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Monday, July 20 • 1:00pm - 1:40pm
F3: Neuronal morphology imposes a tradeoff between stability, accuracy and efficiency of synaptic scaling

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Adriano Bellotti, Saeed Aljaberi, Fulvio Forni, Timothy O'Leary

Synaptic scaling is a homeostatic normalization mechanism that preserves relative synaptic strengths by adjusting them with a common factor. This multiplicative change is believed to be critical, since synaptic strengths are involved in learning and memory retention. Further, this homeostatic process is thought to be crucial for neuronal stability, playing a stabilizing role in otherwise runaway Hebbian plasticity [1-3]. Synaptic scaling requires a mechanism to sense total neuron activity and globally adjust synapses to achieve some activity set-point [4]. This process is relatively slow, which places limits on its ability to stabilize network activity [5]. Here we show that this slow response is inevitable in realistic neuronal morphologies. Furthermore, we reveal that global scaling can in fact be a source of instability unless responsiveness or scaling accuracy are sacrificed.

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A neuron with tens of thousands of synapses must regulate its own excitability to compensate for changes in input. The time requirement for global feedback can introduce critical phase lags in a neuron’s response to perturbation. The severity of phase lag increases with neuron size. Further, a more expansive morphology worsens cell responsiveness and scaling accuracy, especially in distal regions of the neuron. Local pools of reserve receptors improve efficiency, potentiation, and scaling, but this comes at a cost. Trafficking large quantities of receptors requires time, exacerbating the phase lag and instability. Local homeostatic feedback mitigates instability, but this too comes at the cost of reducing scaling accuracy.

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Realization of the phase lag instability requires a unified model of synaptic scaling, regulation, and transport. We present such a model with global and local feedback in realistic neuron morphologies (Fig. 1). This combined model shows that neurons face a tradeoff between stability, accuracy, and efficiency. Global feedback is required for synaptic scaling but favors either system stability or efficiency. Large receptor pools improve scaling accuracy in large morphologies but worsen both stability and efficiency. Local feedback improves the stability-efficiency tradeoff at the cost of scaling accuracy. This project introduces unexplored constraints on neuron size, morphology, and synaptic scaling that are weakened by an interplay between global and local feedback.

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The authors are supported by European Research Council Grant FLEXNEURO (716643) as well as Abu Dhabi National Oil Company, NIH OxCam Scholars program, and Gates Cambridge Trust


1. Royer, Sébastien, and Denis Paré. "Conservation of total synaptic weight through balanced synaptic depression and potentiation." Nature 422, no. 6931 (2003): 518-522. 2. Chen, Jen-Yung, et al. "Heterosynaptic plasticity prevents runaway synaptic dynamics." Journal of Neuroscience 33, no. 40 (2013): 15915-15929. 3. Chistiakova, Marina, et al. "Homeostatic role of heterosynaptic plasticity: models and experiments." Frontiers in computational neuroscience 9 (2015): 89. 4. Turrigiano, Gina G. "The self-tuning neuron: synaptic scaling of excitatory synapses." Cell 135, no. 3 (2008): 422-435. 5. Zenke, Friedemann, Guillaume Hennequin, and Wulfram Gerstner. "Synaptic plasticity in neural networks needs homeostasis with a fast rate detector." PLoS computational biology 9, no. 11 (2013).


Adriano Bellotti

Department of Engineering, University of Cambridge

Monday July 20, 2020 1:00pm - 1:40pm CEST
  Featured Talk, Neurons to Circuits
  • Moderator Annalisa Scimemi; Tatiana Kameneva