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Eye Movements in Visual Duration Perception: Disentangling Stimulus from Time in Predecisional Processes
Published on: January 19, 2024
The timing of vision - how neural processing links to different temporal dynamics
Timothée Masquelier1, Larissa Albantakis, Gustavo Deco
1Unit for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra Barcelona, Spain.
Frontiers in Psychology
|July 13, 2011
Summary
This review models visual perception using spiking neural networks, revealing distinct processing modes for object recognition, discrimination, and attention. Simulations confirm biologically inspired models explain neural dynamics and coding, like phase-of-firing, crucial for efficient visual processing.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Artificial Intelligence
Background:
- Visual perception involves complex neural processing, with distinct modes for different tasks.
- Biologically inspired neural networks offer a powerful tool for modeling these processes.
- Understanding neural dynamics is key to deciphering visual perception.
Purpose of the Study:
- To model the neural correlates of visual perception using biologically inspired spiking neural networks.
- To investigate distinct processing modes in the visual system and their underlying neural mechanisms.
- To explore the role of dynamical aspects, including oscillatory activity and spike timing-dependent plasticity (STDP), in visual processing.
Main Methods:
- Simulations of biologically inspired networks of spiking neurons.
- Modeling feedforward processing for rapid object recognition using latency coding and STDP.
- Modeling recurrent networks with long time-constants for evidence accumulation in perceptual discrimination.
- Incorporating top-down attentional signals to modulate neural competition and oscillatory activity.
- Investigating phase-of-firing coding and its decoding by STDP.
Main Results:
- Simulations confirm the plausibility and efficiency of feedforward models for rapid object recognition.
- Recurrent networks with specific properties explain evidence accumulation and perceptual discrimination.
- Attentional modulation of neural competition and oscillatory activity enhances information processing.
- Oscillatory activity leads to faster reaction times and enhanced information transfer, supporting phase-of-firing coding.
Conclusions:
- Biologically inspired spiking neural networks can effectively model diverse visual perception modes.
- Dynamical aspects, including STDP, recurrent connectivity, and oscillations, are crucial for visual processing.
- The models provide explanations for experimental observations and suggest future research directions in natural vision and bio-inspired systems.
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