons decrease their responses to frequently-presented (standard) stimuli but not, or not as much, to other, rare (deviant) stimuli. SSA is present in all mammalian species in which it has been tested as well as in birds. SSA confers short-term memory to neuronal responses, and may lie upstream of the generation of mismatch negativity (MMN), an important human event-related potential. Previously published models of SSA mostly rely on synaptic depression of the feedforward, thalamocortical input. Here we study SSA in a recurrent neural network model of primary auditory cortex. When the recurrent, intracortical synapses display synaptic depression, the network generates population spikes (PSs). SSA occurs in this network when deviants elicit a PS but standards do not, and we demarcate the regions in parameter space that allow SSA. While SSA based on PSs does not require feedforward depression, we identify feedforward depression as a mechanism for expanding the range of parameters that support SSA. We provide predictions for experiments that could help differentiate between SSA due to synaptic depression of feedforward connections and SSA due to synaptic depression of recurrent connections. Similar to experimental data, the magnitude of SSA in the model depends on the frequency difference between deviant and standard, probability of the deviant, inter-stimulus interval and input amplitude. In contrast to models based on feedforward depression, our model shows true deviance sensitivity as found in experiments.

A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords

Experimental evidence indicates that neurophysiological responses to well-known meaningful sensory items and symbols (such as familiar objects, faces, or words) differ from those to matched but novel and senseless materials (unknown objects, scrambled faces, and pseudowords). Spectral responses in the high beta- and gamma-band have been observed to be generally stronger to familiar stimuli than to unfamiliar ones. These differences have been hypothesized to be caused by the activation of distributed neuronal circuits or cell assemblies, which act as long-term memory traces for learned familiar items only. Here, we simulated word learning using a biologically constrained neurocomputational model of the left-hemispheric cortical areas known to be relevant for language and conceptual processing. The 12-area spiking neural-network architecture implemented replicates physiological and connectivity features of primary, secondary, and higher-association cortices in the frontal, temporal, and occipital lobes of the human brain. We simulated elementary aspects of word learning in it, focussing specifically on semantic grounding in action and perception. As a result of spike-driven Hebbian synaptic plasticity mechanisms, distributed, stimulus-specific cell-assembly (CA) circuits spontaneously emerged in the network. After training, presentation of one of the learned "word" forms to the model correlate of primary auditory cortex induced periodic bursts of activity within the corresponding CA, leading to oscillatory phenomena in the entire network and spontaneous across-area neural synchronization. Crucially, Morlet wavelet analysis of the network's responses recorded during presentation of learned meaningful "word" and novel, senseless "pseudoword" patterns revealed stronger induced spectral power in the gamma-band for the former than the latter, closely mirroring differences found in neurophysiological data. Furthermore, coherence analysis of the simulated responses uncovered dissociated category specific patterns of synchronous oscillations in distant cortical areas, including indirectly connected primary sensorimotor areas. Bridging the gap between cellular-level mechanisms, neuronal-population behavior, and cognitive function, the present model constitutes the first spiking, neurobiologically, and anatomically realistic model able to explain high-frequency oscillatory phenomena indexing language processing on the basis of dynamics and competitive interactions of distributed cell-assembly circuits which emerge in the brain as a result of Hebbian learning and sensorimotor experience.

Oscillatory power and functional connectivity in the speech change detection network

We used passive and active oddball conditions with two types of acoustic contrasts, between speech syllables that cross phonetic boundaries (across-category, AC) and between those that do not cross them (within-category, WC), to explore the effects of meaningful speech contrasts on the dynamics of the neural network underlying the mismatch negativity (MMN) to the speech deviants. We found that easily detected AC deviants evoked a MMN response that lateralized to the left hemisphere, but the very difficult to detect WC deviants did not elicit a MMN response at all. Based on independent component analysis of the continuous EEG, we computed both power changes within, and functional connectivity (phase synchronization) between, brain regional sources comprising the neural network associated with the MMN for these speech stimuli. We found that for acoustic contrasts for which an MMN was generated, power changes suggested whether a particular brain region was more involved with processing standards or deviants. Moreover, we not only replicated the changes in functional connectivity between orbitofrontal cortex and superior temporal gyrus found in previous experiments, but also found significant increases in synchronization between those regions and regions of the left inferior frontal gyrus (Broca's area), which is thought to be involved in the storage and retrieval of phonological and semantic information.

Conceptual grounding of language in action and perception: a neurocomputational model of the emergence of category specificity and semantic hubs

Current neurobiological accounts of language and cognition offer diverging views on the questions of 'where' and 'how' semantic information is stored and processed in the human brain. Neuroimaging data showing consistent activation of different multi-modal areas during word and sentence comprehension suggest that all meanings are processed indistinctively, by a set of general semantic centres or 'hubs'. However, words belonging to specific semantic categories selectively activate modality-preferential areas; for example, action-related words spark activity in dorsal motor cortex, whereas object-related ones activate ventral visual areas. The evidence for category-specific and category-general semantic areas begs for a unifying explanation, able to integrate the emergence of both. Here, a neurobiological model offering such an explanation is described. Using a neural architecture replicating anatomical and neurophysiological features of frontal, occipital and temporal cortices, basic aspects of word learning and semantic grounding in action and perception were simulated. As the network underwent training, distributed lexico-semantic circuits spontaneously emerged. These circuits exhibited different cortical distributions that reached into dorsal-motor or ventral-visual areas, reflecting the correlated category-specific sensorimotor patterns that co-occurred during action- or object-related semantic grounding, respectively. Crucially, substantial numbers of neurons of both types of distributed circuits emerged in areas interfacing between modality-preferential regions, i.e. in multimodal connection hubs, which therefore became loci of general semantic binding. By relating neuroanatomical structure and cellular-level learning mechanisms with system-level cognitive function, this model offers a neurobiological account of category-general and category-specific semantic areas based on the different cortical distributions of the underlying semantic circuits.

Mismatch negativity (MMN) to pitch change is susceptible to order-dependent bias

Pattern learning facilitates prediction about upcoming events. Within the auditory system such predictions can be studied by examining effects on a component of the auditory-evoked potential known as mismatch negativity (MMN). MMN is elicited when sound does not conform to the characteristics inferred from statistical probabilities derived from the recent past. Stable patterning in sequences elevates confidence in automatically generated perceptual inferences about what sound should come next and when. MMN amplitude should be larger when sequence is highly stable compared to when it is more volatile. This expectation has been tested using a multi-timescale paradigm. In this study, two sounds of different duration alternate roles as a predictable repetitive “standard” and rare MMN-eliciting “deviation.” The paradigm consists of sound sequences that differ in the rate at which the roles of two tones alternate, varying from slowly changing (high stability) to rapidly alternating (low stability). Previous studies using this paradigm discovered a “primacy bias” affecting how stability in patterning impacts MMN amplitude. The primacy bias refers to the observation that the effect of longer-term stability within sequences only appears to impact MMN to the sound first encountered as deviant (the sound that is rare when the sequence commences). This study determines whether this order-driven bias generalizes to sequences that contain two tones differing in pitch. By manipulating (within-subjects) the order in which sounds are encountered as deviants the data demonstrate the two defining characteristics of primacy bias: (1) sequence stability only ever impacts MMN amplitude to the first-deviant sound; and (2) within higher stability sequences, MMN is significantly larger when a sound is the first compared to when it is the second deviant. The results are consistent with a general order-driven bias exerting modulating effects on MMN amplitude over a longer timescale.

Investigating bottom-up auditory attention

Bottom-up attention is a sensory-driven selection mechanism that directs perception toward a subset of the stimulus that is considered salient, or attention-grabbing. Most studies of bottom-up auditory attention have adapted frameworks similar to visual attention models whereby local or global “contrast” is a central concept in defining salient elements in a scene. In the current study, we take a more fundamental approach to modeling auditory attention; providing the first examination of the space of auditory saliency spanning pitch, intensity and timbre; and shedding light on complex interactions among these features. Informed by psychoacoustic results, we develop a computational model of auditory saliency implementing a novel attentional framework, guided by processes hypothesized to take place in the auditory pathway. In particular, the model tests the hypothesis that perception tracks the evolution of sound events in a multidimensional feature space, and flags any deviation from background statistics as salient. Predictions from the model corroborate the relationship between bottom-up auditory attention and statistical inference, and argues for a potential role of predictive coding as mechanism for saliency detection in acoustic scenes.

Age differences in the neuroelectric adaptation to meaningful sounds

Much of what we know regarding the effect of stimulus repetition on neuroelectric adaptation comes from studies using artificially produced pure tones or harmonic complex sounds. Little is known about the neural processes associated with the representation of everyday sounds and how these may be affected by aging. In this study, we used real life, meaningful sounds presented at various azimuth positions and found that auditory evoked responses peaking at about 100 and 180 ms after sound onset decreased in amplitude with stimulus repetition. This neural adaptation was greater in young than in older adults and was more pronounced when the same sound was repeated at the same location. Moreover, the P2 waves showed differential patterns of domain-specific adaptation when location and identity was repeated among young adults. Background noise decreased ERP amplitudes and modulated the magnitude of repetition effects on both the N1 and P2 amplitude, and the effects were comparable in young and older adults. These findings reveal an age-related difference in the neural processes associated with adaptation to meaningful sounds, which may relate to older adults' difficulty in ignoring task-irrelevant stimuli.

Stimulus-specific adaptation and deviance detection in the inferior colliculus

Deviancy detection in the continuous flow of sensory information into the central nervous system is of vital importance for animals. The task requires neuronal mechanisms that allow for an efficient representation of the environment by removing statistically redundant signals. Recently, the neuronal principles of auditory deviance detection have been approached by studying the phenomenon of stimulus-specific adaptation (SSA). SSA is a reduction in the responsiveness of a neuron to a common or repetitive sound while the neuron remains highly sensitive to rare sounds (Ulanovsky et al., 2003). This phenomenon could enhance the saliency of unexpected, deviant stimuli against a background of repetitive signals. SSA shares many similarities with the evoked potential known as the “mismatch negativity,” (MMN) and it has been linked to cognitive process such as auditory memory and scene analysis (Winkler et al., 2009) as well as to behavioral habituation (Netser et al., 2011). Neurons exhibiting SSA can be found at several levels of the auditory pathway, from the inferior colliculus (IC) up to the auditory cortex (AC). In this review, we offer an account of the state-of-the art of SSA studies in the IC with the aim of contributing to the growing interest in the single-neuron electrophysiology of auditory deviance detection. The dependence of neuronal SSA on various stimulus features, e.g., probability of the deviant stimulus and repetition rate, and the roles of the AC and inhibition in shaping SSA at the level of the IC are addressed.

Past tense in the brain's time: neurophysiological evidence for dual-route processing of past-tense verbs

A controversial issue in neuro- and psycholinguistics is whether regular past-tense forms of verbs are stored lexically or generated productively by the application of abstract combinatorial schemas, for example affixation rules. The success or failure of models in accounting for this particular issue can be used to draw more general conclusions about cognition and the degree to which abstract, symbolic representations and rules are psychologically and neurobiologically real. This debate can potentially be resolved using a neurophysiological paradigm, in which alternative predictions of the brain response patterns for lexical and syntactic processing are put to the test. We used magnetoencephalography (MEG) to record neural responses to spoken monomorphemic words ('hide'), pseudowords ('smide'), regular past-tense forms ('cried') and ungrammatical (overregularised) past-tense forms ('flied') in a passive listening oddball paradigm, in which lexically and syntactically modulated stimuli are known to elicit distinct patterns of the mismatch negativity (MMN) brain response. We observed an enhanced ('lexical') MMN to monomorphemic words relative to pseudowords, but a reversed ('syntactic') MMN to ungrammatically inflected past tenses relative to grammatical forms. This dissociation between responses to monomorphemic and bimorphemic stimuli indicates that regular past tenses are processed more similarly to syntactic sequences than to lexically stored monomorphemic words, suggesting that regular past tenses are generated productively by the application of a combinatorial scheme to their separately represented stems and affixes. We suggest discrete combinatorial neuronal assemblies, which bind classes of sequentially occurring lexical elements into morphologically complex units, as the neurobiological basis of regular past tense inflection.

GABA(A)-mediated inhibition modulates stimulus-specific adaptation in the inferior colliculus

The ability to detect novel sounds in a complex acoustic context is crucial for survival. Neurons from midbrain through cortical levels adapt to repetitive stimuli, while maintaining responsiveness to rare stimuli, a phenomenon called stimulus-specific adaptation (SSA). The site of origin and mechanism of SSA are currently unknown. We used microiontophoretic application of gabazine to examine the role of GABA(A)-mediated inhibition in SSA in the inferior colliculus, the midbrain center for auditory processing. We found that gabazine slowed down the process of adaptation to high probability stimuli but did not abolish it, with response magnitude and latency still depending on the probability of the stimulus. Blocking GABA(A) receptors increased the firing rate to high and low probability stimuli, but did not completely equalize the responses. Together, these findings suggest that GABA(A)-mediated inhibition acts as a gain control mechanism that enhances SSA by modifying the responsiveness of the neuron.

Inflection in action: Semantic motor system activation to noun- and verb-containing phrases is modulated by the presence of overt grammatical markers

A recent breakthrough in understanding brain-language mechanisms is the discovery of local motor cortex activations that index specific meaning features of words, phrases and sentences. The words "talk" and "walk" activate different parts of the motor cortex, reflecting the body part relationship of actions the linguistic items are typically used to speak about. It has been suggested that such semantic motor mapping can be explained by behaviorist theories, based on conditioning mechanisms also effective in Pavlov's dog when it salivates to bell sounds. In contrast, a neurobiological approach to language predicts modulation of semantic activation by grammatical, including inflectional-morphological, information. Here, we test these competing predictions by putting action words into different phrasal contexts invoking morphosyntactic and morphophonological processes and demonstrate that semantic motor mappings are modulated by grammatical sentence properties, especially the presence of overtly realized inflectional affixes on nouns or verbs embedded in grammatical phrases. Mechanistic neuroscience theories taking into account both meaning and grammar, including morphology and syntax, are required to explain these observations. A direct comparison between phrases containing nouns and verbs revealed a tendency towards greater activation to noun phrases in left-inferior premotor cortex and posterior Broca's region (BA 44), thus questioning previous suggestions that left inferiorfrontal areas might be dedicated to verb processing per se.

Epidural Auditory Event-Related Potentials in the Rat to Frequency and duration Deviants: Evidence of Mismatch Negativity?

The capacity of the human brain to detect deviance in the acoustic environment pre-attentively is reflected in a brain event-related potential (ERP), mismatch negativity (MMN). MMN is observed in response to the presentation of rare oddball sounds that deviate from an otherwise regular pattern of frequent background standard sounds. While the primate and cat auditory cortex (AC) exhibit MMN-like activity, it is unclear whether the rodent AC produces a deviant response that reflects deviance detection in a background of regularities evident in recent auditory stimulus history or differential adaptation of neuronal responses due to rarity of the deviant sound. We examined whether MMN-like activity occurs in epidural AC potentials in awake and anesthetized rats to high and low frequency and long and short duration deviant sounds. ERPs to deviants were compared with ERPs to common standards and also with ERPs to deviants when interspersed with many different standards to control for background regularity effects. High frequency (HF) and long duration deviant ERPs in the awake rat showed evidence of deviance detection, consisting of negative displacements of the deviant ERP relative to ERPs to both common standards and deviants with many standards. The HF deviant MMN-like response was also sensitive to the extent of regularity in recent acoustic stimulation. Anesthesia in contrast resulted in positive displacements of deviant ERPs. Our results suggest that epidural MMN-like potentials to HF sounds in awake rats encode deviance in an analogous manner to the human MMN, laying the foundation for animal models of disorders characterized by disrupted MMN generation, such as schizophrenia.

An N-methyl-D-aspartate receptor agonist facilitates sleep-independent synaptic plasticity associated with working memory capacity enhancement

Working memory (WM) capacity improvement is impacted by sleep, and possibly by N-methyl-D-aspartate (NMDA) agonists such as D-cycloserine (DCS), which also affects procedural skill performance. However, the mechanisms behind these relationships are not well understood. In order to investigate the neural basis underlying relationships between WM skill learning and sleep, DCS, and both sleep and DCS together, we evaluated training-retest performances in the n-back task among healthy subjects who were given either a placebo or DCS before the task training, and then followed task training sessions either with wakefulness or sleep. DCS facilitated WM capacity enhancement only occurring after a period of wakefulness, rather than sleep, indicating that WM capacity enhancement is affected by a cellular heterogeneity in synaptic plasticity between time spent awake and time spent asleep. These findings may contribute to development, anti-aging processes, and rehabilitation of higher cognition.