Abstract
How information in the nervous system is encoded by patterns of action potentials (i.e. spikes) remains an open question. Multi-neuron patterns of single spikes are a prime candidate for spike time encoding but their temporal variability requires further characterisation. Here we show how known sources of spike count variability affect stimulus-evoked spike time patterns between neurons separated over multiple layers and columns of adult rat somatosensory cortex in vivo. On subsets of trials (clusters) and after controlling for stimulus-response adaptation, spike time differences between pairs of neurons are “time-warped” (compressed/stretched) by trial-to-trial changes in shared excitability, explaining why fixed spike time patterns and noise correlations are seldom reported. We show that predicted cortical state is correlated between groups of 4 neurons, introducing the possibility of spike time pattern modulation by population-wide trial-to-trial changes in excitability (i.e. cortical state). Under the assumption of state-dependent coding, we propose an improved potential encoding capacity.
Highlights
How information in the nervous system is encoded by patterns of action potentials remains an open question
This paper aims to better characterise shared spike time variability in the somatosensory cortex over repeated trials of single sensory stimuli, as this constrains the form in which spike times encode sensory information
Single deflections were made at a certain frequency, such that the intertrial-interval was 1/f
Summary
How information in the nervous system is encoded by patterns of action potentials (i.e. spikes) remains an open question. We show how known sources of spike count variability affect stimulus-evoked spike time patterns between neurons separated over multiple layers and columns of adult rat somatosensory cortex in vivo. This paper aims to better characterise shared spike time variability in the somatosensory cortex over repeated trials of single sensory stimuli, as this constrains the form in which spike times encode sensory information. Neurons across the population respond with either higher or lower spike counts depending on the population excitability level on a single trial (Fig. 1a, centre). Such shared variability has been explained by an additive interaction of stimulus-evoked activity with spontaneous background. We characterise how precise spike time representations are modulated by the shared excitability-level of spatially separated neurons on single trials
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