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

The Missing Link between Morphemic Assemblies and Behavioral Responses:a Bayesian Information-Theoretical model of lexical processing

We present the Bayesian Information-Theoretical (BIT) model of lexical processing: A mathematical model illustrating a novel approach to the modelling of language processes. The model shows how a neurophysiological theory of lexical processing relying on Hebbian association and neural assemblies can directly account for a variety of effects previously observed in behavioural experiments. We develop two information-theoretical measures of the distribution of usages of a morpheme or word, and use them to predict responses in three visual lexical decision datasets investigating inflectional morphology and polysemy. Our model offers a neurophysiological basis for the effects of morpho-semantic neighbourhoods. These results demonstrate how distributed patterns of activation naturally result in the arisal of symbolic structures. We conclude by arguing that the modelling framework exemplified here, is a powerful tool for integrating behavioural and neurophysiological results

Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning

In blind people, the visual cortex takes on higher cognitive functions, including language. Why this functional organisation mechanistically emerges at the neuronal circuit level is still unclear. Here, we use a biologically constrained network model implementing features of anatomical structure, neurophysiological function and connectivity of fronto-temporal-occipital areas to simulate word-meaning acquisition in visually deprived and undeprived brains. We observed that, only under visual deprivation, distributed word-related neural circuits ‘grew into’ the deprived visual areas, which therefore adopted a linguistic-semantic role. Three factors are crucial for explaining this deprivation-related growth: changes in the network’s activity balance brought about by the absence of uncorrelated sensory input, the connectivity structure of the network, and Hebbian correlation learning. In addition, the blind model revealed long-lasting spiking neural activity compared to the sighted model during word recognition, which is a neural correlate of enhanced verbal working memory. The present neurocomputational model offers a neurobiological account for neural changes followed by sensory deprivation, thus closing the gap between cellular-level mechanisms, system-level linguistic and semantic function

PREFERENTIAL ACCESS TO OBJECT SEMANTICS VIA LEXICAL PROCESSING IN THE VENTRAL STREAM OF THE BRAIN

Converging evidence supports a distributed-plus-hub view of semantic processing in the brain, in which there are distributed modular semantic sub-systems (e.g., for shape, colour, and action) connected to an amodal semantic hub. Furthermore, object semantic processing of colour and shape, and lexical reading and identification, are processed mainly along the ventral stream, while action semantic processing occurs mainly along the dorsal stream. In Experiment 1, participants read a prime word that required imagining either the object or action referent, and then named a lexical word target. In Experiments 2 and 3, participants performed a lexical decision task (LDT) with the same targets as in Experiment 1, in the presence of foils that were legal nonwords (NWs; Experiment 2; allows orthography, phonology, and semantics to contribute to responding) or pseudohomophones (PHs; Experiment 3; allows only orthography to contribute to responding). Semantic priming was similar in effect size regardless of prime type for naming and the LDT with NW foils, but was greater for object primes than action primes for the LDT with PH foils, suggesting a shared-stream advantage when the task demands focus on orthographic lexical processing. Experiment 4 used functional magnetic resonance imaging (fMRI) and identified the potential loci of shared-stream processing to regions in the ventral stream anterior to colour sensitive visual area V4 cortex and anterior to lexical and shape sensitive regions in the left fusiform gyrus, as well as in cerebellar lobule VI. Action priming showed more activation than object priming in dorsal stream motion related regions of the right parietal occipital junction, right superior occipital gyrus, and bilateral visual area V3. Experiment 5 identified structural connectivity using diffusion tensor imaging (DTI), and implicated connections from the cerebellar lobule VI to the anterior temporal lobe (ATL) semantic hub via the thalamus, supporting that this cerebellar region may act as a visual object semantic sub-system of the semantic network. The behavioural experiments demonstrate that object semantic and lexical processing are temporally shared, and the fMRI activation supports the theory that spatially shared-stream activation occurs in the ventral stream during object (but not action) priming of lexical processing. The DTI connectivity analysis supports the theory that lobule VI may act as an additional object semantic sub-system. This research suggests that shared-stream processing occurs between lexical identification and object semantic processing in the ventral stream, providing preferential access to object semantics via lexical processing. This shared-stream processing has implications for models of reading and the semantic system, which currently do not delineate between different modalities of semantic processing. The shared-stream regions identified may prove useful for pre-surgical localization of important intersections between the reading and semantic networks. These results also provide predictions that pure alexia and surface dyslexia patients with comorbid semantic deficits may be disproportionately affected by object semantic deficits compared to action semantic deficits

Biological constraints on neural network models of cognitive function

Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. Here, we discuss different types of neural models, including localist, auto-associative and hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning, to implementation of inhibition and control, along with neuroanatomical properties including area structure and local and long-range connectivity. We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, based on these brain-constrained neural models, hitherto unaddressed issues regarding the nature, localization and ontogenetic and phylogenetic development of higher brain functions. In closing, we point to possible future clinical applications of brain-constrained modelling

Finding structure in language

Since the Chomskian revolution, it has become apparent that natural language is richly structured, being naturally represented hierarchically, and requiring complex context sensitive rules to define regularities over these representations. It is widely assumed that the richness of the posited structure has strong nativist implications for mechanisms which might learn natural language, since it seemed unlikely that such structures could be derived directly from the observation of linguistic data (Chomsky 1965).This thesis investigates the hypothesis that simple statistics of a large, noisy, unlabelled corpus of natural language can be exploited to discover some of the structure which exists in natural language automatically. The strategy is to initially assume no knowledge of the structures present in natural language, save that they might be found by analysing statistical regularities which pertain between a word and the words which typically surround it in the corpus.To achieve this, various statistical methods are applied to define similarity between statistical distributions, and to infer a structure for a domain given knowledge of the similarities which pertain within it. Using these tools, it is shown that it is possible to form a hierarchical classification of many domains, including words in natural language. When this is done, it is shown that all the major syntactic categories can be obtained, and the classification is both relatively complete, and very much in accord with a standard linguistic conception of how words are classified in natural language.Once this has been done, the categorisation derived is used as the basis of a similar classification of short sequences of words. If these are analysed in a similar way, then several syntactic categories can be derived. These include simple noun phrases, various tensed forms of verbs, and simple prepositional phrases. Once this has been done, the same technique can be applied one level higher, and at this level simple sentences and verb phrases, as well as more complicated noun phrases and prepositional phrases, are shown to be derivable