1,721,130 research outputs found
The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task
Full text link: Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding
Multisensory bayesian inference depends on synapse maturation during training: Theoretical analysis and neural modeling implementation
Full text linkRecent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding-the idea that a population of neurons can encode probability functions to perform Bayesian inference. The model consists of two chains of unisensory neurons (auditory and visual) topologically organized. They receive the corresponding input through a plastic receptive field and reciprocally exchange plastic cross-modal synapses, which encode the spatial co-occurrence of visual-auditory inputs. A third chain of multisensory neurons performs a simple sum of auditory and visual excitations. Thework includes a theoretical part and a computer simulation study. We show how a simple rule for synapse learning (consisting of Hebbian reinforcement and a decay term) can be used during training to shrink the receptive fields and encode the unisensory likelihood functions. Hence, after training, each unisensory area realizes a maximum likelihood estimate of stimulus position (auditory or visual). In crossmodal conditions, the same learning rule can encode information on prior probability into the cross-modal synapses. Computer simulations confirm the theoretical results and show that the proposed network can realize a maximum likelihood estimate of auditory (or visual) positions in unimodal conditions and a Bayesian estimate, with moderate deviations from optimality, in cross-modal conditions. Furthermore, the model explains the ventriloquism illusion and, looking at the activity in the multimodal neurons, explains the automatic reweighting of auditory and visual inputs on a trial-by-trial basis, according to the reliability of the individual cues
A neural network for learning the meaning of objects and words from a featural representation
No full textThe present work investigates how complex semantics can be extracted from the statistics of input features, using an attractor neural network. The study is focused on how feature dominance and feature distinctiveness can be naturally coded using Hebbian training, and how similarity among objects can be managed. The model includes a lexical network (which represents word-forms) and a semantic network composed of several areas: each area is topologically organized (similarity) and codes for a different feature. Synapses in the model are created using Hebb rules with different values for pre-synaptic and post-synaptic thresholds, producing patterns of asymmetrical synapses. This work uses a simple taxonomy of schematic objects (i.e., a vector of features), with shared features (to realize categories) and distinctive features (to have individual members) with different frequency of occurrence.The trained network can solve simple object recognition tasks and object naming tasks by maintaining a distinction between categories and their members, and providing a different role for dominant features vs. marginal features. Marginal features are not evoked in memory when thinking of objects, but they facilitate the reconstruction of objects when provided as input. Finally, the topological organization of features allows the recognition of objects with some modified features
A protocol for trustworthy EEG decoding with neural networks
Full text linkDeep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of many hyperparameters, defining data pre-processing, network architecture, network training, and data augmentation. Automatic hyperparameter search is rarely performed and limited to network-related hyperparameters. Moreover, pipelines are highly sensitive to performance fluctuations due to random initialization, hindering their reliability. Here, we design a comprehensive protocol for EEG decoding that explores the hyperparameters characterizing the entire pipeline and that includes multi-seed initialization for providing robust performance estimates. Our protocol is validated on 9 datasets about motor imagery, P300, SSVEP, including 204 participants and 26 recording sessions, and on different deep learning models. We accompany our protocol with extensive experiments on the main aspects influencing it, such as the number of participants used for hyperparameter search, the split into sequential simpler searches (multi-step search), the use of informed vs. non-informed search algorithms, and the number of random seeds for obtaining stable performance. The best protocol included 2-step hyperparameter search via an informed search algorithm, with the final training and evaluation performed using 10 random initializations. The optimal trade-off between performance and computational time was achieved by using a subset of 3-5 participants for hyperparameter search. Our protocol consistently outperformed baseline state-of-the-art pipelines, widely across datasets and models, and could represent a standard approach for neuroscientists for decoding EEG in a trustworthy and reliable way
Audiovisual integration in hemianopia: A neurocomputational account based on cortico-collicular interaction
No full textHemianopic patients retain some abilities to integrate audiovisual stimuli in the blind hemifield, showing both modulation of visual perception by auditory stimuli and modulation of auditory perception by visual stimuli. Indeed, conscious detection of a visual target in the blind hemifield can be improved by a spatially coincident auditory stimulus (auditory enhancement of visual detection), while a visual stimulus in the blind hemifield can improve localization of a spatially coincident auditory stimulus (visual enhancement of auditory localization). To gain more insight into the neural mechanisms underlying these two perceptual phenomena, we propose a neural network model including areas of neurons representing the retina, primary visual cortex (V1), extrastriate visual cortex, auditory cortex and the Superior Colliculus (SC). The visual and auditory modalities in the network interact via both direct cortical-cortical connections and subcortical-cortical connections involving the SC; the latter, in particular, integrates visual and auditory information and projects back to the cortices. Hemianopic patients were simulated by unilaterally lesioning V1, and preserving spared islands of V1 tissue within the lesion, to analyze the role of residual V1 neurons in mediating audiovisual integration. The network is able to reproduce the audiovisual phenomena in hemianopic patients, linking perceptions to neural activations, and disentangles the individual contribution of specific neural circuits and areas via sensitivity analyses. The study suggests i) a common key role of SC-cortical connections in mediating the two audiovisual phenomena; ii) a different role of visual cortices in the two phenomena: auditory enhancement of conscious visual detection being conditional on surviving V1 islands, while visual enhancement of auditory localization persisting even after complete V1 damage. The present study may contribute to advance understanding of the audiovisual dialogue between cortical and subcortical structures in healthy and unisensory deficit conditions
Decoding sensorimotor information from superior parietal lobule of macaque via Convolutional Neural Networks
Full text linkDespite the well-recognized role of the posterior parietal cortex (PPC) in processing sensory information to guide action, the differential encoding properties of this dynamic processing, as operated by different PPC brain areas, are scarcely known. Within the monkey's PPC, the superior parietal lobule hosts areas V6A, PEc, and PE included in the dorso-medial visual stream that is specialized in planning and guiding reaching movements. Here, a Convolutional Neural Network (CNN) approach is used to investigate how the information is processed in these areas. We trained two macaque monkeys to perform a delayed reaching task towards 9 positions (distributed on 3 different depth and direction levels) in the 3D peripersonal space. The activity of single cells was recorded from V6A, PEc, PE and fed to convolutional neural networks that were designed and trained to exploit the temporal structure of neuronal activation patterns, to decode the target positions reached by the monkey. Bayesian Optimization was used to define the main CNN hyper-parameters. In addition to discrete positions in space, we used the same network architecture to decode plausible reaching trajectories. We found that data from the most caudal V6A and PEc areas outperformed PE area in the spatial position decoding. In all areas, decoding accuracies started to increase at the time the target to reach was instructed to the monkey, and reached a plateau at movement onset. The results support a dynamic encoding of the different phases and properties of the reaching movement differentially distributed over a network of interconnected areas. This study highlights the usefulness of neurons' firing rate decoding via CNNs to improve our understanding of how sensorimotor information is encoded in PPC to perform reaching movements. The obtained results may have implications in the perspective of novel neuroprosthetic devices based on the decoding of these rich signals for faithfully carrying out patient's intentions.(C) 2022 Published by Elsevier Ltd
Audiovisual Rehabilitation in Hemianopia: A Model-Based Theoretical Investigation
Full text linkHemianopic patients exhibit visual detection improvement in the blind field when audiovisual stimuli are given in spatiotemporally coincidence. Beyond this "online" multisensory improvement, there is evidence of long-lasting, "offline" effects induced by audiovisual training: patients show improved visual detection and orientation after they were trained to detect and saccade toward visual targets given in spatiotemporal proximity with auditory stimuli. These effects are ascribed to the Superior Colliculus (SC), which is spared in these patients and plays a pivotal role in audiovisual integration and oculomotor behavior. Recently, we developed a neural network model of audiovisual cortico-collicular loops, including interconnected areas representing the retina, striate and extrastriate visual cortices, auditory cortex, and SC. The network simulated unilateral V1 lesion with possible spared tissue and reproduced "online" effects. Here, we extend the previous network to shed light on circuits, plastic mechanisms, and synaptic reorganization that can mediate the training effects and functionally implement visual rehabilitation. The network is enriched by the oculomotor SC-brainstem route, and Hebbian mechanisms of synaptic plasticity, and is used to test different training paradigms (audiovisual/visual stimulation in eye-movements/fixed-eyes condition) on simulated patients. Results predict different training effects and associate them to synaptic changes in specific circuits. Thanks to the SC multisensory enhancement, the audiovisual training is able to effectively strengthen the retina-SC route, which in turn can foster reinforcement of the SC-brainstem route (this occurs only in eye-movements condition) and reinforcement of the SC-extrastriate route (this occurs in presence of survived V1 tissue, regardless of eye condition). The retina-SC-brainstem circuit may mediate compensatory effects: the model assumes that reinforcement of this circuit can translate visual stimuli into short-latency saccades, possibly moving the stimuli into visual detection regions. The retina-SC-extrastriate circuit is related to restitutive effects: visual stimuli can directly elicit visual detection with no need for eye movements. Model predictions and assumptions are critically discussed in view of existing behavioral and neurophysiological data, forecasting that other oculomotor compensatory mechanisms, beyond short-latency saccades, are likely involved, and stimulating future experimental and theoretical investigations
Sensory fusion: A neurocomputational approach
No full textThe merging of senses is one of the most impressive capacities of the brain. Understanding how this ability can be realized by biologically inspired neural circuits is of the greatest value not only in cognitive neuroscience, but also in neuroclinics (for the design of innovative rehabilitation procedures and neuroprostheses) and in many engineering problems. The present study describes two neural networks inspired by brain functioning, to simulate the main characteristics of audio-visual multisensory integration. The first, motivated by the present knowledge on the Superior Colliculus (a midbrain structure that drives reflexive movements) is based on a feedforward schema, and can simulate many properties of audio-visual integration. The second, inspired by perception in the cerebral cortex, also includes a feedback between the auditory and visual activities, and realizes a Bayesian inference, able to estimate stimuli positions and their causal structure in a near-optimal way
Neural Networks and Connectivity among Brain Regions
Full text linkAs is widely understood, brain functioning depends on the interaction among several neural populations, which are linked via complex connectivity circuits and work together (in antagonistic or synergistic ways) to exchange information, synchronize their activity, adapt plastically to external stimuli or internal requirements, and more generally to participate in solving multifaceted cognitive tasks [...
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