Learning Nonlinear Dynamics in Efficient, Balanced Spiking Networks Using Local Plasticity Rules


  • Alireza Alemi ENS and UC Davis
  • Christian Machens Champalimaud Centre for the Unknown
  • Sophie Deneve Ecole Normale Superieure, Paris
  • Jean-Jacques Slotine MIT


Learning, Local Plasticity Rules, Spiking Neural Networks, Efficiency, Robustness, Dynamical Systems, Nonlinear Dynamics, E-I Balance


The brain uses spikes in neural circuits to perform many dynamical computations. The computations are performed with properties such as spiking efficiency, i.e. minimal number of spikes, and robustness to noise. A major obstacle for learning computations in artificial spiking neural networks with such desired biological properties is due to lack of our understanding of how biological spiking neural networks learn computations. Here, we consider the credit assignment problem, i.e. determining the local contribution of each synapse to the network's global output error, for learning nonlinear dynamical computations in a spiking network with the desired properties of biological networks. We approach this problem by fusing the theory of efficient, balanced neural networks (EBN) with nonlinear adaptive control theory to propose a local learning rule. Locality of learning rules are ensured by feeding back into the network its own error, resulting in a learning rule depending solely on presynaptic inputs and error feedbacks. The spiking efficiency and robustness of the network are guaranteed by maintaining a tight excitatory/inhibitory balance, ensuring that each spike represents a local projection of the global output error and minimizes a loss function. The resulting networks can learn to implement complex dynamics with very small numbers of neurons and spikes, exhibit the same spike train variability as observed experimentally, and are extremely robust to noise and neuronal loss.




How to Cite

Alemi, A., Machens, C., Deneve, S., & Slotine, J.-J. (2018). Learning Nonlinear Dynamics in Efficient, Balanced Spiking Networks Using Local Plasticity Rules. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11320



AAAI Technical Track: Cognitive Modeling