[Artificial neural networks in Neurosciences].

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Intelligence

Background:

• Physiological changes, such as neurotransmitter decrease, impact cognitive functions in aging.
• Recognition tasks in older adults are complex and influenced by neurobiological factors.

Purpose of the Study:

• To investigate the relationship between physiological changes and cognitive performance in older adults using artificial neural networks.
• To model the effect of neurotransmitter decrease on recognition behavior in the elderly.

Main Methods:

• Design of a novel artificial neural network inspired by the human nervous system.
• Development of input coding strategies and an orthogonalization process for pattern recognition.
• Simulation of experimental results by adjusting activation thresholds within the artificial neural network.

Main Results:

• The artificial neural network successfully recognized learned patterns.
• Adjusting activation thresholds allowed the network to simulate the observed behavior of older adults in recognition tasks.
• The network's design, inspired by the nervous system, provided a viable model for cognitive processes.

Conclusions:

• Artificial neural networks can effectively confirm the link between physiological changes and cognitive alterations.
• The developed model offers insights into how neurotransmitter levels influence cognitive tasks in aging populations.
• The study highlights the potential of neuro-inspired computing for understanding age-related cognitive decline.