Lakshminarasimhan, K. | Search bioRxiv

bioRxiv2017

Local differences in neuronal activation level decorrelate spatially coherent global fluctuations in gamma rhythms

Neuronal coherence is thought to constitute a unique substrate for information transmission distinct from firing rate. However, since the spatial scale of extracellular oscillations typically exceeds that of firing rates, it is unclear whether coherence complements or compromises the rate code. We examined responses in the macaque primary visual cortex and found that fluctuations in gamma-band (~40Hz) neuronal coherence correlated more with firing rate than oscillations in the local-field-potential (LFP). Although the spatial extent of LFP rhythms was broader, that of neuronal coherence was indistinguishable from firing rates. To identify the mechanism, we developed a statistical technique to isolate the rhythmic component of the spiking process and found that above results are explained by an activation-dependent increase in neuronal sensitivity to gamma-rhythmic input. Such adaptive changes in sensitivity to rhythmic inputs might constitute a fundamental homeostatic mechanism that prevents globally coherent inputs from undermining spatial resolution of the neural code.

neuroscience↗

bioRxiv2017

Inferring decoding strategies for multiple correlated neural populations

Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.\n\nAuthor SummaryThe neocortex is structurally organized into distinct brain areas. The role of specific brain areas in sensory perception is typically studied using two kinds of laboratory experiments: those that measure correlations between neural activity and reported percepts, and those that inactivate a brain region and measure the resulting changes in percepts. The two types of experiments have generally been interpreted in isolation, in part because no theory has been able combine their outcomes. Here, we describe a mathematical framework that synthesizes both kinds of results, giving us a new way to assess how different brain areas contribute to perception. When we apply our framework to experiments on behaving monkeys, we discover two models that can explain the perplexing finding that one brain area can predict an animals percepts, even though the percepts are not affected when that brain area is inactivated. The two models ascribe dramatically different efficiencies to brain computation. We show that these two models can be distinguished by an experiment that measures correlations while inactivating different brain areas.

neuroscience↗