A novel mutual information estimator to measure spike train correlations in a model thalamocortical network

Abstract

The impact of thalamic state on information transmission to the cortex remains poorly understood. This limitation exists due to the rich dynamics displayed by thalamocortical networks and because of inadequate tools to characterize those dynamics. Here, we introduce a novel estimator of mutual information and use it to determine the impact of a computational model of thalamic state on information transmission. Using several criteria, this novel estimator, which uses an adaptive partition, is shown to be superior to other mutual information estimators with uniform partitions when used to analyze simulated spike train data with different mean spike rates, as well as electrophysiological data from simultaneously recorded neurons. When applied to a thalamocortical model, the estimator revealed that thalamocortical cell T-type calcium current conductance influences mutual information between the input and output from this network. In particular, a T-type calcium current conductance of about 40 nS appears to produce maximal mutual information between the input to this network (conceptualized as afferent input to the thalamocortical cell) and the output of the network at the level of a layer 4 cortical neuron. Furthermore, at particular combinations of inputs to thalamocortical and thalamic reticular nucleus cells, thalamic cell bursting correlated strongly with recovery of mutual information between thalamic afferents and layer 4 neurons. These studies suggest that the novel mutual information estimator has advantages over previous estimators, and that thalamic reticular nucleus activity can enhance mutual information between thalamic afferents and thalamorecipient cells in the cortex.

Projection from the Amygdala to the Thalamic Reticular Nucleus Amplifies Cortical Sound Responses

“…The effects were stronger in the MGB than the AC, further suggesting that the MGB serves as a relay for cortico-collicular control. These effects could be accounted for by a three-cell model of the TRN-MGB-AC connections ( Figures 3A and 4), with the critical effect provided by the difference in timing and magnitude of the inhibition that the TRN delivered to the MGB, consistent with previous thalamocortical models (Gribkova et al, 2018;Haas and Landisman, 2012;Pham and Haas, 2018;Willis et al, 2015). Our study thus establishes an important pathway connecting the emotional and sensory processing centers that potentially drives changes in auditory perception as a result of emotional learning.…”

“…We constructed a three-cell model of MGB, TRN, and AC neurons ( Figure 4). As all thalamic neurons exhibit bursts of spikes resulting from T currents (Gribkova et al, 2018;Huguenard, 1996;Willis et al, 2015), we used a bursting neuron model for our TRN and MGB neurons (Haas and Landisman, 2012;Traub et al, 2005a). Our simulation code is available at https://github.com/jhaaslab/ trn_aud.…”

Role of NMDAR plasticity in a computational model of synaptic memory

“…6 C,D). Using the MI estimator AIMIE 37 , we show that there is a greater degree of similarity between the original signal and recall signal reconstructed from SITDL simulations (Fig. 6 E).…”

“…A mutual information (MI) estimator (Adaptive partition using Interspike intervals MI Estimator (AIMIE)), was used to compare recall signal and original glutamate signal 37 . Short signals were repeated with proper periodicity, such that each signal had at least 4000 spikes for more accurate MI estimation.…”

“…AIMIE has been shown to work well with spike time series with disparate firing rates and may provide more accurate estimates of MI than several other commonly used MI estimators. Details on AIMIE’s calculations and its use in information flow analysis of a spiking network are provided in previous work 37 .…”