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

Visualization of functional and effective connectivity underlying auditory descriptive naming

OBJECTIVE

We visualized functional and effective connectivity within specific white matter networks in response to auditory descriptive questions.

METHODS

We investigated 40 Japanese-speaking patients with focal epilepsy and estimated connectivity measures using cortical high-gamma dynamics and MRI tractography.

RESULTS

Hearing a wh-interrogative at question onset enhanced inter-hemispheric functional connectivity, with left-to-right callosal facilitatory flows between the superior-temporal gyri, contrasted by functional connectivity diminution with right-to-left callosal suppressive flows between dorsolateral prefrontal regions. Processing verbs associated with concrete objects or adverbs increased left intra-hemispheric connectivity, with bidirectional facilitatory flows through extensive white matter pathways. Questions beginning with what, compared to where, induced greater neural engagement in the left posterior inferior-frontal gyrus at question offset, linked to enhanced functional connectivity and bidirectional facilitatory flows to the temporal lobe neocortex via the arcuate fasciculus. During overt responses, inter-hemispheric functional connectivity was enhanced, with bidirectional callosal flows between Rolandic areas, and individuals with higher IQ scores exhibited less prolonged neural engagement in the left posterior middle frontal gyrus.

CONCLUSIONS

Visualization of directional neural interactions within white matter networks during overt naming is feasible.

SIGNIFICANCE

Phrase order may influence network dynamics in listeners, even when presented with auditory descriptive questions conveying similar meanings.

On the ability of standard and brain-constrained deep neural networks to support cognitive superposition: a position paper

The ability to coactivate (or "superpose") multiple conceptual representations is a fundamental function that we constantly rely upon; this is crucial in complex cognitive tasks requiring multi-item working memory, such as mental arithmetic, abstract reasoning, and language comprehension. As such, an artificial system aspiring to implement any of these aspects of general intelligence should be able to support this operation. I argue here that standard, feed-forward deep neural networks (DNNs) are unable to implement this function, whereas an alternative, fully brain-constrained class of neural architectures spontaneously exhibits it. On the basis of novel simulations, this proof-of-concept article shows that deep, brain-like networks trained with biologically realistic Hebbian learning mechanisms display the spontaneous emergence of internal circuits (cell assemblies) having features that make them natural candidates for supporting superposition. Building on previous computational modelling results, I also argue that, and offer an explanation as to why, in contrast, modern DNNs trained with gradient descent are generally unable to co-activate their internal representations. While deep brain-constrained neural architectures spontaneously develop the ability to support superposition as a result of (1) neurophysiologically accurate learning and (2) cortically realistic between-area connections, backpropagation-trained DNNs appear to be unsuited to implement this basic cognitive operation, arguably necessary for abstract thinking and general intelligence. The implications of this observation are briefly discussed in the larger context of existing and future artificial intelligence systems and neuro-realistic computational models.

GEPAF: A non-monotonic generalized activation function in neural network for improving prediction with diverse data distributions characteristics

The world today has made prescriptive analytics that uses data-driven insights to guide future actions. The distribution of data, however, differs depending on the scenario, making it difficult to interpret and comprehend the data efficiently. Different neural network models are used to solve this, taking inspiration from the complex network architecture in the human brain. The activation function is crucial in introducing non-linearity to process data gradients effectively. Although popular activation functions such as ReLU, Sigmoid, Swish, and Tanh have advantages and disadvantages, they may struggle to adapt to diverse data characteristics. A generalized activation function named the Generalized Exponential Parametric Activation Function (GEPAF) is proposed to address this issue. This function consists of three parameters expressed: α, which stands for a differencing factor similar to the mean; σ, which stands for a variance to control distribution spread; and p, which is a power factor that improves flexibility; all these parameters are present in the exponent. When p=2, the activation function resembles a Gaussian function. Initially, this paper describes the mathematical derivation and validation of the properties of this function mathematically and graphically. After this, the GEPAF function is practically implemented in real-world supply chain datasets. One dataset features a small sample size but exhibits high variance, while the other shows significant variance with a moderate amount of data. An LSTM network processes the dataset for sales and profit prediction. The suggested function performs better than popular activation functions when a comparative analysis of the activation function is performed, showing at least 30% improvement in regression evaluation metrics and better loss decay characteristics.

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.

Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks

Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition architecture, compatible and scalable with deep learning frameworks, and demonstrate that end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Significant cross-frequency couplings, indicative of these oscillations, are measured within and across network layers during speech processing, whereas no such interactions are observed when handling background noise inputs. Furthermore, our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronizing neural activity to improve recognition performance. Overall, on top of developing our understanding of synchronization phenomena notably observed in the human auditory pathway, our architecture exhibits dynamic and efficient information processing, with relevance to neuromorphic technology.

Theoretical strategies for an embodied cognitive neuroscience: Mechanistic explanations of brain-body-environment systems

Cognitive neuroscience seeks to explain mind, brain, and behavior. But how do we generate explanations? In this integrative theoretical paper, we review the commitments of the 'New Mechanist' movement within the philosophy of science, focusing specifically on the role of mechanistic models in scientific explanation. We highlight how this approach differs from other explanatory approaches within the field, showing its unique contributions to the efforts of scientific explanation. We then argue that the commitments of the Embodied Cognition framework converge with the commitments of the New Mechanist movement in a way that provides a necessary explanatory strategy available to cognitive neuroscience. We then discuss a number of consequences of this convergence, including issues related to the inadequacy of statistical prediction, neuroscientific reduction, the autonomy of psychology from neuroscience, and psychological and neuroscientific ontology. We hope that our integrative thesis provides researchers with a theoretical strategy for an embodied cognitive neuroscience.

On the embodied nature of knowledge: From neurons to numbers

Interdisciplinary investigations of the human mind through the cognitive sciences have identified a key role of the body in representing knowledge. After characterizing knowledge at grounded, embodied, and situated levels, number knowledge is analyzed from this hierarchical perspective. Lateralized cortical processing of coarse versus fine detail is identified as a grounding substrate for the population stereotype few/left, many/right, which then contributes to number-related sensory and motor biases at the embodied and situated levels. Implications of this perspective for education and rehabilitation are discussed.

Active Inference and Social Actors: Towards a Neuro-Bio-Social Theory of Brains and Bodies in Their Worlds

Although research including biological concepts and variables has gained more prominence in sociology, progress assimilating the organ of experience, the brain, has been theoretically and technically challenging. Formal uptake and assimilation have thus been slow. Within psychology and neuroscience, the traditional brain, which has made brief appearances in sociological research, is a "bottom-up" processor in which sensory signals are passed up the neural hierarchy where they are eventually cognitively and emotionally processed, after which actions and responses are generated. In this paper, we introduce the Active Inference Framework (AIF), which casts the brain as a Bayesian "inference engine" that tests its "top-down" predictive models against "bottom-up" sensory error streams in its attempts to resolve uncertainty and make the world more predictable. After assembling and presenting key concepts in the AIF, we describe an integrated neuro-bio-social model that prioritizes the microsociological assertion that the scene of action is the situation, wherein brains enculturate. Through such social dynamics, enculturated brains share models of the world with one another, enabling collective realities that disclose the actions afforded in those times and places. We conclude by discussing this neuro-bio-social model within the context of exemplar sociological research areas, including the sociology of stress and health, the sociology of emotions, and cognitive cultural sociology, all areas where the brain has received some degree of recognition and incorporation. In each case, sociological insights that do not fit naturally with the traditional brain model emerge intuitively from the predictive AIF model, further underscoring the interconnections and interdependencies between these areas, while also providing a foundation for a probabilistic sociology.

Regionally specific cortical lateralization of abstract and concrete verb processing: Magnetic mismatch negativity study

The neural underpinnings of processing concrete and abstract semantics remain poorly understood. Previous fMRI studies have shown that multimodal and amodal neural networks respond differentially to different semantic types; importantly, abstract semantics activates more left-lateralized networks, as opposed to more bilateral activity for concrete words. Due to the lack of temporal resolution, these fMRI results do not allow to easily separate language- and task-specific brain responses and to disentangle early processing stages from later post-comprehension phenomena. To tackle this, we used magnetoencephalography (MEG), a time-resolved neuroimaging technique, in combination with a task-free oddball mismatch negativity (MMN) paradigm, an established approach to tracking early automatic activation of word-specific memory traces in the brain. We recorded the magnetic MMN responses in 30 healthy adults to auditorily presented abstract and concrete action verbs to assess lateralization of word-specific lexico-semantic processing in a set of neocortical areas. We found that MMN responses to these stimuli showed different lateralization patterns of activity in the upper limb motor area (BA4) and parts of Broca's area (BA45/BA47) within ∼100-350 ms after the word disambiguation point. Importantly, the greater leftward response lateralization for abstract semantics was due to the lesser involvement of the right-hemispheric homologues, not increased left-hemispheric activity. These findings suggest differential region-specific involvement of bilateral sensorimotor systems already in the early automatic stages of processing abstract and concrete action semantics.