Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network.

Christoph Hartmann, Andreea Lazar, Bernhard Nessler, Jochen Triesch

Abstract: Author Summary Neural recordings seem very noisy. If the exact same stimulus is shown to an animal multiple times, the neural response will vary substantially. In fact, the activity of a single neuron shows many features of a random process. Furthermore, the spontaneous activity occurring in the absence of any sensory stimulus, which is usually considered a kind of background noise, often has a magnitude comparable to the activity evoked by stimulus presentation and interacts with sensory inputs in interesting ways. Here we show that the key features of neural variability and spontaneous activity can all be accounted for by a simple and completely deterministic neural network learning a predictive model of its sensory inputs. The network’s deterministic dynamics give rise to structured but variable responses matching key experimental findings obtained in different mammalian species with different recording techniques. Our results suggest that the notorious variability of neural recordings and the complex features of spontaneous brain activity could reflect the dynamics of a largely deterministic but highly adaptive network learning a predictive model of its sensory environment.