Neurobiological Causal Models of Language Processing.

The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the "machine language" of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.

The Tripod neuron: a minimal structural reduction of the dendritic tree.

Neuron models with explicit dendritic dynamics have shed light on mechanisms for coincidence detection, pathway selection and temporal filtering. However, it is still unclear which morphological and physiological features are required to capture these phenomena. In this work, we introduce the Tripod neuron model and propose a minimal structural reduction of the dendritic tree that is able to reproduce these computations. The Tripod is a three-compartment model consisting of two segregated passive dendrites and a somatic compartment modelled as an adaptive, exponential integrate-and-fire neuron. It incorporates dendritic geometry, membrane physiology and receptor dynamics as measured in human pyramidal cells. We characterize the response of the Tripod to glutamatergic and GABAergic inputs and identify parameters that support supra-linear integration, coincidence-detection and pathway-specific gating through shunting inhibition. Following NMDA spikes, the Tripod neuron generates plateau potentials whose duration depends on the dendritic length and the strength of synaptic input. When fitted with distal compartments, the Tripod encodes previous activity into a dendritic depolarized state. This dendritic memory allows the neuron to perform temporal binding, and we show that it solves transition and sequence detection tasks on which a single-compartment model fails. Thus, the Tripod can account for dendritic computations previously explained only with more detailed neuron models or neural networks. Due to its simplicity, the Tripod neuron can be used efficiently in simulations of larger cortical circuits. KEY POINTS: We present a neuron model, called the Tripod, with two segregated dendritic branches that are connected to an axosomatic compartment. Each branch implements inhibitory GABAergic and excitatory glutamatergic synaptic transmission, including voltage-gated NMDA receptors. Dendrites are modelled on relevant geometric and physiological parameters measured in human pyramidal cells. The neuron reproduces classical dendritic computations, such as coincidence detection and pathway selection via shunting inhibition, that are beyond the scope of point-neuron models. Under some conditions, dendritic NMDA spikes cause plateau potentials, and we show that they provide a form of short-term memory which is useful for sequence recognition. The dendritic structure of the Tripod neuron is sufficiently simple to be integrated into efficient network simulations and studied in a broad functional context.

Neuronal spike-rate adaptation supports working memory in language processing.

Language processing involves the ability to store and integrate pieces of information in working memory over short periods of time. According to the dominant view, information is maintained through sustained, elevated neural activity. Other work has argued that short-term synaptic facilitation can serve as a substrate of memory. Here we propose an account where memory is supported by intrinsic plasticity that downregulates neuronal firing rates. Single neuron responses are dependent on experience, and we show through simulations that these adaptive changes in excitability provide memory on timescales ranging from milliseconds to seconds. On this account, spiking activity writes information into coupled dynamic variables that control adaptation and move at slower timescales than the membrane potential. From these variables, information is continuously read back into the active membrane state for processing. This neuronal memory mechanism does not rely on persistent activity, excitatory feedback, or synaptic plasticity for storage. Instead, information is maintained in adaptive conductances that reduce firing rates and can be accessed directly without cued retrieval. Memory span is systematically related to both the time constant of adaptation and baseline levels of neuronal excitability. Interference effects within memory arise when adaptation is long lasting. We demonstrate that this mechanism is sensitive to context and serial order which makes it suitable for temporal integration in sequence processing within the language domain. We also show that it enables the binding of linguistic features over time within dynamic memory registers. This work provides a step toward a computational neurobiology of language.

Language ERPs reflect learning through prediction error propagation.

Event-related potentials (ERPs) provide a window into how the brain is processing language. Here, we propose a theory that argues that ERPs such as the N400 and P600 arise as side effects of an error-based learning mechanism that explains linguistic adaptation and language learning. We instantiated this theory in a connectionist model that can simulate data from three studies on the N400 (amplitude modulation by expectancy, contextual constraint, and sentence position), five studies on the P600 (agreement, tense, word category, subcategorization and garden-path sentences), and a study on the semantic P600 in role reversal anomalies. Since ERPs are learning signals, this account explains adaptation of ERP amplitude to within-experiment frequency manipulations and the way ERP effects are shaped by word predictability in earlier sentences. Moreover, it predicts that ERPs can change over language development. The model provides an account of the sensitivity of ERPs to expectation mismatch, the relative timing of the N400 and P600, the semantic nature of the N400, the syntactic nature of the P600, and the fact that ERPs can change with experience. This approach suggests that comprehension ERPs are related to sentence production and language acquisition mechanisms.

Evaluation of sentinel lymph node biopsy prior to axillary lymph node dissection: the role of isolated tumor cells/micrometastases and multifocality/multicentricity-a retrospective study of 1214 breast cancer patients.

PURPOSE: Sentinel lymph node biopsy (SLNB) alone has thus become an accepted surgical approach for patients with limited axillary metastatic disease. We investigated to what extent isolated tumor cells (ITC) or micrometastasis in SLNBs is associated with proven tumor cells or metastasis in non-sentinel lymph nodes. Furthermore, we investigated the feasibility of SLNB in multifocal and multicentric tumors as both entities have been considered a contraindication for this technique. METHODS: 1214 women suffering from T1 and T2 invasive breast cancer, with clinically and sonographically insuspect axillary status and undergoing primary breast cancer surgery including SLNB and axillary staging in case of SLN (sentinel lymph node) metastases, were recruited into this multicentered study. RESULTS: ITC and micrometastases were found in 2.01 and 21.4% of patients with SLN metastases (n = 299). Among patients with sentinel micrometastases, 4.7% showed further axillary micrometastases, while only two patients (3.1%) had two axillary macrometastases. Multifocal and multicentric tumors were diagnosed in 9.3 and 2.6% of our patients who at least had one SLN resected, respectively. Detection rates of SLNs did not differ between the cohorts suffering from unicentric and multifocal or multicentric disease. Moreover, the portion of tumor-free SLNs, the number of SLNs with metastasis as well as the mean number of resected SLNs did not differ. CONCLUSIONS: No patient with sentinel node micrometastases showed more than two axillary macrometastases. Multifocal and multicentric disease is no contraindication for SLNB.

Evaluation of Sentinel Lymph Node Biopsy and Axillary Lymph Node Dissection for Breast Cancer Treatment Concepts - a Retrospective Study of 1,214 Breast Cancer Patients.

BACKGROUND: Most breast cancer patients require lumpectomy with axillary sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). The ACOSOG Z0011-trial failed to detect significant effects of ALND on disease-free and overall survival among patients with limited sentinel lymph node (SLN) metastases. Intense dose-dense chemotherapy and supraclavicular fossa radiation (SFR) are indicated for patients with extensive axillary metastases. In this multicentered study, we investigated the relevance of ALND after positive SLNB to determine adequate adjuvant therapy. METHODS: We retrospectively analyzed data from 1,214 patients with clinically nodal negative T1-T2 invasive breast cancer undergoing surgery at Hanau City Hospital Breast cancer center. RESULTS: 681 patients underwent ALND after SLNB. 20 patients (8.5%) from the group with 1 or 2 SLN metastases (n = 236) showed more than 3 lymph node metastases after ALND. 13 patients (31.7%) from the group with more than 2 SLN metastases (n = 41) were diagnosed with a minimum of 4 axillary lymph node metastases after ALND. CONCLUSIONS: In 8.5% of the patients with 1 or 2 SLN metastases, ALND detected more than 3 macrometastases, setting the indication for intense dose-dense chemotherapy and SFR. More than 2 SLN metastases, T stage and grading predict lymph node metastases.

Meaningful questions: The acquisition of auxiliary inversion in a connectionist model of sentence production.

Nativist theories have argued that language involves syntactic principles which are unlearnable from the input children receive. A paradigm case of these innate principles is the structure dependence of auxiliary inversion in complex polar questions (Chomsky, 1968, 1975, 1980). Computational approaches have focused on the properties of the input in explaining how children acquire these questions. In contrast, we argue that messages are structured in a way that supports structure dependence in syntax. We demonstrate this approach within a connectionist model of sentence production (Chang, 2009) which learned to generate a range of complex polar questions from a structured message without positive exemplars in the input. The model also generated different types of error in development that were similar in magnitude to those in children (e.g., auxiliary doubling, Ambridge, Rowland, & Pine, 2008; Crain & Nakayama, 1987). Through model comparisons we trace how meaning constraints and linguistic experience interact during the acquisition of auxiliary inversion. Our results suggest that auxiliary inversion rules in English can be acquired without innate syntactic principles, as long as it is assumed that speakers who ask complex questions express messages that are structured into multiple propositions.

Reservoir computing and the Sooner-is-Better bottleneck.

Prior language input is not lost but integrated with the current input. This principle is demonstrated by "reservoir computing": Untrained recurrent neural networks project input sequences onto a random point in high-dimensional state space. Earlier inputs can be retrieved from this projection, albeit less reliably so as more input is received. The bottleneck is therefore not "Now-or-Never" but "Sooner-is-Better."

What baboons can (not) tell us about natural language grammars.

Rey et al. (2012) present data from a study with baboons that they interpret in support of the idea that center-embedded structures in human language have their origin in low level memory mechanisms and associative learning. Critically, the authors claim that the baboons showed a behavioral preference that is consistent with center-embedded sequences over other types of sequences. We argue that the baboons' response patterns suggest that two mechanisms are involved: first, they can be trained to associate a particular response with a particular stimulus, and, second, when faced with two conditioned stimuli in a row, they respond to the most recent one first, copying behavior they had been rewarded for during training. Although Rey et al. (2012) 'experiment shows that the baboons' behavior is driven by low level mechanisms, it is not clear how the animal behavior reported, bears on the phenomenon of Center Embedded structures in human syntax. Hence, (1) natural language syntax may indeed have been shaped by low level mechanisms, and (2) the baboons' behavior is driven by low level stimulus response learning, as Rey et al. propose. But is the second evidence for the first? We will discuss in what ways this study can and cannot give evidential value for explaining the origin of Center Embedded recursion in human grammar. More generally, their study provokes an interesting reflection on the use of animal studies in order to understand features of the human linguistic system.

Do lemmas speak German? A verb position effect in German structural priming.

Lexicalized theories of syntax often assume that verb-structure regularities are mediated by lemmas, which abstract over variation in verb tense and aspect. German syntax seems to challenge this assumption, because verb position depends on tense and aspect. To examine how German speakers link these elements, a structural priming study was performed which varied syntactic structure, verb position (encoded by tense and aspect), and verb overlap. Abstract structural priming was found, both within and across verb position, but priming was larger when the verb position was the same between prime and target. Priming was boosted by verb overlap, but there was no interaction with verb position. The results can be explained by a lemma model where tense and aspect are linked to structural choices in German. Since the architecture of this lemma model is not consistent with results from English, a connectionist model was developed which could explain the cross-linguistic variation in the production system. Together, these findings support the view that language learning plays an important role in determining the nature of structural priming in different languages.

Getting real about semantic illusions: rethinking the functional role of the P600 in language comprehension.

In traditional theories of language comprehension, syntactic and semantic processing are inextricably linked. This assumption has been challenged by the 'semantic illusion effect' found in studies using event related brain potentials. Semantically anomalous sentences did not produce the expected increase in N400 amplitude but rather one in P600 amplitude. To explain these findings, complex models have been devised in which an independent semantic processing stream can arrive at a sentence interpretation that may differ from the interpretation prescribed by the syntactic structure of the sentence. We review five such multi-stream models and argue that they do not account for the full range of relevant results because they assume that the amplitude of the N400 indexes some form of semantic integration. Based on recent evidence we argue that N400 amplitude might reflect the retrieval of lexical information from memory. On this view, the absence of an N400-effect in semantic illusion sentences can be explained in terms of priming. Furthermore, we suggest that semantic integration, which has previously been linked to the N400 component, might be reflected in the P600 instead. When combined, these functional interpretations result in a single-stream account of language processing that can explain all of the Semantic Illusion data.