Disentangling narrow and coarse semantic networks in the brain: The role of computational models of word meaning

Theories of embodied cognition consider language understanding as intimately linked to sensory and motor processes. Here we review evidence from kinematic and electrophysiological studies for the idea that processing of words referring to bodily actions, even when subliminally presented, recruits the same motor regions that are involved in motor control. We further discuss the functional role of the motor system in action word retrieval in light of neuropsychological data showing modulation of masked priming effects for action verbs in Parkinson’s patients as a function of dopaminergic treatment. Finally, a neuroimaging study revealing semantic somatotopy in the motor cortex during reading of idioms that include action words is presented. Altogether these findings provide strong arguments that semantic mechanisms are grounded in action-perception systems of the brain. They support the existence of common brain signatures to action words, even when embedded in idiomatic sentences, and motor action. They further suggest that motor schemata reflecting word meaning contribute to lexico-semantic retrieval of action words.

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Electrical synapses and transient signals in feedforward canonical circuits

10.1101/394593 ◽

2018 ◽

Author(s):

Tuan Pham ◽

Julie S. Haas

Keyword(s):

Signal Processing ◽

Computational Models ◽

Transient Signal ◽

Electrical Synapses ◽

Transient Signals ◽

Network Information ◽

Growing Body ◽

Neuronal Processing ◽

The Brain

AbstractAs information about the world traverses the brain, the signals exchanged between neurons are passed and modulated by synapses, or specialized contacts between neurons. While neurotransmitter-based synapses tend to be either relay excitatory or inhibitory pulses of influence on the postsynaptic neuron, electrical synapses, composed of plaques of gap junction channels, are always-on transmitters that can either excite or inhibit a coupled neighbor. A growing body of evidence indicates that electrical synapses, similar to their chemical counterparts, are modified in strength during physiological neuronal activity. The synchronizing role of electrical synapses in neuronal oscillations has been well established, but their impact on transient signal processing in the brain is much less understood. Here we constructed computational models based on the canonical feedforward neuronal circuit, and included electrical synapses between inhibitory interneurons. We provided discrete closely-timed inputs to the circuits, and characterize the influence of electrical synapses on both the subthreshold summation and spike trains in the output neuron. Our simulations highlight the diverse and powerful roles that electrical synapses play even in simple circuits. Because these canonical circuits are represented widely throughout the brain, we expect that these are general principles for the influence of electrical synapses on transient signal processing across the brain.Author SummaryThe role that electrical synapses play in neural oscillations, network synchronization and rhythmicity is well established, but their role neuronal processing of transient inputs is much less understood. Here we used computational models of canonical feedforward circuits and networks to investigate how the strength of electrical synapses regulates the flow of transient signals passing through those circuits. We show that because the influence of electrical synapses on coupled neighbors can be either inhibitory or excitatory, their role in network information processing is heterogeneous.. Because of the widespread existence of electrical synapses between interneurons as well as a growing body of evidence for their plasticity, we expect such effects play a significant role in how the brain processes transient inputs.

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A Computational Neuroscience Perspective on the Change Process in Psychotherapy

Neuroscience of Enduring Change ◽

10.1093/oso/9780190881511.003.0015 ◽

2020 ◽

pp. 395-432 ◽

Cited By ~ 2

Author(s):

Ryan Smith ◽

Richard D. Lane ◽

Lynn Nadel ◽

Michael Moutoussis

Keyword(s):

Computational Neuroscience ◽

Change Process ◽

Computational Models ◽

Therapeutic Process ◽

Repetitive Behavior ◽

Predictive Coding ◽

Emotional Awareness ◽

Limited Application ◽

The Brain

The application of computational neuroscience models to mental disorders has given rise to the emerging field of computational psychiatry. To date, however, there has been limited application of this approach to understanding the change process in psychotherapy. This chapter reviews leading approaches in computational neuroscience: predictive coding, active inference, and reinforcement learning. We then provide examples of how these complimentary approaches can be used to model a range of clinical phenomena and associated clinical interventions, including those associated with emotional awareness, specific phobia, maladaptive self-related beliefs, maladaptive repetitive behavior patterns, and the role of re-experiencing negative affect in the therapeutic process. The authors illustrate how this perspective can provide additional insights into the nature of the types of memories (cast as parameters in computational models) that maintain psychopathology, how they may be instantiated in the brain, and how new experiences in psychotherapy can alter/update these memories in a manner that can be quantitatively modeled. The authors conclude that the computational perspective represents a unique level of description that compliments that of the integrated memory model in a synergistic and informative manner.

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Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008737 ◽

2021 ◽

Vol 17 (2) ◽

pp. e1008737

Author(s):

Carlos Coronel-Oliveros ◽

Rodrigo Cofré ◽

Patricio Orio

Keyword(s):

Computational Models ◽

Functional Organization ◽

Brain Activity ◽

Functional Integration ◽

Whole Brain ◽

Inhibitory Circuits ◽

Inhibitory Feedback ◽

Biophysical Mechanism ◽

The Brain

Segregation and integration are two fundamental principles of brain structural and functional organization. Neuroimaging studies have shown that the brain transits between different functionally segregated and integrated states, and neuromodulatory systems have been proposed as key to facilitate these transitions. Although whole-brain computational models have reproduced this neuromodulatory effect, the role of local inhibitory circuits and their cholinergic modulation has not been studied. In this article, we consider a Jansen & Rit whole-brain model in a network interconnected using a human connectome, and study the influence of the cholinergic and noradrenergic neuromodulatory systems on the segregation/integration balance. In our model, we introduce a local inhibitory feedback as a plausible biophysical mechanism that enables the integration of whole-brain activity, and that interacts with the other neuromodulatory influences to facilitate the transition between different functional segregation/integration regimes in the brain.

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Spatialization of Time in the Entorhinal-Hippocampal System

Frontiers in Behavioral Neuroscience ◽

10.3389/fnbeh.2021.807197 ◽

2022 ◽

Vol 15 ◽

Author(s):

Troy M. Houser

Keyword(s):

Empirical Evidence ◽

Functional Role ◽

Spatial Representation ◽

Computational Models ◽

Spatial Information ◽

Cognitive Representations ◽

Hippocampal Activity ◽

The Brain

The functional role of the entorhinal-hippocampal system has been a long withstanding mystery. One key theory that has become most popular is that the entorhinal-hippocampal system represents space to facilitate navigation in one’s surroundings. In this Perspective article, I introduce a novel idea that undermines the inherent uniqueness of spatial information in favor of time driving entorhinal-hippocampal activity. Specifically, by spatializing events that occur in succession (i.e., across time), the entorhinal-hippocampal system is critical for all types of cognitive representations. I back up this argument with empirical evidence that hints at a role for the entorhinal-hippocampal system in non-spatial representation, and computational models of the logarithmic compression of time in the brain.

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The role of transporters and synaptic cleft morphology in glutamate and GABA homeostasis and their effect on neuronal function

10.1101/670844 ◽

2019 ◽

Author(s):

Ghanim Ullah

Keyword(s):

Computational Models ◽

Synaptic Cleft ◽

Signal Integration ◽

Neuronal Function ◽

Postsynaptic Receptors ◽

Neuronal Functions ◽

The Brain ◽

And Diffusion ◽

Synaptic Responses

AbstractThe spatiotemporal dynamics of glutamate and gama-aminobutyric acide (GABA) in the synaptic cleft plays a key role in the signal integration in the brain. Since there is no extracellular metabolism of glutamate and GABA, cellular uptake through transporters and diffusion to extracellular space (ECS) regulates the concentration of both neurotransmitters in the cleft. We use the most up to date information about the transporters and synaptic cleft to model the homeostasis of both glutamate and GABA. We show that the models can be used to investigate the role played by different isoforms of transporters, uptake by different neuronal compartments or glia cells, and key parameters determining the morphology of synaptic cleft in the neurotransmitter concentration in the cleft and ECS, and how they shape synaptic responses through postsynaptic receptors. We demonstrate the utility of our models by application to simple neuronal networks and showing that varying the neurotransmitter uptake capacity and synaptic cleft parameters within experimentally observed range can lead to significant changes in neuronal behavior such as the transition of the network between gamma and beta rhythms. The modular form of the models allows easy extension in the future and integration with other computational models of normal and pathological neuronal functions.

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On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs

Frontiers in Neuroscience ◽

10.3389/fnins.2020.603796 ◽

2021 ◽

Vol 14 ◽

Author(s):

Mehul Rastogi ◽

Sen Lu ◽

Nafiul Islam ◽

Abhronil Sengupta

Keyword(s):

Computational Models ◽

Fault Tolerant ◽

Spike Timing ◽

Underlying Structure ◽

Neuromorphic Computing ◽

Learning Platforms ◽

Computational Paradigm ◽

Self Repair ◽

The Brain

Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in the algorithm and hardware design of next-generation machine learning platforms. This work goes beyond the focus of current neuromorphic computing architectures on computational models for neuron and synapse to examine other computational units of the biological brain that might contribute to cognition and especially self-repair. We draw inspiration and insights from computational neuroscience regarding functionalities of glial cells and explore their role in the fault-tolerant capacity of Spiking Neural Networks (SNNs) trained in an unsupervised fashion using Spike-Timing Dependent Plasticity (STDP). We characterize the degree of self-repair that can be enabled in such networks with varying degree of faults ranging from 50 to 90% and evaluate our proposal on the MNIST and Fashion-MNIST datasets.

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The Role of Mitochondria in the Genesis of Neuroaxonal Dystrophy in the Brains of Aging Mice

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100115481 ◽

1975 ◽

Vol 33 ◽

pp. 372-373

Author(s):

J.E. Johnson

Keyword(s):

Electron Microscopy ◽

Present Report ◽

Light And Electron Microscopy ◽

Dorsal Column ◽

Neuroaxonal Dystrophy ◽

Nucleus Gracilis ◽

Dystrophic Axons ◽

The Brain ◽

Nucleus Cuneatus

Although neuroaxonal dystrophy (NAD) has been examined by light and electron microscopy for years, the nature of the components in the dystrophic axons is not well understood. The present report examines nucleus gracilis and cuneatus (the dorsal column nuclei) in the brain stem of aging mice.Mice (C57BL/6J) were sacrificed by aldehyde perfusion at ages ranging from 3 months to 23 months. Several brain areas and parts of other organs were processed for electron microscopy.At 3 months of age, very little evidence of NAD can be discerned by light microscopy. At the EM level, a few axons are found to contain dystrophic material. By 23 months of age, the entire nucleus gracilis is filled with dystrophic axons. Much less NAD is seen in nucleus cuneatus by comparison. The most recurrent pattern of NAD is an enlarged profile, in the center of which is a mass of reticulated material (reticulated portion; or RP).

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