NETDYNCORTEX

Network dynamics of auditory cortex and the impact of correlations on the encoding of sensory information

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 Obiettivo del progetto (Objective)

'Populations of neurons in the cerebral cortex represent sensory information, motor commands and other cognitive functions in their seemingly stochastic activity. Population coding works by distributing the information over the spiking activity of many neurons because single neuronal activity is very unreliable. The statistical structure of the variability across the neural population, i.e. the correlations among neurons, has an enormous impact on the encoding of information in the activity of a neuronal population.

Because correlations are viewed as a consequence of shared inputs between nearby neurons, the stochasticity is thought to be an inevitable consequence of the hard-wired connectivity and to limit the efficiency of sensory coding. We have recently shown that recurrent neural networks can generate an asynchronous state with arbitrarily low correlations despite large amounts of shared input (Renart, de la Rocha et al 2010). This implies that correlations are not necessarily the consequence of shared inputs and that encoding in the brain need not be intrinsically stochastic.

Our goal is to investigate the origin and functional consequences of the observed cortical stochasticity by studying: (1) the relation between brain state, circuit dynamics and correlations and (2) by quantifying their impact on sensory information representation. We will combine recordings of the spiking activity of large cell populations (50-150) in vivo, with the analysis of computational network models. We will characterize the statistics of spontaneous and stimulus-evoked activity in auditory cortex of (1) anesthetized rats and (2) freely moving rats performing a sensory discrimination task. We will quantify the encoding efficiency of auditory circuits across brain states and will develop a computational model to provide a mechanistic understanding of the data. The results will help to elucidate the neuronal correlates of auditory perception and the basis of a neural code.'

Introduzione (Teaser)

The seemingly random activity of billions of cortical neurons is responsible for all we think, feel and do. A closer look at correlations in neuronal activity critical to neural information encoding has shed light on mechanisms of perception.

Descrizione progetto (Article)

Depolarisation or hyperpolarisation of the resting membrane potential or spike activity that happens when a depolarisation threshold is crossed are measures of neuronal activity. Electrical activity can be recorded in single neurons or populations (a field potential average).

Individual cortical neurons have also been shown to switch spontaneously between an up state (slightly depolarised) and a down state (hyperpolarised). These fluctuations are more conspicuous during synchronised slow-wave oscillations recorded from populations of cells, and the two are thought to be related.

Scientists investigated the stochastic nature of cortical networks with EU funding of the NETDYNCORTEX (Network dynamics of auditory cortex and the impact of correlations on the encoding of sensory information) project.

Animals under urethane anaesthesia exhibit patterns of cortical activity similar to those seen in non-anaesthetised animals. Scientists studied the periods of up and down activity in auditory cortex of anaesthetised rats during activity similar to that of slow-wave sleep. The periods were more irregular than previously thought, suggesting the two phenomena may not be related. Computational network models pointed to possible mechanisms of up and down activity and functions.

The team then studied pair-wise noise correlations, a phenomenon by which two nearby cells tend to share a part of the statistical variability in their spiking patterns. Interestingly, scientists showed that the correlations were primarily due to periods during which all neurons have no spiking activity, in contrast to previous studies arguing the correlations are due to shared anatomical input. A model confirmed that possibility.

A computational model coupling a standard sensory circuit to a standard decision-making circuit was used to make predictions about the role of neuronal fluctuations on perceptual decision making. The model explained an important contradiction in the literature and predictions were confirmed in recordings from monkeys. Additional studies investigated the role of recent experience on predictions of likely stimuli in rats performing an auditory discrimination task.

NETDYNCORTEX highlighted the complexity of the relationship between stochastic non-linear dynamics present in cortical circuits, underlying mechanisms and perception. The data have shed light on potential cortical mechanisms of information processing and establish tools for further investigation.