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[Display omitted] Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks (DNNs) to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations. However, typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks, leading to suboptimal brain-wide correspondence during cognitive tasks. To address this challenge, this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks. Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration, resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception. Furthermore, this multi-level integration framework enables a deeper understanding of the brain's neural representation mechanism, accurately predicting the neural response to complex visual concepts. We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks. [ABSTRACT FROM AUTHOR]

Internal representation of the space is a fundamental and crucial function of the animal's brain. Grid cells in the medial entorhinal cortex are thought to provide an environment-invariant metric system for the navigation of the animal. Most experimental and theoretical studies have focused on the horizontal planar codes of grid cell, while how this metric coordinate system is configured in the actual three-dimensional space remains unclear. Evidence has implied the spatial cognition may not be fully volumetric. We proposed an oscillatory interference model with a novel gravity and body plane modulation to simulate grid cell activity in complex space for rodents. The animal can perceive the rotation of its body plane along the local surface by sensing the gravity, causing the modulation to the dendritic oscillations. The results not only reproduce the firing patterns of the grid cell recorded from known experiments, but also predict the grid codes in novel environments. It further demonstrates that the gravity signal is indispensable for the animal's navigation, and supports the hypothesis that the periodic firing of the grid cell is intrinsically not a volumetric code in three-dimensional space. This will provide new insights to understand the spatial representation of the actual world in the brain. [ABSTRACT FROM AUTHOR]

Neuronal synchronization plays important roles in information encoding and transmission in the brain. Mathematical models of neurons have been widely used to simulate synchronization behavior and analyze its mechanisms. Common stochastic inputs are considered to be effective in facilitating synchronization. However, the mechanisms of how partial reset affects neuronal synchronization are still not well understood. In this paper, the synchronization of Stein's model neurons with partial reset is studied. The differences in synchronization mechanisms between neurons with full reset and those with partial reset are analyzed, and the findings lead to the novel concept of transient synchronization. Furthermore, it is proven analytically that due to common stochastic inputs, Stein's model neurons with different initial membrane potentials and partial reset achieve transient synchronization with probability 1. Additionally, a systematic numerical analysis is performed to explore the similarities and differences between full reset and partial reset regarding model parameters, synchronization time, and desynchronization behavior. Thus, partial reset is a powerful and flexible tool that facilitates neuronal synchronization while reserving the possibility of desynchronization. Our analysis also provides an alternative approach to analyze neurons of the integrate-and-fire family and a theoretical complement implying possible information encoding mechanisms in the brain. [ABSTRACT FROM AUTHOR]

Transmitting information in engineered neural communication systems is a promising solution to delay-sensitive applications for the Internet of Bio-Nanothings (IoBNTs). As widely used in wired and wireless communication systems, introducing multiplexing into neural communication system could improve channel transmission efficiency. In this paper, we model a neural communication system for IoBNTs and propose a neural signal multiplexing scheme for this system, based on frequency-division multiplexing (FDM) principles. The whole system including channel modeling, neural encoding, demultiplexing scheme, and decoding method using kernel density estimation (KDE) are presented. The optimal parameters for KDE and bit error probability are analyzed, and the performance of the proposed strategy is evaluated in terms of error rate and mutual information rate. The work can help researchers better understanding the underlying mechanism of neural multiplexing and pave the way for the implementation of IoBNT applications. [ABSTRACT FROM AUTHOR]

A recent article shows that the brain automatically estimates the probabilities of possible future actions before it has even received all the information necessary to decide what to do next. [ABSTRACT FROM AUTHOR]

How is space represented in the visual system? At first glance, the answer to this fundamental question appears straightforward: spatial information is directly encoded in the locations of neurons within maps. This concept has long dominated visual neuroscience, leading to mainstream theories of how neurons encode information. However, an accumulation of evidence indicates that this purely spatial view is incomplete and that, even for static images, the representation is fundamentally spatiotemporal. The evidence for this new understanding centers on recent experimental findings concerning the functional role of fixational eye movements, the tiny movements humans and other species continually perform, even when attending to a single point. We review some of these findings and discuss their functional implications. [ABSTRACT FROM AUTHOR]

Abstract: In a modified reflexive spatial attention paradigm, when the cue and the target are at the same spatial location, processing of the target is faster when the cue and the target have different shapes compared to same (shape effect). Recent physiological findings suggest distinct population level encoding of shape in ventral versus dorsal cortical visual streams in monkeys. In human observers, we tested whether the effect of shape on reflexive spatial attention could be attributed to ventral and/or dorsal stream encoding of shape. In the modified reflexive spatial attention paradigm, we varied the shapes of the cue and target. Based on data from monkey physiology (), we selected four pairs of cue and target shapes. In some pairs, cue and target were similarly encoded (similar encoding distance) by a population of cells in the lateral intraparietal cortex, a dorsal stream area, but more dissimilarly encoded (having a greater encoding distance) by a population of cells in the anterior inferotemporal cortex (AIT), a ventral stream area. In other pairs, cue and target were similarly encoded in AIT and had greater dissimilarity in LIP encoding. We found that pairs of cue and target with greater dissimilarity in LIP encoding produced larger and more consistent shape effects up to a cue to target onset asynchrony (CTOA) of 450ms. The shape effects for cue and target pairs with greater dissimilarity in AIT encoding were smaller and inconsistent, suggesting that shape effects in reflexive spatial attention are largely driven by the dorsal stream. [Copyright &y& Elsevier]

Abstract: The connectivity of the cerebral cortex is diluted, with the probability of excitatory connections between even nearby pyramidal cells rarely more than 0.1, and in the hippocampus 0.04. To investigate the extent to which this diluted connectivity affects the dynamics of attractor networks in the cerebral cortex, we simulated an integrate-and-fire attractor network taking decisions between competing inputs with diluted connectivity of 0.25 or 0.1, and with the same number of synaptic connections per neuron for the recurrent collateral synapses within an attractor population as for full connectivity. The results indicated that there was less spiking-related noise with the diluted connectivity in that the stability of the network when in the spontaneous state of firing increased, and the accuracy of the correct decisions increased. The decision times were a little slower with diluted than with complete connectivity. Given that the capacity of the network is set by the number of recurrent collateral synaptic connections per neuron, on which there is a biological limit, the findings indicate that the stability of cortical networks, and the accuracy of their correct decisions or memory recall operations, can be increased by utilizing diluted connectivity and correspondingly increasing the number of neurons in the network, with little impact on the speed of processing of the cortex. Thus diluted connectivity can decrease cortical spiking-related noise. In addition, we show that the Fano factor for the trial-to-trial variability of the neuronal firing decreases from the spontaneous firing state value when the attractor network makes a decision. This article is part of a Special Issue entitled “Neural Coding”. [Copyright &y& Elsevier]

Abstract: Although the healthy adult possesses a large repertoire of coordinative strategies for oromotor behaviors, a range of nonverbal, speech-like movements can be observed during speech. The extent of overlap among sensorimotor speech and nonspeech neural correlates and the role of neuromodulatory inputs generated during oromotor behaviors are unknown. The focus of this review is to consider the adaptive capacity of the orofacial substrate, and the neural correlates of kinematic parameter encoding at cortical and subcortical levels subserving oromotor behaviors. Special emphasis is directed toward distributed neural networks that are dynamically modulated by environment and task related demands. Learning outcomes: Readers will (1) gain a better understanding of healthy adult orofacial pathways, (2) be able to identify orofacial pathway components that contribute to sensorimotor integration, and (3) better understand the flexible connectivity among distributed neural networks subserving oromotor behavior. [Copyright &y& Elsevier]

Abstract: For successful restoration of visual function by retinal implant, a method for electrical stimulation should be devised so that the evoked activities of retinal ganglion cells (RGCs) should convey sufficient information on visual input. By observing RGC activities under different stimulation constraints, it may be possible to determine optimal pulse parameters, such as pulse rate, intensity, and duration, for faithful transmission of visual information. To test the feasibility of this approach, we analyzed RGC spike trains evoked by temporally patterned stimulation from retinal patches mounted on a planar multielectrode array. Assuming that the intensity of uniform visual input is transformed to amplitudes of pulse trains, we attempted to determine optimal methods for modulating the pulse amplitude so that the information essential for the perception of intensity variation is properly represented in RGC responses. RGC firing rates could be modulated to track the temporal pattern of pulse amplitude variations, which implies that pulse amplitude modulation is a plausible means to enable perception of temporal visual patterns by retinal implants. As expected, specific pulse amplitude modulation parameters were crucial for proper encoding of visual input. RGC firing rates increased monotonically according to the pulse amplitude in a defined pulse amplitude range (20–60 μA). The similarity between the RGC firing rate and the temporal pulse intensity pattern was highest when the pulse amplitude was modulated within this range. The optimal pulse rate range could be similarly determined. [Copyright &y& Elsevier]

Abstract: Peri-stimulus time histograms (PSTHs) reveal the temporal distribution of action potentials, averaged over many stimulus presentations. PSTHs have been used as model responses to solve the classification problem, in which a single response (i.e., spike train) is assigned to one of a set of response models evoked by a set of stimuli. In this study, we developed and applied a sparse firing frequency-based method to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs). Extracellularly recorded individual SAR spike trains were evoked by one of three different lung inflation volumes in anesthetized, paralyzed adult male New Zealand White rabbits. Three different PSTH-based firing frequency response models (i.e., one for each stimulus) were constructed from two-thirds of the responses to the 600 inflations presented at each volume, while the remaining one-third were used as responses to be classified. An instantaneous firing frequency representation of each remaining “test response” was computed from their individual spike trains, using one of two forms: sparse and filled. The sparse format assigned instantaneous firing rate values only in bins that contained spikes, while the filled format assigned values to intervening bins too. Classification was performed by computing the Euclidean distance between the response spike trains and the three PSTH-based models using both sparse and filled representations. When comparing the two representations with regard to classification accuracy, we found that the sparse representation does not diminish performance appreciably, while reducing computational burden significantly. [Copyright &y& Elsevier]

The reliability with which a neuron is able to create the same firing pattern when presented with the same stimulus is of critical importance to the understanding of neuronal information processing. We show that reliability is closely related to the process of phaselocking. Experimental results for the reliability of neuronal firing in the spinal cord of rat are presented and compared to results from an integrate and fire model. [Copyright &y& Elsevier]

Auditory brainstem responses were recorded in normal children (NL) and children clinically diagnosed with a learning problem (LP). These responses were recorded to both a click stimulus and the formant transition portion of a speech syllable /da/. While no latency differences between the NL and LP populations were seen in responses to the click stimuli, the syllable /da/ did elicit latency differences between these two groups. Deficits in cortical processing of signals in noise were seen for those LP subjects with delayed brainstem responses to the /da/, but not for LPs with normal brainstem measures. Preliminary findings indicate that training may be beneficial to LP subjects with brainstem processing delays. [Copyright &y& Elsevier]