scholarly journals Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy, and Mobility

2012 ◽  
Vol 24 (5) ◽  
pp. 1147-1185 ◽  
Author(s):  
C. C. Alan Fung ◽  
K. Y. Michael Wong ◽  
He Wang ◽  
Si Wu
Keyword(s):  
Information Processing ◽  
Neural Systems ◽  
Neural Responses ◽  
Short Term ◽  
Neuronal Interactions ◽  
Neural Information ◽  
Neural Information Processing ◽  
Network States ◽  
Improved Accuracy ◽  
The Impact

Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity: short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and rapid learning and may serve as substrates for neural systems manipulating temporal information on relevant timescales. This study investigates the impact of STD and STF on the dynamics of continuous attractor neural networks and their potential roles in neural information processing. We find that STD endows the network with slow-decaying plateau behaviors: the network that is initially being stimulated to an active state decays to a silent state very slowly on the timescale of STD rather than on that of neuralsignaling. This provides a mechanism for neural systems to hold sensory memory easily and shut off persistent activities gracefully. With STF, we find that the network can hold a memory trace of external inputs in the facilitated neuronal interactions, which provides a way to stabilize the network response to noisy inputs, leading to improved accuracy in population decoding. Furthermore, we find that STD increases the mobility of the network states. The increased mobility enhances the tracking performance of the network in response to time-varying stimuli, leading to anticipative neural responses. In general, we find that STD and STP tend to have opposite effects on network dynamics and complementary computational advantages, suggesting that the brain may employ a strategy of weighting them differentially depending on the computational purpose.

Orienting Attention

2014 ◽  
Author(s):  
Charles Spence
Keyword(s):  
Information Processing ◽  
Neural Circuits ◽  
Sensory Modality ◽  
Spatial Location ◽  
Neural Systems ◽  
Attentional Orienting ◽  
Rapid Rise ◽  
Spatial Orienting ◽  
Neural Information ◽  
Neural Information Processing

The last 30 years or so have seen a rapid rise in research on attentional orienting from a crossmodal perspective. The majority of this research has tended to focus on the consequences of the covert orienting of attention (either to a sensory modality or spatial location) for both perception and neural information processing. The results of numerous studies have now highlighted the robust crossmodal links that exist in the case of both overt and covert, and both exogenous and endogenous spatial orienting. Neuroimaging studies have started to highlight the neural circuits underlying such crossmodal effects. Researchers are increasingly using transcranial magnetic stimulation in order to lesion temporarily putative areas within these networks; the aim of such research often being to determine whether attentional orienting is controlled by supramodal versus modality-specific neural systems that are somehow linked (this is known as the ‘separable-but-linked’ hypothesis). The available research demonstrates that crossmodal attentional orienting (and multisensory integration—from which it is sometimes hard to distinguish) can affect the very earliest stages of information processing in the human brain.

scholarly journals Information Confusion Reveals an Innate Limit of the Information Processing by Neurons

2020 ◽  
Author(s):  
Yang Tian ◽  
Justin L. Gardner ◽  
Guoqi Li ◽  
Pei Sun
Keyword(s):  
Information Processing ◽  
Neural Coding ◽  
Neural System ◽  
Negative Answer ◽  
Processing Capacity ◽  
Neural Responses ◽  
Information Processing Capacity ◽  
Neural Ensembles ◽  
Neural Information ◽  
Neural Information Processing

AbstractInformation experiences complex transformation processes in the brain, involving various errors. A daunting and critical challenge in neuroscience is to understand the origin of these errors and their effects on neural information processing. While previous efforts have made substantial progresses in studying the information errors in bounded, unreliable and noisy transformation cases, it still remains elusive whether the neural system is inherently error-free under an ideal and noise-free condition. This work brings the controversy to an end with a negative answer. We propose a novel neural information confusion theory, indicating the widespread presence of information confusion phenomenon after the end of transmission process, which originates from innate neuron characteristics rather than external noises. Then, we reformulate the definition of zero-error capacity under the context of neuroscience, presenting an optimal upper bound of the zero-error transformation rates determined by the tuning properties of neurons. By applying this theory to neural coding analysis, we unveil the multi-dimensional impacts of information confusion on neural coding. Although it reduces the variability of neural responses and limits mutual information, it controls the stimulus-irrelevant neural activities and improves the interpretability of neural responses based on stimuli. Together, the present study discovers an inherent and ubiquitous precision limitation of neural information transformation, which shapes the coding process by neural ensembles. These discoveries reveal that the neural system is intrinsically error-prone in information processing even in the most ideal cases.Author summaryOne of the most central challenges in neuroscience is to understand the information processing capacity of the neural system. Decades of efforts have identified various errors in nonideal neural information processing cases, indicating that the neural system is not optimal in information processing because of the widespread presences of external noises and limitations. These incredible progresses, however, can not address the problem about whether the neural system is essentially error-free and optimal under ideal information processing conditions, leading to extensive controversies in neuroscience. Our work brings this well-known controversy to an end with a negative answer. We demonstrate that the neural system is intrinsically error-prone in information processing even in the most ideal cases, challenging the conventional ideas about the superior neural information processing capacity. We further indicate that the neural coding process is shaped by this innate limit, revealing how the characteristics of neural information functions and further cognitive functions are determined by the inherent limitation of the neural system.

Neural Information Processing

1967 ◽  
Vol 12 (11) ◽  
pp. 558-559
Author(s):  
STEPHAN L. CHOROVER
Keyword(s):  
Information Processing ◽  
Neural Information ◽  
Neural Information Processing

scholarly journals Cortical response to naturalistic stimuli is largely predictable with deep neural networks

2021 ◽  
Vol 7 (22) ◽  
pp. eabe7547
Author(s):  
Meenakshi Khosla ◽  
Gia H. Ngo ◽  
Keith Jamison ◽  
Amy Kuceyeski ◽  
Mert R. Sabuncu
Keyword(s):  
Brain Function ◽  
Deep Neural Networks ◽  
Neural Responses ◽  
Dynamic Interactions ◽  
Hierarchical Processing ◽  
Human Connectome Project ◽  
Neural Information ◽  
Neural Information Processing ◽  
Visual Interactions ◽  
High Level

Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict neural responses to arbitrary stimuli can be very useful for studying brain function. However, existing models focus on limited aspects of naturalistic stimuli, ignoring the dynamic interactions of modalities in this inherently context-rich paradigm. Using movie-watching data from the Human Connectome Project, we build group-level models of neural activity that incorporate several inductive biases about neural information processing, including hierarchical processing, temporal assimilation, and auditory-visual interactions. We demonstrate how incorporating these biases leads to remarkable prediction performance across large areas of the cortex, beyond the sensory-specific cortices into multisensory sites and frontal cortex. Furthermore, we illustrate that encoding models learn high-level concepts that generalize to task-bound paradigms. Together, our findings underscore the potential of encoding models as powerful tools for studying brain function in ecologically valid conditions.

scholarly journals Autism as a disorder of neural information processing: Directions for research and targets for therapy

2019 ◽  
Author(s):  
Matthew Belmonte ◽  
John Rubenstein
Keyword(s):  
Brain Development ◽  
Autism Spectrum ◽  
Behavioural Syndrome ◽  
Neural Systems ◽  
Developmental Courses ◽  
Genetic Syndromes ◽  
Causal Factors ◽  
Neural Information ◽  
Neural Information Processing ◽  
Broad Variation

The broad variation in phenotypes and severities within autism spectrum disorders suggests the involvement of multiple predisposing factors, interacting in complex ways with normal developmental courses and gradients. Identification of these factors, and the common developmental path into which they feed, is hampered by the large degrees of convergence from causal factors to altered brain development, and divergence from abnormal brain development into altered cognition and behaviour. Genetic, neurochemical, neuroimaging and behavioural findings on autism, as well as studies of normal development and of genetic syndromes that share symptoms with autism, offer hypotheses as to the nature of causal factors and their possible effects on the structure and dynamics of neural systems. Such alterations in neural properties may in turn perturb activity-dependent development, giving rise to a complex behavioural syndrome many steps removed from the root causes. Animal models based on genetic, neurochemical, neurophysiological, and behavioural manipulations offer the possibility of exploring these developmental processes in detail, as do human studies addressing endophenotypes beyond the diagnosis itself.

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