Bottom-Up and Top-Down Input Augment the Variability of Cortical Neurons

Area of Science:

• Neuroscience
• Computational Neuroscience
• Sensory Processing

Background:

• Cortical neurons exhibit inconsistent responses to repeated stimuli, despite underlying stable sensory experiences.
• The source of this neural variability is largely unknown, with a prevailing view of random spike patterns.
• Understanding neural variability is crucial for deciphering brain function and sensory perception.

Purpose of the Study:

• To investigate the origins of spike train irregularity and trial-to-trial variability in cortical neurons.
• To determine the role of bottom-up and top-down inputs in generating neural variability.
• To model the impact of input synchrony on neuronal firing patterns.

Main Methods:

• Reversible inactivation of distant input sources to cortical visual areas in alert primates.
• Analysis of spike train irregularity and trial-to-trial variability of single neurons.
• Computational modeling of neuronal responses to silenced pre-synaptic input.

Main Results:

• Silencing distant bottom-up or top-down inputs reduced spike train irregularity and trial-to-trial variability.
• A computational model reproduced these findings when a fraction of input was silenced, assuming correlated inputs within pools.
• Temporal correlations within excitatory and inhibitory input pools were critical for the model's success.

Conclusions:

• A significant component of cortical neuron variability may stem from synchronous input signals arriving from multiple sources.
• The study challenges the view of purely random spike patterns, highlighting the role of structured input.
• This research provides insights into the mechanisms of neural coding and sensory representation in the cerebral cortex.