Probabilistic Models of the Brain: Perception and Neural Function. Neural Information Processing Series.Edited by Rajesh P N  Rao, Bruno A  Olshausen, and , Michael S  Lewicki. A Bradford Book. Cambridge (Massachusetts): MIT Press. $50.00. x + 324 p; ill.; index. ISBN: 0–262–18224–6. 2002.

2003 ◽  
Vol 78 (1) ◽  
pp. 124-125 ◽  
Author(s):  
Bruce Bridgeman
Keyword(s):  
Information Processing ◽  
Probabilistic Models ◽  
Neural Function ◽  
Neural Information ◽  
Neural Information Processing ◽  
The Brain

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.

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.

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

scholarly journals Consciousness, decision making, and volition: freedom beyond chance and necessity

2021 ◽  
Author(s):  
Hans Liljenström
Keyword(s):  
Decision Making ◽  
Free Will ◽  
Brain Activity ◽  
Brain Structures ◽  
Complex Process ◽  
Neural Information ◽  
Neural Information Processing ◽  
Stochastic Population Model ◽  
The Brain

AbstractWhat is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what “you” decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world.

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