Abstract
Spontaneous activity is a fundamental characteristic of the developing nervous system. Intriguingly, it often takes the form of multiple structured assemblies of neurons. Such assemblies can form even in the absence of afferent input, for instance in the zebrafish optic tectum after bilateral enucleation early in life. While the development of neural assemblies based on structured afferent input has been theoretically well-studied, it is less clear how they could arise in systems without afferent input. Here we show that a recurrent network of binary threshold neurons with initially random weights can form neural assemblies based on a simple Hebbian learning rule. Over development the network becomes increasingly modular while being driven by initially unstructured spontaneous activity, leading to the emergence of neural assemblies. Surprisingly, the set of neurons making up each assembly then continues to evolve, despite the number of assemblies remaining roughly constant. In the mature network assembly activity builds over several timesteps before the activation of the full assembly, as recently observed in calcium-imaging experiments. Our results show that Hebbian learning is sufficient to explain the emergence of highly structured patterns of neural activity in the absence of structured input.
Highlights
Developing nervous systems exhibit ongoing neural activity even in the absence of sensory stimulation [1]
We draw on calcium imaging experiments in zebrafish larvae to construct a computational model of assembly formation in neural networks without correlated input
Our model shows how a simple learning rule can explain the emergence and dynamics of patterned neural activity in the early nervous system, and predicts a continual reorganisation of assemblies despite maintaining stable statistical properties
Summary
Developing nervous systems exhibit ongoing neural activity even in the absence of sensory stimulation [1]. Theoretical progress has been made towards understanding how plasticitydriven self-organisation can explain some of the statistical properties of synaptic wiring in cortex [7,8,9,10,11,12,13], and on the development and dynamics of structured spontaneous activity in computational models of neural circuits [11, 14,15,16,17] It has been shown how multiple forms of synaptic plasticity and homeostasis can interact to develop neural assemblies from repeated sensory stimulation [18], and how trained memories can be retrieved as the activation of neural assemblies both spontaneously [18] and by partial cues [15] in detailed circuit models. Receptive field structure in networks with feedforward excitation and lateral inhibition is unstable, with an autocorrelation that decays to zero despite continued stimulation with the same set of stimuli [20]
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