No abstract

Neuronal responses are correlated on a range of timescales. Correlations can affect population coding and may play an important role in cortical function. Correlations are known to depend on stimulus drive, behavioral context, and experience, but the mechanisms that determine their properties are poorly understood. Here we make use of the laminar organization of cortex, with its variations in sources of input, local circuit architecture, and neuronal properties, to test whether networks engaged in similar functions but with distinct properties generate different patterns of correlation. We find that slow timescale correlations are prominent in the superficial and deep layers of primary visual cortex (V1) of macaque monkeys, but near zero in the middle layers. Brief timescale correlation (synchrony), on the other hand, was slightly stronger in the middle layers of V1, although evident at most cortical depths. Laminar variations were also apparent in the power of the local field potential, with a complementary pattern for low frequency (<10 Hz) and gamma (30-50 Hz) power. Recordings in area V2 revealed a laminar dependence similar to V1 for synchrony, but slow timescale correlations were not different between the input layers and nearby locations. Our results reveal that cortical circuits in different laminae can generate remarkably different patterns of correlations, despite being tightly interconnected.

Summary Relaying neural signals between cortical areas is central to cognition and sensory processing. The temporal coordination of activity in a source population has been suggested to determine corticocortical signaling efficacy, but others have argued that coordination is functionally irrelevant. We reasoned that if coordination significantly influenced signaling, spiking in downstream networks should be preceded by transiently elevated coordination in a source population. We developed a metric to quantify network coordination in brief epochs, and applied it to simultaneous recordings of neuronal populations in cortical areas V1 and V2 of the macaque monkey. Spiking in the input layers of V2 was preceded by brief epochs of elevated V1 coordination, but this was not the case in other layers of V2. Our results indicate that V1 coordination influences its signaling to direct downstream targets, but that coordinated V1 epochs do not propagate through multiple downstream networks as in some corticocortical signaling schemes.

Summary of recent advancesNeural activity in cortex is correlated, an observation that has traditionally been attributed to neurons receiving input from a shared and limited presynaptic pool. Recent studies have shown that correlations are also strongly influenced by network fluctuations that operate over a range of spatial and temporal scales, extending in some cases across cortical areas. These fluctuations are sensitive to internal states and external drive, so that correlations themselves depend strongly on cognitive state and stimulus properties. Given their potential impact on population coding, this modulation of correlations may play an important role in sensory processing.The activity of cortical neurons is correlated. Correlations are seen as shared fluctuations in responsiveness in the absence of changes in sensory drive or motor output. They occur over a range of time scales, from the precise temporal alignment of spiking (synchrony) to comodulation of firing rate over many seconds [1,2]. They are typically measured by computing cross-correlation functions, which reveal the relationship between a cell's spiking and the firing of another, or by comparing trial-to-trial fluctuations in spike counts evoked by repeated presentations of the same stimulus.Correlations are of interest for three interrelated reasons. First, they can strongly affect population coding, with their impact depending on their relationship to neuronal tuning and on how information is extracted from the population. Second, they may provide a signature of perceptual and cognitive processes that involve modulation of ensemble behavior rather than neuronal firing rates. Third, they can provide insights into the functional architecture and dynamics of cortical networks.The effect of correlations on population coding has been reviewed thoroughly by Averbeck et al. [3, see also 4]. Here we focus on recent studies that have investigated how correlations in cortex are affected by internal states and external drive and on the network dynamics and mechanisms that underlie them. This work, using techniques ranging from intracellular recording to functional MRI, has revealed that correlations are affected by cognitive state, strongly altered by stimulus drive, and arise in part from network fluctuations that occur over a range of spatial and temporal scales.

How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.