Abstract
Noise correlations are a common feature of neural responses and have been observed in many cortical areas across different species. These correlations can influence information processing by enhancing or diminishing the quality of the neural code, but the origin of these correlations is still a matter of controversy. In this computational study we explore the hypothesis that noise correlations are the result of local recurrent excitatory and inhibitory connections. We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels. Noise correlations decay with distance between neurons but are only observed if the range of excitatory connections is smaller than the range of inhibitory connections (“Mexican hat” connectivity) and if the connection strengths are sufficiently strong. These correlations arise from a moving blob-like structure of evoked activity, which is absent if inhibitory interactions have a smaller range (“inverse Mexican hat” connectivity). Spatially structured external inputs fixate these blobs to certain locations and thus effectively reduce noise correlations. We further investigated the influence of these network configurations on stimulus encoding. On the one hand, the observed correlations diminish information about a stimulus encoded by a network. On the other hand, correlated activity allows for more precise encoding of stimulus information if the decoder has only access to a limited amount of neurons.
Highlights
One of the fundamental problems in neuroscience is deciphering the neural code and understanding how the brain encodes sensory stimuli
We study the interaction of the neural dynamics with structured input, which is derived from an orientation map model of primary visual cortex
In networks of adaptive exponential integrate and fire neurons, Mexican hat coupling with wider inhibitory than excitatory connectivity spread leads to noise correlations
Summary
One of the fundamental problems in neuroscience is deciphering the neural code and understanding how the brain encodes sensory stimuli. During the past decades the analysis of neural coding has shifted from single cells to investigating population codes due to the development of multi-electrode recordings as well as improved theoretical models. An important aspect regarding population coding is whether neural responses are correlated, especially when driven by the same stimulus. In this case one speaks of noise correlations or shared variability (Cohen and Kohn, 2011; Hansen et al, 2012). For example, been widely observed in the visual cortex (Kohn and Smith, 2005; Martin and Schröder, 2013), where the magnitude of pairwise noise correlations decays with the distance between cell pairs (Smith and Kohn, 2008; Solomon et al, 2014). The underlying mechanisms and the role of noise correlations in information processing are, not well understood
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