The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding

  title={The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding},
  author={Robert Meyer and Josef Ladenbauer and Klaus Obermayer},
  journal={Frontiers in Computational Neuroscience},
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… 
2 Citations

Noise sharing and Mexican-hat coupling in a stochastic neural field.

It is confirmed that a spatial pattern that is damped in time in a deterministic system may be sustained and amplified by stochasticity and found that spatially smoothed noise alone causes pattern formation even without direct spatial coupling.

Synergistic population encoding and precise coordinated variability across interlaminar ensembles in the early visual system

This work uses Neuropixels to simultaneously record tens to hundreds of single neurons in primary visual cortex (V1) and lateral geniculate nucleus (LGN) of mice and estimates population information, finding a mix of synergistic and redundant coding.



Negative Correlations in Visual Cortical Networks.

It is found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy.

The Asynchronous State in Cortical Circuits

It is shown theoretically that recurrent neural networks can generate an asynchronous state characterized by arbitrarily low mean spiking correlations despite substantial amounts of shared input, which generates negative correlations in synaptic currents which cancel the effect of sharedinput.

Stochastic transitions into silence cause noise correlations in cortical circuits

The hypothesis that correlations are dominated by neuronal coinactivation is investigated: the occurrence of brief silent periods during which all neurons in the local network stop firing, which suggests that the observed changes in correlation are due to changes in the likeliness of the microcircuit to transiently cease firing.

Origin of information-limiting noise correlations

This study indicates that noise at the sensory periphery could have a major effect on cortical representations in widely studied discrimination tasks and provides an analytical framework to understand the functional relevance of different sources of experimentally measured correlations.

The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding

It is suggested that quantities are represented as rate codes in ensembles of 50–100 neurons, which implies that single neurons perform simple algebra resembling averaging, and that more sophisticated computations arise by virtue of the anatomical convergence of novel combinations of inputs to the cortical column from external sources.

Laminar dependence of neuronal correlations in visual cortex.

It is found that slow timescale correlations are prominent in the superficial and deep layers of primary visual cortex of macaque monkeys, but near zero in the middle layers.

Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons

Sparsely-connected networks of conductance-based integrate-and-fire neurons with balanced excitatory and inhibitory connections with finite axonal propagation speed are studied, finding that first and second-order “mean-field” statistics of such networks do not depend on the details of the connectivity at a microscopic scale.

Spatial and Temporal Scales of Neuronal Correlation in Primary Visual Cortex

The spatial extent and functional specificity of correlated spontaneous and evoked activity and the circuit mechanism they imply provide new constraints on the functional role that correlation may play in visual processing are investigated.

The spatial structure of correlated neuronal variability

This work combines computational models and in vivo recordings to study the relationship between the spatial structure of connectivity and correlated variability in neural circuits and shows that incorporating distance-dependent connectivity improves the extent to which balanced network theory can explain correlated neural variability.

Population coding in mouse visual cortex: response reliability and dissociability of stimulus tuning and noise correlation

This paper used two-photon calcium imaging data to relate the performance of different methods for studying population coding to their underlying assumptions and shows that population coding cannot be approximated by a simple summation of inputs, but is heavily influenced by factors such as input reliability and noise correlation structure.