Harnessing the Variability of Neuronal Activity: From Single Neurons to Networks

Abstract

Neurons and networks of the brain may use various strategies of computation to provide the neural substrate for sensation, perception, or cognition. To simplify the scenario, two of the most commonly cited neural codes are firing rate and temporal coding, whereby firing rates are typically measured over a longer duration of time (i.e., seconds or minutes), and temporal codes use shorter time windows (i.e., 1 to 100 ms). However, it is possible that neurons may use other strategies. Here, we highlight three methods of computation that neurons, or networks, of the brain may use to encode and/or decode incoming activity. First, we explain how single neurons of the brain can utilize a neuronal oscillation, specifically by employing a ‘spike-phase’ code wherein responses to stimuli have greater reliability, in turn increasing the ability to discriminate between stimuli. Our focus was to explore the limitations of spike-phase coding, including the assumptions of low firing rates and precise timing of action potentials. Second, we examined the ability of single neurons to track the onset of network bursting activity, namely ‘burst predictors’. In addition, we show that burst predictors were less susceptible to an in vitro model of neuronal stroke (i.e., excitotoxicity). Third, we discuss the possibility of distributed processing with neuronal networks of the brain. Specifically, we show experimental and computational evidence supporting the possibility that the population activity of cortical networks may be useful to downstream classification. Furthermore, we show that when network activity is highly variable across time, there is an increase in the ability to linearly separate the spiking activity of various networks. Overall, we use the results of both experimental and computational methods to highlight three strategies of computation that neurons and networks of the brain may employ.