Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing

Abstract

Neuronal responses to sensory stimuli are not only driven by feedforward sensory pathways but also depend upon intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation. To understand how these factors together regulate cortical dynamics, we recorded simultaneously spontaneous and somatosensory-evoked multiunit activity from primary somatosensory cortex and from the locus coeruleus (LC) (the neuromodulatory nucleus releasing norepinephrine) in urethane-anesthetized rats. We found that bursts of ipsilateral-LC firing preceded by few tens of milliseconds increases of cortical excitability, and that the 1-to 10-Hz rhythmicity of LC discharge appeared to increase the power of deltaband (1-4 Hz) cortical synchronization. To investigate quantitatively how LC firing might causally influence spontaneous and stimulus-driven cortical dynamics, we then constructed and fitted to these data a model describing the dynamical interaction of stimulus drive, ongoing synchronized cortical activity, and noradrenergic neuromodulation. The model proposes a coupling between LC and cortex that can amplify delta-range cortical fluctuations, and shows how suitably timed phasic LC bursts can lead to enhanced cortical responses to weaker stimuli and increased temporal precision of cortical stimulus-evoked responses. Thus, the temporal structure of noradrenergic modulation may selectively and dynamically enhance or attenuate cortical responses to stimuli. Finally, using the model prediction of single-trial cortical stimulus-evoked responses to discount single-trial state-dependent variability increased by ∼70% the sensory information extracted from cortical responses. This suggests that downstream circuits may extract information more effectively after estimating the state of the circuit transmitting the sensory message.oscillations | state dependence | dynamical systems | information coding | somatosensation R esponsiveness of cortical sensory neurons is state dependent. In other words, the neural responses to a sensory stimulus do not only depend on the features of extrinsic sensory inputs but also on intrinsic network variables that can be collectively defined as the "network state" (1). Cortical sensory neurons receive information about the external world from peripheral receptors via feedforward sensory pathways. However, the abundance of recurrent and feedback connectivity (2) may generate ongoing activity that shapes the background on which the afferent information is processed (3). In addition, neuromodulatory inputs from neurochemically specialized brain nuclei that are not part of the direct spino-thalamo-cortical pathway can modulate the dynamics of cortical networks (4), as well as control the animal's behavioral state. The concurrent integration of information about the external world and about internal states is likely to be central for computational operations of cortical circuits and for the production of complex behavior, yet its mechanisms and implications for...

History-dependent variability in population dynamics during evidence accumulation in cortex

“…For example, the neuron-neuron activity correlation structure remaining after the subtraction of activity resulting from a selected subset of task variables, typically referred to as ‘noise correlations’, may reflect, in some cases, ‘residual correlations’ due to additional signals in the PPC. Together, our results therefore combine and put into a new context features identified in previous studies, including heterogeneous activity patterns across neurons 5,17,18,20 , distributed representations of task stimuli including for non-relevant inputs 19,20 , activity-dependent processing of stimuli 35,36 , and the encoding of previous stimuli that indicates stimulus reverberation 30-33,37-43 .…”

State-Dependent Decoding Algorithms Improve the Performance of a Bidirectional BMI in Anesthetized Rats

“…These results suggest that the detailed time course of ongoing activity carries important information about changes in excitability of the network and strongly influences the responses to stimuli, as also put forward by recent studies using dynamical systems to model ongoing activity (Curto et al, 2009; Safaai et al, 2015). …”

“…The simplest way to take advantage of state dependent rules is, assuming that state-induced variability is additive, to first estimate the amount of single-trial firing due to state-induced variability and to discount it by subtracting it out (Safaai et al, 2015; Panzeri et al, 2016). Although this suggestion has not been tested yet in BMIs, studies that have used models of state dependence of neural responses based on the observation of neural activity encourage us to think that this idea may work.…”

The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance

“…There is a wealth of evidence to suggest that neuromodulatory inputs can have complex, non-linear effects on network organization and behavior (Bargmann and Marder, 2013), perhaps as a result of the balance between the ‘top-down’ attentional modulation of network architecture (Sara, 2009) and ‘bottom-up’ neuromodulatory input from the brainstem (Safaai et al, 2015). The network of right-lateralized cortical regions consistently associated with elevations in integration in our study provides further support for this hypothesis (Figure 4c), as ascending noradrenergic inputs preferentially impact neural function within the right cortical hemisphere (Pearlson and Robinson, 1981).…”