Abstract
Cortical networks exhibit 'global oscillations', in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a 'prediction error' while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code.
Highlights
Oscillations are a prominent feature of cortical activity
We show that, in a network of leaky integrate-and-fire neurons (LIF) optimized for efficient coding, this trade-off is best met by adding noise to the network to ensure that: (i) neural spike trains are entrained to a global oscillatory rhythm, but (ii) only a small fraction of cells fire on each oscillation cycle
This prediction error is reflected in neural membrane potentials
Summary
Oscillations are a prominent feature of cortical activity. In sensory areas, one typically observes ’global oscillations’ in the gamma-band range (30–80 Hz), alongside single neuron responses that are irregular and sparse (Buzsaki and Wang, 2012; Yu and Ferster, 2010). The magnitude and frequency of gamma-band oscillations are modulated by changes to the sensory environment (e.g. visual stimulus contrast (Henrie and Shapley, 2005) and behavioural state (e.g. attention [Fries et al, 2001]) of the animal This has led a number of authors to propose that neural oscillations play a fundamental role in cortical computation (Fries, 2009; Engel et al, 2001). A prevalent theory of sensory processing, the ’efficient coding hypothesis’, posits that the role of early sensory processing is to communicate information about the environment using a minimal number of spikes (Barlow, 1961) This implies that the responses of individual neurons should be as asynchronous as possible, so that they do not communicate redundant information (Simoncelli and Olshausen, 2001). Oscillations are generally seen as a bad thing for efficient rate coding, as they tend to synchronise neural responses, and introduce redundancy
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