Spike-Time-Dependent Plasticity and Heterosynaptic Competition Organize Networks to Produce Long Scale-Free Sequences of Neural Activity

• DOI: 10.1016/j.neuron.2010.02.003
• Publication Date: 2010-02-25T10:46:34Z
• Authors: Ila R. Fiete; Walter Senn; Claude Z.H. Wang; Richard H.R. Hahn...
• Keywords: Neurons; 0301 basic medicine; SYSBIO; Neuronal Plasticity; High Vocal Center; Neuroscience(all); Models, Neurological; Neural Conduction; 2800 General Neuroscience; Action Potentials; Membrane Potentials; Electrophysiology; 03 medical and health sciences; SIGNALING; Synapses; 570 Life sciences; biology; Animals; Learning; Finches; Nerve Net; SYSNEURO; 10194 Institute of Neuroinformatics

Sequential neural activity patterns are as ubiquitous as the outputs they drive, which include motor gestures and sequential cognitive processes. Neural sequences are long, compared to the activation durations of participating neurons, and sequence coding is sparse. Numerous studies demonstrate that spike-time-dependent plasticity (STDP), the primary known mechanism for temporal order learning in neurons, cannot organize networks to generate long sequences, raising the question of how such networks are formed. We show that heterosynaptic competition within single neurons, when combined with STDP, organizes networks to generate long unary activity sequences even without sequential training inputs. The network produces a diversity of sequences with a power law length distribution and exponent -1, independent of cellular time constants. We show evidence for a similar distribution of sequence lengths in the recorded premotor song activity of songbirds. These results suggest that neural sequences may be shaped by synaptic constraints and network circuitry rather than cellular time constants.