Coherence Potentials: Loss-Less, All-or-None Network Events in the Cortex

Abstract

Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks.

Author Summary

Perception and behavior are thought to arise from transient associations among sub-groups of nerve cells in the brain. However, identifying which of the many active neurons are associated at any given time and how poses a challenge. Here we show that when the composite activity of a local group of cortical neurons, measured as a complex waveform in the extracellular field, exceeds a threshold, its activity pattern extending up to hundreds of milliseconds occurs without distortion at other cortical sites via fast synaptic transmission. We call these all-or-none propagated patterns “coherence potentials”, in analogy to action potentials at the single cell level. In contrast to action potentials, which are stereotypical and thus capable only of binary coding, coherence potentials are diverse and complex waveforms that can serve as a high-dimensional parameter for encoding information. The non-linear relationship between local activity and its extent of replicated spread suggests a “tipping point” that bears analogy to the propagation of innovations and economic behavior in social networks, which can spread rapidly once they have garnered a local critical mass.