Degeneracy Explains Diversity in Interneuronal Regulation of Pattern Separation in Heterogeneous Dentate Gyrus...

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Pattern separation, essential for memory, was theoretically linked to divergent feedforward excitatory connectivity.
• Conflicting evidence suggests local circuit connectivity also plays a critical role in regulating pattern separation.

Purpose of the Study:

• To reconcile divergent views on pattern separation regulation in dentate gyrus (DG) networks.
• To investigate the role of local circuit connectivity and neuronal subtypes in pattern separation under enforced divergent feedforward connectivity.

Main Methods:

• Generated 20,000 heterogeneous DG spiking networks using validated single-neuron models of four DG neuronal subtypes.
• Applied novel quantitative metrics to assess pattern separation performance on morphed input patterns.
• Analyzed network outputs to identify contributions of interneuron subtypes and synaptic weights.

Main Results:

• Divergent feedforward connectivity alone was insufficient for effective pattern separation in most networks (0.23%).
• Three interneuron subtypes significantly contributed to granule cell sparsity and pattern separation, but with high variability.
• Synaptic weight differences and synaptic degeneracy explained the diverse interneuronal regulation of pattern separation.

Conclusions:

• Pattern separation efficacy in DG networks arises from diverse interactions between granule cells and interneurons, not solely feedforward connectivity.
• Synaptic degeneracy is a key mechanism underlying the varied contributions of interneurons to pattern separation.
• Heterogeneous DG networks exhibit greater resilience to synaptic jitter than homogeneous networks.