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System of Memory

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Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...

Working Memory

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Working memory refers to a combination of components, including short-term memory and attention, that allow an individual to hold information temporarily as we perform cognitive tasks. It is an essential cognitive function that enables the execution of complex tasks such as problem-solving, comprehension, and reasoning. Unlike short-term memory, which simply involves the storage of information for a brief period, working memory involves the active manipulation and processing of this...

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Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
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Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)

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G-protein Coupled Receptors

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G-protein coupled receptors are ligand binding receptors that indirectly affect changes in the cell. The actual receptor is a single polypeptide that transverses the cell membrane seven times creating intracellular and extracellular loops. The extracellular loops create a ligand specific pocket which binds to neurotransmitters or hormones. The intracellular loops holds onto the G-protein.

Spin–Spin Coupling: One-Bond Coupling

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Coupling interactions are strongest between NMR-active nuclei bonded to each other, where spin information can be transmitted directly through the pair of bonding electrons. While nuclei polarize their electrons to the opposite spins, the bonding electron pair has opposite spins. Configurations with antiparallel nuclear spins are expected to be lower in energy. When coupling makes antiparallel states more favorable, J is considered to have a positive value. The one-bond coupling constant, 1J,...

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Updated: Feb 12, 2026

Cut-loading: A Useful Tool for Examining the Extent of Gap Junction Tracer Coupling Between Retinal Neurons

Cut-loading: A Useful Tool for Examining the Extent of Gap Junction Tracer Coupling Between Retinal Neurons

Published on: January 12, 2012

Working Memory Load Modulates Neuronal Coupling.

Dimitris A Pinotsis^1,2, Timothy J Buschman^1,3, Earl K Miller¹

¹The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.

Cerebral Cortex (New York, N.Y. : 1991)

|April 3, 2018

Summary

This summary is machine-generated.

Cognitive capacity is limited by neuronal coupling breakdown in the prefrontal cortex, frontal eye fields, and lateral intraparietal area network. This breakdown coincides with impaired working memory performance.

Keywords:

biophysical modeling cognitive capacity prefrontal cortex synchrony working memory

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Working memory capacity is severely limited, but its neurophysiological basis is not fully understood.
Neuronal coupling, or communication between brain regions, is a potential factor influencing cognitive limits.

Purpose of the Study:

To investigate if differences in neuronal coupling explain the capacity limits of working memory.
To explore the neurophysiological underpinnings of cognitive capacity limitations.

Main Methods:

Developed a theoretical model based on Predictive Coding.
Analyzed Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP) in monkeys.
Monkeys performed a change detection task with varying memory loads (1-3 objects).

Main Results:

Changes in memory load altered connectivity within the PFC-FEF-LIP network.
Feedback (top-down) coupling significantly broke down when memory load exceeded cognitive capacity.
Impaired behavioral performance correlated with a breakdown in Prediction signals.

Conclusions:

Neuronal coupling dynamics, particularly feedback connectivity, are crucial for maintaining working memory capacity.
The breakdown of top-down predictive signals in a distributed network underlies cognitive capacity limitations.
This study offers novel insights into the neural mechanisms governing working memory capacity and network function under varying cognitive loads.