scholarly journals Neural dynamics in response to binary taste mixtures

2013 ◽  
Vol 109 (8) ◽  
pp. 2108-2117 ◽  
Author(s):  
Joost X. Maier ◽  
Donald B. Katz
Keyword(s):  
Citric Acid ◽  
Temporal Dynamics ◽  
Neural Dynamics ◽  
Response Patterns ◽  
Single Neurons ◽  
Taste System ◽  
Mixture Suppression ◽  
Access Test ◽  
The Brain ◽  
Related Pattern

Taste stimuli encountered in the natural environment are usually combinations of multiple tastants. Although a great deal is known about how neurons in the taste system respond to single taste stimuli in isolation, less is known about how the brain deals with such mixture stimuli. Here, we probe the responses of single neurons in primary gustatory cortex (GC) of awake rats to an array of taste stimuli including 100% citric acid (100 mM), 100% sodium chloride (100 mM), 100% sucrose (100 mM), and a range of binary mixtures (90/10, 70/30, 50/50, 30/70, and 10/90%). We tested for the presence of three different hypothetical response patterns: 1) responses varying monotonically as a function of concentration of sucrose (or acid) in the mixture (the “monotonic” pattern); 2) responses increasing or decreasing as a function of degree of mixture of the stimulus (the “mixture” pattern); and 3) responses that change abruptly from being similar to one pure taste to being similar the other (the “categorical” pattern). Our results demonstrate the presence of both monotonic and mixture patterns within responses of GC neurons. Specifically, further analysis (that included the presentation of 50 mM sucrose and citric acid) made it clear that mixture suppression reliably precedes a palatability-related pattern. The temporal dynamics of the emergence of the palatability-related pattern parallel the temporal dynamics of the emergence of preference behavior for the same mixtures as measured by a brief access test. We saw no evidence of categorical coding.

scholarly journals Neural population dynamics underlying expected value computation

2020 ◽  
Author(s):  
Hiroshi Yamada ◽  
Yuri Imaizumi ◽  
Masayuki Matsumoto
Keyword(s):  
Ventral Striatum ◽  
Temporal Dynamics ◽  
Dorsal Striatum ◽  
Expected Value ◽  
Economic Behavior ◽  
Neural Dynamics ◽  
Neural Population ◽  
Integrative Process ◽  
Expected Values ◽  
The Brain

AbstractComputation of expected values, i.e., probability times magnitude, seems to be a dynamic integrative process performed in the brain for efficient economic behavior. However, neural dynamics underlying this computation remain largely unknown. We examined (1) whether four core reward-related regions detect and integrate the probability and magnitude cued by numerical symbols and (2) whether these regions have distinct dynamics in the integrative process. Extractions of mechanistic structure of neural population signal demonstrated that expected-value signals simultaneously arose in central part of orbitofrontal cortex (cOFC, area 13m) and ventral striatum (VS). These expected-value signals were incredibly stable in contrast to weak and/or fluctuated signals in dorsal striatum and medial OFC. Notably, temporal dynamics of these stable expected-value signals were unambiguously distinct: sharp and gradual signal evolutions in cOFC and VS, respectively. These intimate dynamics suggest that cOFC and VS compute the expected-values with unique time constants, as distinct, partially overlapping processes.

Temporal Dynamics of Figure-Ground Segregation in Human Vision

2007 ◽  
Vol 97 (1) ◽  
pp. 951-957 ◽  
Author(s):  
Peter Neri ◽  
Dennis M. Levi
Keyword(s):  
Visual Stimulus ◽  
Temporal Dynamics ◽  
Human Perception ◽  
Human Vision ◽  
Reverse Correlation ◽  
Single Neurons ◽  
Neural Signals ◽  
Temporal Window ◽  
Electrophysiological Studies ◽  
The Brain

The segregation of figure from ground is arguably one of the most fundamental operations in human vision. Neural signals reflecting this operation appear in cortex as early as 50 ms and as late as 300 ms after presentation of a visual stimulus, but it is not known when these signals are used by the brain to construct the percepts of figure and ground. We used psychophysical reverse correlation to identify the temporal window for figure-ground signals in human perception and found it to lie within the range of 100–160 ms. Figure enhancement within this narrow temporal window was transient rather than sustained as may be expected from measurements in single neurons. These psychophysical results prompt and guide further electrophysiological studies.

scholarly journals Stability and flexibility in multisensory sampling: insights from perceptual illusions

2019 ◽  
Vol 121 (5) ◽  
pp. 1588-1590 ◽  
Author(s):  
Luca Casartelli
Keyword(s):  
Temporal Dynamics ◽  
Multisensory Processing ◽  
General Idea ◽  
Perceptual Illusions ◽  
The Brain ◽  
Multisensory Processes

Neural, oscillatory, and computational counterparts of multisensory processing remain a crucial challenge for neuroscientists. Converging evidence underlines a certain efficiency in balancing stability and flexibility of sensory sampling, supporting the general idea that multiple parallel and hierarchically organized processing stages in the brain contribute to our understanding of the (sensory/perceptual) world. Intriguingly, how temporal dynamics impact and modulate multisensory processes in our brain can be investigated benefiting from studies on perceptual illusions.

Neural Dynamics in the Processing of Personal Objects as an Index of the Brain Representation of the Self

2019 ◽  
Vol 33 (1) ◽  
pp. 86-100 ◽  
Author(s):  
Francisco Muñoz ◽  
Pilar Casado ◽  
David Hernández-Gutiérrez ◽  
Laura Jiménez-Ortega ◽  
Sabela Fondevila ◽  
...  
Keyword(s):  
The Self ◽  
Neural Dynamics ◽  
The Brain

scholarly journals The brain timewise: how timing shapes and supports brain function

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Generative Models ◽  
Network Activity ◽  
Brain Diseases ◽  
Specific Information ◽  
Non Invasive ◽  
The Brain

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

scholarly journals Using the past to estimate sensory uncertainty

2019 ◽  
Author(s):  
Ulrik Beierholm ◽  
Tim Rohe ◽  
Ambra Ferrari ◽  
Oliver Stegle ◽  
Uta Noppeney
Keyword(s):  
Temporal Dynamics ◽  
Visual Signals ◽  
Perceptual Inference ◽  
Reliability Estimates ◽  
Uncertainty Estimates ◽  
Classical Models ◽  
Past Experiences ◽  
Combine Information ◽  
Sensory Uncertainty ◽  
The Brain

AbstractTo form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discountingOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

2019 ◽  
Vol 31 (11) ◽  
pp. 2177-2211 ◽  
Author(s):  
Saurabh Bhaskar Shaw ◽  
Kiret Dhindsa ◽  
James P. Reilly ◽  
Suzanna Becker
Keyword(s):  
Systems Analysis ◽  
Temporal Dynamics ◽  
Field Potential ◽  
Stable States ◽  
Dynamical Systems Analysis ◽  
Eeg Data ◽  
Show Evidence ◽  
Winner Takes All ◽  
Microstate Analysis ◽  
The Brain

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

Saliency-based Rhythmic Coordination of Perceptual Predictions

2020 ◽  
Vol 32 (2) ◽  
pp. 201-211 ◽  
Author(s):  
Qiaoli Huang ◽  
Huan Luo
Keyword(s):  
Temporal Dynamics ◽  
Theta Band ◽  
Behavioral Profile ◽  
Fine Grained ◽  
Attentional Process ◽  
Behavioral Measurement ◽  
Processing Resources ◽  
Rhythmic Coordination ◽  
The Brain ◽  
Different Levels

Objects, shown explicitly or held in mind internally, compete for limited processing resources. Recent studies have demonstrated that attention samples locations and objects rhythmically. Interestingly, periodic sampling not only operates over objects in the same scene but also occurs for multiple perceptual predictions that are held in attention for incoming inputs. However, how the brain coordinates perceptual predictions that are endowed with different levels of bottom–up saliency information remains unclear. To address the issue, we used a fine-grained behavioral measurement to investigate the temporal dynamics of processing of high- and low-salient visual stimuli, which have equal possibility to occur within experimental blocks. We demonstrate that perceptual predictions associated with different levels of saliency are organized via a theta-band rhythmic course and are optimally processed in different phases within each theta-band cycle. Meanwhile, when the high- and low-salient stimuli are presented in separate blocks and thus not competing with each other, the periodic behavioral profile is no longer present. In summary, our findings suggest that attention samples and coordinates multiple perceptual predictions through a theta-band rhythm according to their relative saliency. Our results, in combination with previous studies, advocate the rhythmic nature of attentional process.

scholarly journals Multiple timescales of neural dynamics and integration of task-relevant signals across cortex

2020 ◽  
Vol 117 (36) ◽  
pp. 22522-22531 ◽  
Author(s):  
Mehran Spitmaan ◽  
Hyojung Seo ◽  
Daeyeol Lee ◽  
Alireza Soltani
Keyword(s):  
Cortical Area ◽  
Dynamic Decision Making ◽  
Neural Dynamics ◽  
Neural Processing ◽  
Neuronal Dynamics ◽  
Time Constants ◽  
Multiple Timescales ◽  
Comprehensive Method ◽  
Organizational Principle ◽  
The Brain

A long-lasting challenge in neuroscience has been to find a set of principles that could be used to organize the brain into distinct areas with specific functions. Recent studies have proposed the orderly progression in the time constants of neural dynamics as an organizational principle of cortical computations. However, relationships between these timescales and their dependence on response properties of individual neurons are unknown, making it impossible to determine how mechanisms underlying such a computational principle are related to other aspects of neural processing. Here, we developed a comprehensive method to simultaneously estimate multiple timescales in neuronal dynamics and integration of task-relevant signals along with selectivity to those signals. By applying our method to neural and behavioral data during a dynamic decision-making task, we found that most neurons exhibited multiple timescales in their response, which consistently increased from parietal to prefrontal and cingulate cortex. While predicting rates of behavioral adjustments, these timescales were not correlated across individual neurons in any cortical area, resulting in independent parallel hierarchies of timescales. Additionally, none of these timescales depended on selectivity to task-relevant signals. Our results not only suggest the existence of multiple canonical mechanisms for increasing timescales of neural dynamics across cortex but also point to additional mechanisms that allow decorrelation of these timescales to enable more flexibility.

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