Active Role of Self-Sustained Neural Activity on Sensory Input Processing: A Minimal Theoretical Model

A growing body of work has demonstrated the importance of ongoing oscillatory neural activity in sensory processing and the generation of sensorimotor behaviors. It has been shown, for several different brain areas, that sensory-evoked neural oscillations are generated from the modulation by sensory inputs of inherent self-sustained neural activity (SSA). This letter contributes to that strand of research by introducing a methodology to investigate how much of the sensory-evoked oscillatory activity is generated by SSA and how much is generated by sensory inputs within the context of sensorimotor behavior in a computational model. We develop an abstract model consisting of a network of three Kuramoto oscillators controlling the behavior of a simulated agent performing a categorical perception task. The effects of sensory inputs and SSAs on sensory-evoked oscillations are quantified by the cross product of velocity vectors in the phase space of the network under different conditions (disconnected without input, connected without input, and connected with input). We found that while the agent is carrying out the task, sensory-evoked activity is predominantly generated by SSA (93.10%) with much less influence from sensory inputs (6.90%). Furthermore, the influence of sensory inputs can be reduced by 10.4% (from 6.90% to 6.18%) with a decay in the agent's performance of only 2%. A dynamical analysis shows how sensory-evoked oscillations are generated from a dynamic coupling between the level of sensitivity of the network and the intensity of the input signals. This work may suggest interesting directions for neurophysiological experiments investigating how self-sustained neural activity influences sensory input processing, and ultimately affects behavior.

Top-down predictions of visual features dynamically reverse their bottom-up processing in the occipito-ventral pathway to facilitate stimulus disambiguation and behavior

The prevalent conception of vision-for-categorization suggests an interplay of two dynamic flows of information within the occipito-ventral pathway. The bottom-up flow progressively reduces the high-dimensional input into a lower-dimensional representation that is compared with memory to produce categorization behavior. The top-down flow predicts category information (i.e. features) from memory that propagates down the same hierarchy to facilitate input processing and behavior. However, the neural mechanisms that support such dynamic feature propagation up and down the visual hierarchy and how they facilitate behavior remain unclear. Here, we studied them using a prediction experiment that cued participants (N = 11) to the spatial location (left vs. right) and spatial frequency (SF, Low, LSF, vs. High, HSF) contents of an upcoming Gabor patch. Using concurrent MEG recordings of each participant's neural activity, we compared the top-down flow of representation of the predicted Gabor contents (i.e. left vs. right; LSF vs. HSF) to their bottom-up flow. We show (1) that top-down prediction improves speed of categorization in all participants, (2) the top-down flow of prediction reverses the bottom-up representation of the Gabor stimuli, going from deep right fusiform gyrus sources down to occipital cortex sources contra-lateral to the expected Gabor location and (3) that predicted Gabors are better represented when the stimulus is eventually shown, leading to faster categorizations. Our results therefore trace the dynamic top-down flow of a predicted visual content that chronologically and hierarchically reversed bottom-up processing, further facilitates visual representations in early visual cortex and subsequent categorization behavior.

Extending ONTAgri with Service-Oriented Architecture towards Precision Farming Application

The computer science perspective of ontology refers to ontology as a technology, however, with a different perspective in terms of interrogations and concentrations to construct engineering models of reality. Agriculture-centered architectures are among rich sources of knowledge that are developed, preserved, and released for farmers and agro professionals. Many researchers have developed different variants of existing ontology-based information systems. These systems are primarily picked agriculture-related ontological strategies based on activities such as crops, weeds, implantation, irrigation, and planting, to name a few. By considering the limitations on agricultural resources in the ONTAgri scenario, in this paper, an extension of ontology is proposed. The extended ONTAgri is a service-oriented architecture that connects precision farming with both local and global decision-making methods. These decision-making methods are connected with the Internet of Things systems in parallel for the input processing of system ontology. The proposed architecture fulfills the requirements of Agriculture 4.0. The significance of the proposed approach aiming to solve a multitude of agricultural problems being faced by the farmers is successfully demonstrated through SPARQL queries.

Tract Profiles of The Cerebellar Peduncles In Children Who Stutter

Cerebellar-cortical loops comprise critical neural circuitry that supports self-initiated movements and motor adjustments in response to perceived errors, functions that are affected in stuttering. It is unknown whether structural aspects of cerebellar circuitry are affected in stuttering, in particular in children close to symptom onset. Here we examined white matter diffusivity characteristics of the three cerebellar peduncles (CP) based on diffusion MRI (dMRI) data collected from 41 children who stutter (CWS) and 42 controls in the 3-11 year range. We hypothesized that CWS would exhibit decreased fractional anisotropy (FA) in the right CPs given the contralateral connectivity of the cerebellar-cortical loops and past reports of structural differences in left cortical areas in stuttering speakers. Automatic Fiber Quantification (AFQ) was used to track and segment cerebellar white matter pathways and to extract diffusivity measures. We found significant group differences for FA in the right Inferior CP (ICP) only: controls showed significantly higher FA in the right ventral ICP compared to CWS, controlling for age, sex, and verbal IQ. Furthermore, FA of right ICP was negatively correlated with stuttering frequency in CWS. These results suggest an early developmental difference in the right ICP for CWS compared to age-matched peers, which may indicate an alteration in error processing, a function previously linked to the ICP. Lower FA here may impact error monitoring and sensory input processing to guide motor corrections. Further longitudinal investigations in children may provide additional insights into how CP development links to stuttering persistence and recovery.

The brain and its time: intrinsic neural timescales are key for input processing

We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs’ stochastics with the ongoing temporal statistics of the brain’s neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.

Towards a Life Cycle for IoT Applications Development

An IoT system is specified in terms of sensors/actuators and communication between them. However, we argue for performing upstream activities of the Systems Development Life Cycle during IoT application development. We propose the conceptual design stage followed by conversion to an IoT system and show that we need concepts for autonomy, perception, input processing, changing the external world, maintenance of historical information and communication. To handle these, we use the notion of communicative agents, COMMAGs and develop the Communicative Agent Model. We show conversion of this model into the IoT system to be.

Towards a Life Cycle for IoT Applications Development

An IoT system is specified in terms of sensors/actuators and communication between them. However, we argue for performing upstream activities of the Systems Development Life Cycle during IoT application development. We propose the conceptual design stage followed by conversion to an IoT system and show that we need concepts for autonomy, perception, input processing, changing the external world, maintenance of historical information and communication. To handle these, we use the notion of communicative agents, COMMAGs and develop the Communicative Agent Model. We show conversion of this model into the IoT system to be.