Independent Component Analysis in Spiking Neurons

Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition.Author Summary: How the brain learns to encode and represent sensory information has been a longstanding question in neuroscience. Computational theories predict that sensory neurons should reduce redundancies between their responses to a given stimulus set in order to maximize the amount of information they can encode. Specifically, a powerful set of learning algorithms called Independent Component Analysis (ICA) and related models, such as sparse coding, have emerged as a standard for learning efficient codes for sensory information. These algorithms have been able to successfully explain several aspects of sensory representations in the brain, such as the shape of receptive fields of neurons in primary visual cortex. Unfortunately, it remains unclear how networks of spiking neurons can implement this function and, even more difficult, how they can learn to do so using known forms of neuronal plasticity. This paper solves this problem by presenting a model of a network of spiking neurons that performs ICA-like learning in a biologically plausible fashion, by combining three different forms of neuronal plasticity. We demonstrate the model's effectiveness on several standard sensory learning problems. Our results highlight the importance of studying the interaction of different forms of neuronal plasticity for understanding learning processes in the brain.