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.

The hippocampus as a sorter and reverberatory integrator of sensory inputs

In entorhinal-hippocampal networks, the trisynaptic pathway, including the CA3 recurrent circuit, processes episodes of context and space. Recurrent connectivity can generate reverberatory activity, an intrinsic activity pattern of neurons that occurs after sensory inputs have ceased. However, the role of reverberatory activity in memory encoding remains incompletely understood. Here we demonstrate that in mice, synchrony between conditioned stimulus (CS) and unconditioned stimulus (US)-responsible cells occurs during the reverberatory phase, lasting for approximately 15 s, but not during CS and US inputs, in the CA1 and the reverberation is crucial for the linking of CS and US in the encoding of delay-type cued-fear memory. Retrieval-responsive cells developed primarily during the reverberatory phase. Mutant mice lacking N-methyl-D-aspartate receptors (NRs) in CA3 showed a cued-fear memory impairment and a decrease in synchronized reverberatory activities between CS- and US-responsive CA1 cells. Optogenetic CA3 silencing at the reverberatory phase during learning impaired cued-fear memory. Our findings suggest that reverberation recruits future retrieval-responsive cells via synchrony between CS- and US-responsive cells. The hippocampus uses reverberatory activity to link CS and US inputs, and avoid crosstalk during sensory inputs.

Specific Interventions and the Role of Occupational Therapy on Children With ASD

Occupational therapy focuses on children's sensory processing and modulation. This chapter approaches specific interventions on children with ASD from several perspectives. OT is based on sensory integrative approach when working with children with ASD: helping parents understand their child's behavior, helping children organize responses to sensory input. The sensory integrative approach is a formulated activity plan that helps people who haven't been able to develop their own sensory recognition program. This plan allows a child to integrate all sorts of different sensory activities in their day so they can engage in and begin to work with a wide variety of sensory inputs. This provides a wide number of benefits. Their focus and attention span increases because they won't have meltdowns from trying to process too much information; sensory integrative approach helps to rebuild/reform the child's nervous system. This allows them to physically handle more sensory input. As a result, OT has been proven effective in working with children with ASD.

A Hybrid Visual-Based SLAM Architecture: Local Filter-Based SLAM with KeyFrame-Based Global Mapping

This work presents a hybrid visual-based SLAM architecture that aims to take advantage of the strengths of each of the two main methodologies currently available for implementing visual-based SLAM systems, while at the same time minimizing some of their drawbacks. The main idea is to implement a local SLAM process using a filter-based technique, and enable the tasks of building and maintaining a consistent global map of the environment, including the loop closure problem, to use the processes implemented using optimization-based techniques. Different variants of visual-based SLAM systems can be implemented using the proposed architecture. This work also presents the implementation case of a full monocular-based SLAM system for unmanned aerial vehicles that integrates additional sensory inputs. Experiments using real data obtained from the sensors of a quadrotor are presented to validate the feasibility of the proposed approach.

insomniac links the development and function of a sleep regulatory circuit

Although many genes are known to influence sleep, when and how they impact sleep-regulatory circuits remain ill-defined. Here we show that Insomniac (Inc), a conserved adaptor for the autism-associated Cul3 ubiquitin ligase, acts in a restricted period of neuronal development to impact sleep in adult Drosophila. The loss of inc causes structural and functional alterations within the mushroom body, a center for sensory integration, associative learning, and sleep regulation. In inc mutants, mushroom body neurons are produced in excess, develop anatomical defects that impede circuit assembly, and are unable to promote sleep when activated in adulthood. Our findings link neurogenesis and postmitotic development of sleep-regulatory neurons to their adult function and suggest that developmental perturbations of circuits that couple sensory inputs and sleep may underlie sleep dysfunction in neurodevelopmental disorders.

Sensorimotor Self-organization via Circular-Reactions

Newborns demonstrate innate abilities in coordinating their sensory and motor systems through reflexes. One notable characteristic is circular reactions consisting of self-generated motor actions that lead to correlated sensory and motor activities. This paper describes a model for goal-directed reaching based on circular reactions and exocentric reference-frames. The model is built using physiologically plausible visual processing modules and arm-control neural networks. The model incorporates map representations with ego- and exo-centric reference frames for sensory inputs, vector representations for motor systems, as well as local associative learning that result from arm explorations. The integration of these modules is simulated and tested in a three-dimensional spatial environment using Unity3D. The results show that, through self-generated activities, the model self-organizes to generate accurate arm movements that are tolerant with respect to various sources of noise.

Neural dynamics of causal inference in the macaque frontoparietal circuit

ABSTRACTNatural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits that are essential for representing and updating the hidden causal structure and corresponding sensory representations during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals on the basis of their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined historical information and current multisensory signals to estimate the hidden common source and subsequently updated both the causal structure and sensory representation. Single-unit recordings in premotor and parietal cortices revealed that neural activity in premotor cortex represents the core computation of causal inference, characterizing the estimation and update of the likelihood of integrating multiple sensory inputs at a trial-by-trial level. In response to signals from premotor cortex, neural activity in parietal cortex also represents the causal structure and further dynamically updates the sensory representation to maintain consistency with the causal inference structure. Thus, our results indicate how premotor cortex integrates historical information and sensory inputs to infer hidden variables and selectively updates sensory representations in parietal cortex to support behavior. This dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body-awareness and agency.

An internal model approach for motor behavior

Interacting with the environment requires a remarkable ability to control, learn, and adapt motor skills to ever-changing conditions. The intriguing complexity involved in the process of controlling, learning, and adapting motor skills has led to the development of many theoretical approaches to explain and investigate motor behavior. This paper will present a theoretical approach built upon the top-down mode of motor control that shows substantial internal coherence and has a large and growing body of empirical evidence: The Internal Models. The Internal Models are representations of the external world within the CNS, which learn to predict this external world, simulate behaviors based on sensory inputs, and transform these predictions into motor actions. We present the Internal Models’ background based on two main structures, Inverse and Forward models, explain how they work, and present some applicability.

Dopamine, behavior, and addiction

Addictive drugs are habit-forming. Addiction is a learned behavior; repeated exposure to addictive drugs can stamp in learning. Dopamine-depleted or dopamine-deleted animals have only unlearned reflexes; they lack learned seeking and learned avoidance. Burst-firing of dopamine neurons enables learning—long-term potentiation (LTP)—of search and avoidance responses. It sets the stage for learning that occurs between glutamatergic sensory inputs and GABAergic motor-related outputs of the striatum; this learning establishes the ability to search and avoid. Independent of burst-firing, the rate of single-spiking—or “pacemaker firing”—of dopaminergic neurons mediates motivational arousal. Motivational arousal increases during need states and its level determines the responsiveness of the animal to established predictive stimuli. Addictive drugs, while usually not serving as an external stimulus, have varying abilities to activate the dopamine system; the comparative abilities of different addictive drugs to facilitate LTP is something that might be studied in the future.

Patrick David Wall. 5 April 1925—8 August 2001

Patrick (Pat) Wall was a neurophysiologist and true pioneer in the science of pain. He discovered that the sensory information arising from receptors in our body, such as those for touch and heat, could be modified, or ‘gated’, in the spinal cord by other sensory inputs and also by information descending from the brain; this meant, as is now well recognized, that the final sensory experience is not necessarily predictable from the original pain-eliciting sensory input. He used this to explain the poor relationship between injury and pain, and to illustrate the fallacy of judging what someone ‘should’ be feeling from the sensory input alone. In 1969, together with his colleague, Ron Melzack, Pat proposed the ‘gate control theory of pain’ and the circuit diagram that summarized how central spinal cord circuits can modulate sensory inputs. Later on, he began to regret that ‘goddamned diagram’, which had come to dominate his life and work, but, like all great models, it paved the way for the future. Now, over 50 years after it was first published, molecular genetic dissection of dorsal horn neuronal circuitry has indisputably confirmed that sensory inputs are indeed ‘gated’ in the spinal cord dorsal horn. Through a career that started with a medical degree in Oxford, followed by almost 20 years at Yale and MIT in the USA, and continued at University College London, Pat Wall was a highly influential, critical, creative and original thinker who revolutionized our understanding of the relationship between injury and pain, and who also became a champion for all who suffered from chronic pain.