Adaptive learning rate in dynamical binary environments: the signature of adaptive information processing.

Adaptive mechanisms of learning models play critical roles in interpreting adaptive behavior of humans and animals. Different learning models, varying from Bayesian models, deep learning or regression models to reward-based reinforcement learning models, adopt similar update rules. These update rules can be reduced to the same generalized mathematical form: the Rescorla-Wagner equation.

Causal contributions of cell-type-specific circuits in the posterior dorsal striatum to auditory decision-making.

In the dorsal striatum (DS), the direct- and indirect-pathway striatal projection neurons (dSPNs and iSPNs) play crucial opposing roles in controlling actions. However, it remains unclear whether and how dSPNs and iSPNs provide distinct and specific contributions to decision-making, a process transforming sensory inputs to actions. Here, we perform causal interrogations on the roles of dSPNs and iSPNs in the posterior DS (pDS) in auditory-guided decision-making.

Reviving Bistable Perception in Patients With Depression by Decreasing the Overestimation of Prior Precision.

Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression.

A prefrontal-thalamic circuit encodes social information for social recognition.

Social recognition encompasses encoding social information and distinguishing unfamiliar from familiar individuals to form social relationships. Although the medial prefrontal cortex (mPFC) is known to play a role in social behavior, how identity information is processed and by which route it is communicated in the brain remains unclear. Here we report that a ventral midline thalamic area, nucleus reuniens (Re) that has reciprocal connections with the mPFC, is critical for social recognition in male mice.

Synchronization and oscillation behaviors of excitatory and inhibitory populations with spike-timing-dependent plasticity.

The effect of synaptic plasticity on the synchronization mechanism of the cerebral cortex has been a hot research topic over the past two decades. There are a great deal of literatures on excitatory pyramidal neurons, but the mechanism of interaction between the inhibitory interneurons is still under exploration. In this study, we consider a complex network consisting of excitatory (E) pyramidal neurons and inhibitory (I) interneurons interacting with chemical synapses through spike-timing-dependent plasticity (STDP).

Entorhinal-hippocampal interactions lead to globally coherent representations of space.

The firing maps of grid cells in the entorhinal cortex are thought to provide an efficient metric system capable of supporting spatial inference in all environments. However, whether spatial representations of grid cells are determined by local environment cues or are organized into globally coherent patterns remains undetermined. We propose a navigation model containing a path integration system in the entorhinal cortex and a cognitive map system in the hippocampus.

The effects of first-dose methylphenidate on the neural signatures of visual selective attention in children with attention-deficit/hyperactivity disorder.

Although methylphenidate (MPH) has been shown to significantly improve selective attention in children with attention-deficit/hyperactivity disorder (ADHD), the neural mechanism of this effect remains unclear. We investigated the effects of first-dose MPH on the neural signatures of visual selective attention in children with ADHD. We measured the impact of first-dose MPH on electrophysiological indexes from eighteen children with ADHD (8.

Abnormal Reactivity of Brain Oscillations to Visual Search Target in Children With Attention-Deficit/Hyperactivity Disorder.

Background: Previous studies have shown that impaired goal-directed alpha lateralization and functional disconnection within attention networks during the cue period are significant features of attention-deficit/hyperactivity disorder (ADHD). This study aimed to explore the role of brain oscillations in the visual search process, focusing on target-induced posterior alpha lateralization, midfrontal theta synchronization, and their functional connection in children with ADHD.

Methods: Electroencephalograms were recorded from typically developing (TD) children (n = 72) and children with ADHD (n = 96) while they performed a visual search task.

A theory of geometry representations for spatial navigation.

The geometric information of space, such as environment boundaries, is represented heterogeneously across brain regions. The computational mechanisms of encoding the spatial layout of environments remain to be determined. Here, we postulate a conjunctive encoding theory to illustrate the construct of cognitive maps from geometric perception.

Characterization of exploratory patterns and hippocampal-prefrontal network oscillations during the emergence of free exploration.

During free exploration, the emergence of patterned and sequential behavioral responses to an unknown environment reflects exploration traits and adaptation. However, the behavioral dynamics and neural substrates underlying the exploratory behavior remain poorly understood. We developed computational tools to quantify the exploratory behavior and performed in vivo electrophysiological recordings in a large arena in which mice made sequential excursions into unknown territory.

Probabilistic learning vector quantization on manifold of symmetric positive definite matrices.

In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matrices, which are inherently points that live on a curved Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, traditional Euclidean machine learning algorithms yield poor results on such data.

A brain-inspired compact cognitive mapping system.

In many simultaneous localization and mapping (SLAM) systems, the map of the environment grows over time as the robot explores the environment. The ever-growing map prevents long-term mapping, especially in large-scale environments. In this paper, we develop a compact cognitive mapping approach inspired by neurobiological experiments.

The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control.

The proposed architecture applies the principle of predictive coding and deep learning in a brain-inspired approach to robotic sensorimotor control. It is composed of many layers each of which is a recurrent network. The component networks can be spontaneously active due to the homeokinetic learning rule, a principle that has been studied previously for the purpose of self-organized generation of behavior.

Video data for the cognitive mapping process of NeuroBayesSLAM system.

Simultaneous localization and mapping (SLAM), which addresses the problem of constructing a spatial map of an unknown environment while simultaneously determining the mobile robot's position relative to this map, is regarded as one of the key technologies in mobile robot navigation. This data article presents four raw video files, demonstrating the mapping and localization processes of NeuroBayesSLAM, a neurobiologically inspired SLAM system, on two publicly available datasets, namely the St Lucia suburb dataset and the iRat Australia dataset. The cognitive mapping process was recorded by a free screen recorder software on ubuntu Linux system.

Learning Cognitive Map Representations for Navigation by Sensory-Motor Integration.

How to transform a mixed flow of sensory and motor information into memory state of self-location and to build map representations of the environment are central questions in the navigation research. Studies in neuroscience have shown that place cells in the hippocampus of the rodent brains form dynamic cognitive representations of locations in the environment. We propose a neural-network model called sensory-motor integration network model (SeMINet) to learn cognitive map representations by integrating sensory and motor information while an agent is exploring a virtual environment.

NeuroBayesSLAM: Neurobiologically inspired Bayesian integration of multisensory information for robot navigation.

Spatial navigation depends on the combination of multiple sensory cues from idiothetic and allothetic sources. The computational mechanisms of mammalian brains in integrating different sensory modalities under uncertainty for navigation is enlightening for robot navigation. We propose a Bayesian attractor network model to integrate visual and vestibular inputs inspired by the spatial memory systems of mammalian brains.

Learning joint space-time-frequency features for EEG decoding on small labeled data.

Brain-computer interfaces (BCIs), which control external equipment using cerebral activity, have received considerable attention recently. Translating brain activities measured by electroencephalography (EEG) into correct control commands is a critical problem in this field. Most existing EEG decoding methods separate feature extraction from classification and thus are not robust across different BCI users.

A novel pyramidal cell type promotes sharp-wave synchronization in the hippocampus.

To support cognitive function, the CA3 region of the hippocampus performs computations involving attractor dynamics. Understanding how cellular and ensemble activities of CA3 neurons enable computation is critical for elucidating the neural correlates of cognition. Here we show that CA3 comprises not only classically described pyramid cells with thorny excrescences, but also includes previously unidentified 'athorny' pyramid cells that lack mossy-fiber input.

A Reinforcement Learning Neural Network for Robotic Manipulator Control.

We propose a neural network model for reinforcement learning to control a robotic manipulator with unknown parameters and dead zones. The model is composed of three networks. The state of the robotic manipulator is predicted by the state network of the model, the action policy is learned by the action network, and the performance index of the action policy is estimated by a critic network.

Cognitive Mapping Based on Conjunctive Representations of Space and Movement.

It is a challenge to build robust simultaneous localization and mapping (SLAM) system in dynamical large-scale environments. Inspired by recent findings in the entorhinal-hippocampal neuronal circuits, we propose a cognitive mapping model that includes continuous attractor networks of head-direction cells and conjunctive grid cells to integrate velocity information by conjunctive encodings of space and movement. Visual inputs from the local view cells in the model provide feedback cues to correct drifting errors of the attractors caused by the noisy velocity inputs.

Selforganization of modular activity of grid cells.

A unique topographical representation of space is found in the concerted activity of grid cells in the rodent medial entorhinal cortex. Many among the principal cells in this region exhibit a hexagonal firing pattern, in which each cell expresses its own set of place fields (spatial phases) at the vertices of a triangular grid, the spacing and orientation of which are typically shared with neighboring cells. Grid spacing, in particular, has been found to increase along the dorso-ventral axis of the entorhinal cortex but in discrete steps, that is, with a modular structure.

Local Autoencoding for Parameter Estimation in a Hidden Potts-Markov Random Field.

A local-autoencoding (LAE) method is proposed for the parameter estimation in a Hidden Potts-Markov random field model. Due to sampling cost, Markov chain Monte Carlo methods are rarely used in real-time applications. Like other heuristic methods, LAE is based on a conditional independence assumption.

Continuous attractor network model for conjunctive position-by-velocity tuning of grid cells.

The spatial responses of many of the cells recorded in layer II of rodent medial entorhinal cortex (MEC) show a triangular grid pattern, which appears to provide an accurate population code for animal spatial position. In layer III, V and VI of the rat MEC, grid cells are also selective to head-direction and are modulated by the speed of the animal. Several putative mechanisms of grid-like maps were proposed, including attractor network dynamics, interactions with theta oscillations or single-unit mechanisms such as firing rate adaptation.

Grid maps for spaceflight, anyone? They are for free!

We show that, given extensive exploration of a three-dimensional volume, grid units can form with the approximate periodicity of a face-centered cubic crystal, as the spontaneous product of a self-organizing process at the single unit level, driven solely by firing rate adaptation.

A model for the differentiation between grid and conjunctive units in medial entorhinal cortex.

The multiple layers of medial entorhinal cortex (mEC) contain cells that differ in selectivity, connectivity, and cellular properties. Grid cells in layer II and in the deeper layers express triangular grid patterns in the environment. The firing rate of the conjunctive cells found in layer III and below, on the other hand, show grid-by-head direction tuning.