An objective function for STDP

We introduce a predictive objective function for the rate aspect of spike-timing dependent plasticity (STDP), i.e., ignoring the effects of synchrony of spikes but looking at spiking {\em rate changes}. The proposed weight update is proportional to the presynaptic spiking (or firing) rate times the {\em temporal change} of the integrated postsynaptic activity. We present an intuitive explanation for the relationship between spike-timing and weight change that arises when the weight change follows this rule. Spike-based simulations agree with the proposed relationship between spike timing and the temporal change of postsynaptic activity. They show a strong correlation between the biologically observed STDP behavior and the behavior obtained from simulations where the weight change follows the gradient of the predictive objective function.

[1]  Karl J. Friston,et al.  Free-energy and the brain , 2007, Synthese.

[2]  József Fiser,et al.  Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment , 2011, Science.

[3]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[4]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[5]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[6]  Xiaohui Xie,et al.  Spike-based Learning Rules and Stabilization of Persistent Neural Activity , 1999, NIPS.

[7]  Geoffrey E. Hinton,et al.  A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.

[8]  Daniel Cownden,et al.  Random feedback weights support learning in deep neural networks , 2014, ArXiv.

[9]  Yoshua Bengio,et al.  Towards Biologically Plausible Deep Learning , 2015, ArXiv.

[10]  W. Gerstner,et al.  Spike-Timing-Dependent Plasticity: A Comprehensive Overview , 2012, Front. Syn. Neurosci..

[11]  G. Shepherd The Synaptic Organization of the Brain , 1979 .

[12]  Wulfram Gerstner,et al.  Stochastic variational learning in recurrent spiking networks , 2014, Front. Comput. Neurosci..

[13]  Ronald J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[14]  Ila R Fiete,et al.  Gradient learning in spiking neural networks by dynamic perturbation of conductances. , 2006, Physical review letters.

[15]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[16]  Pascal Vincent,et al.  A Connection Between Score Matching and Denoising Autoencoders , 2011, Neural Computation.

[17]  Nando de Freitas,et al.  An Introduction to MCMC for Machine Learning , 2004, Machine Learning.

[18]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[19]  Wulfram Gerstner,et al.  A neuronal learning rule for sub-millisecond temporal coding , 1996, Nature.

[20]  Yoshua Bengio,et al.  What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..