scholarly journals Principles for models of neural information processing

2017 ◽  
Author(s):  
Kendrick N. Kay ◽  
Kevin S. Weiner
Keyword(s):  
Information Processing ◽  
Cognitive Neuroscience ◽  
Brain Function ◽  
Network Models ◽  
Neural Network Models ◽  
Specific Effects ◽  
Neural Information ◽  
Neural Information Processing ◽  
The Difference ◽  
The Brain

AbstractThe goal of cognitive neuroscience is to understand how mental operations are performed by the brain. Given the complexity of the brain, this is a challenging endeavor that requires the development of formal models. Here, we provide a perspective on models of neural information processing in cognitive neuroscience. We define what these models are, explain why they are useful, and specify criteria for evaluating models. We also highlight the difference between functional and mechanistic models, and call attention to the value that neuroanatomy has for understanding brain function. Based on the principles we propose, we proceed to evaluate the merit of recently touted deep neural network models. We contend that these models are promising, but substantial work is necessary to (i) clarify what type of explanation these models provide, (ii) determine what specific effects they accurately explain, and (iii) improve our understanding of how they work.

GLOBAL EFFECTS OF FLUCTUATIONS IN NEURAL INFORMATION PROCESSING

1996 ◽  
Vol 07 (04) ◽  
pp. 497-505 ◽  
Author(s):  
HANS LILJENSTRÖM
Keyword(s):  
Information Processing ◽  
Neuronal Excitability ◽  
Complex Dynamics ◽  
Memory State ◽  
Cortical Dynamics ◽  
Neural Information ◽  
Neural Information Processing ◽  
Correct Pattern ◽  
The Brain

We are interested in how the complex dynamics of the brain, which may include oscillations, chaos and noise, can affect the efficiency of neural information processing. Here, we consider the amplification and functional role of fluctuations, expressed as chaos or noise in the system. Using computer simulations of a neural network model of the olfactory cortex, we demonstrate how microscopic fluctuations can result in global effects at the network level. In particular, we show that the rate of information processing in associative memory tasks can be maximized for optimal noise levels. Noise can also induce transitions between different dynamical states, related to learning and memory. A chaotic-like behavior, induced by noise or by an increase in neuronal excitability, can enhance system performance if it is transient and converges to a limit cycle memory state. The level of accuracy required for correct pattern association further affects the rate of information processing. We discuss how neuromodulatory control of the cortical dynamics can shift the balance between rate and accuracy optimization, as well as between sensitivity and stability.

How Visual Expertise Changes Representational Geometry: A Behavioral and Neural Perspective

2021 ◽  
pp. 1-16
Author(s):  
Stefanie Duyck ◽  
Farah Martens ◽  
Chiu-Yueh Chen ◽  
Hans Op de Beeck
Keyword(s):  
Information Processing ◽  
Brain Function ◽  
Visual Expertise ◽  
Fine Grained ◽  
Behavioral Level ◽  
Fmri Study ◽  
Neural Information ◽  
Neural Information Processing ◽  
Subordinate Level ◽  
And Task

Abstract Many people develop expertise in specific domains of interest, such as chess, microbiology, radiology, and, the case in point in our study: ornithology. It is poorly understood to what extent such expertise alters brain function. Previous neuroimaging studies of expertise have typically focused upon the category level, for example, selectivity for birds versus nonbird stimuli. We present a multivariate fMRI study focusing upon the representational similarity among objects of expertise at the subordinate level. We compare the neural representational spaces of experts and novices to behavioral judgments. At the behavioral level, ornithologists (n = 20) have more fine-grained and task-dependent representations of item similarity that are more consistent among experts compared to control participants. At the neural level, the neural patterns of item similarity are more distinct and consistent in experts than in novices, which is in line with the behavioral results. In addition, these neural patterns in experts show stronger correlations with behavior compared to novices. These findings were prominent in frontal regions, and some effects were also found in occipitotemporal regions. This study illustrates the potential of an analysis of representational geometry to understand to what extent expertise changes neural information processing.

scholarly journals Blaming blunders on the brain: can indifferent choices be driven by range adaptation or synaptic plasticity?

2020 ◽  
Author(s):  
Jules Brochard ◽  
Jean Daunizeau
Keyword(s):  
Information Processing ◽  
Activity Patterns ◽  
Computational Method ◽  
Choice Data ◽  
Biological Mechanisms ◽  
Neural Network Models ◽  
Hebbian Plasticity ◽  
Neural Information ◽  
Neural Information Processing ◽  
Biological Constraints

AbstractComputational investigations of learning and decision making suggest that systematic deviations to adaptive behavior may be the incidental outcome of biological constraints imposed on neural information processing. In particular, recent studies indicate that range adaptation, i.e., the mechanism by which neurons dynamically tune their output firing properties to match the changing statistics of their inputs, may drive plastic changes in the brain’s decision system that induce systematic deviations to rationality. Here, we ask whether behaviorally-relevant neural information processing may be distorted by other incidental, hard-wired, biological constraints, in particular: Hebbian plasticity. One of our main contributions is to propose a simple computational method for identifying (and comparing) the neural signature of such biological mechanisms or constraints. Using ANNs (i.e., artificial neural network models) and RSA (i.e., representational similarity analysis), we compare the neural signatures of two types of hard-wired biological mechanisms/constraints: namely, range adaptation and Hebbian plasticity. We apply the approach to two different open fMRI datasets acquired when people make decisions under risk. In both cases, we show that although peoples’ apparent indifferent choices are well explained by biologically-constrained ANNs, choice data alone does not discriminate between range adaptation and Hebbian plasticity. However, RSA shows that neural activity patterns in bilateral Striatum and Amygdala are more compatible with Hebbian plasticity. Finally, the strength of evidence for Hebbian plasticity in these structures predicts inter-individual differences in choice inconsistency.

scholarly journals Cellular-scale silicon probes for high-density, precisely localized neurophysiology

2020 ◽  
Vol 124 (6) ◽  
pp. 1578-1587 ◽  
Author(s):  
Daniel Egert ◽  
Jeffrey R. Pettibone ◽  
Stefan Lemke ◽  
Paras R. Patel ◽  
Ciara M. Caldwell ◽  
...  
Keyword(s):  
Information Processing ◽  
High Density ◽  
Immunohistochemical Markers ◽  
Brain Circuits ◽  
Neural Information ◽  
Neural Information Processing ◽  
The Brain ◽  
Silicon Probes

Devices with many electrodes penetrating into the brain are an important tool for investigating neural information processing, but they are typically large compared with neurons. This results in substantial damage and makes it harder to reconstruct recording locations within brain circuits. This paper presents high-channel-count silicon probes with much smaller features and a method for slicing through probe, brain, and skull all together. This allows probe tips to be directly observed relative to immunohistochemical markers.

Neural Information Processing

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

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

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