Related Experiment Video
Updated: Nov 2, 2025
07:08
Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
Published on: August 1, 2018
8.5K
Effect of Cross-Orientation Normalization on Different Neural Measures in Macaque Primary Visual Cortex
1Centre for Neuroscience, Indian Institute of Science, Bangalore 560012, India.
Cerebral Cortex Communications
|June 7, 2021
Summary
Divisive normalization explains sensory processing. This study shows normalization strength varies across neural measures like spikes and neural oscillations, but a modified model explains these population responses.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Visual Processing
Background:
- Divisive normalization is a key mechanism explaining sensory phenomena.
- Existing models primarily explain single-neuron spiking activity.
- Its applicability to population-level neural measures remains unclear.
Purpose of the Study:
- To investigate if normalization models can explain population-level neural measures.
- To compare normalization strength across different neural signals.
- To determine if a unified model can account for these variations.
Main Methods:
- Presented static and flickering plaid stimuli at varying contrasts to monkeys.
- Recorded multiunit activity (MUA) and local field potentials (LFPs) from the primary visual cortex.
- Quantified modulation in MUA, gamma, high-gamma power, and steady-state visually evoked potential (SSVEP).
Main Results:
- Normalization strength varied across measures: spikes < high-gamma < SSVEP < gamma.
- These differences were observed even under identical stimulus conditions.
- A modified normalization model successfully explained the population response variations.
Conclusions:
- Different neural measures reflect stimulus normalization distinctly.
- A single, adaptable normalization model can account for these varied population responses.
- This provides a unified framework for understanding normalization across neural signals.
