Coping with variability in small neuronal networks.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Neural circuits exhibit substantial animal-to-animal variability in intrinsic and synaptic parameters.
• Despite this variation, functional outputs like motor patterns are often maintained.
• The heartbeat central pattern generator (CPG) in leeches provides a model to study this phenomenon.

Purpose of the Study:

• To investigate how animal-to-animal variation in synaptic strength affects coordinated motor output.
• To test the hypothesis that maintained relative synaptic strengths preserve functional output.
• To understand the mechanisms underlying functional robustness in neural networks.

Main Methods:

• Experimental analysis of segmental heart motor neurons and their four premotor interneuron inputs in medicinal leeches.
• Measurement of synaptic strength profiles and temporal input patterns.
• Analysis of motor neuron output phase progression across a cohort of 12 animals.

Main Results:

• Functional output is maintained despite significant animal-to-animal variation in absolute synaptic strengths.
• Relative synaptic strengths are not strictly maintained across animals, challenging initial hypotheses.
• Variations in individual input strengths did not strongly correlate with output phase.
• Output temporal patterns varied across individuals, similar to input patterns.

Conclusions:

• The number of inputs and the variability of their temporal patterns can buffer the influence of individual synaptic strengths.
• Neural networks can achieve consistent functional output through diverse configurations of synaptic parameters.
• Each animal's nervous system may adopt a unique strategy to produce coordinated motor behavior.