Creation of Neuronal Ensembles and Cell-Specific Homeostatic Plasticity through Chronic Sparse Optogenetic Stimulation

Abstract

Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits, neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles in ex vivo cortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and nonstimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and nonstimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits and, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations.SIGNIFICANCE STATEMENT The connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used an ex vivo approach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms of their correlated activity and functional connectivity. The absence of feedback between different brain areas and full control of external input allowed for a tractable system to study the underlying mechanisms and development of a computational model. Results show that differential stimulation of subpopulations of neurons significantly reshapes cortical circuits and forms subnetworks referred to as neuronal ensembles.

Keywords: Up-states; computational model; homeostatic plasticity; neural dynamics; neuronal ensembles.

Figure 5.

Empirically observed changes in intrinsic excitability is sufficient to account for cluster-specific changes in firing rate. A, The neural network model was composed of 2000 adaptive integrate-and-fire units (1600 Ex, 400 Inh). Traces represent two example Up-states in two sample Ex units in the baseline model. Average firing rate of all excitatory and inhibitory neurons during an Up-state is shown below. B, In the experimental network, there were two populations of Ex units (Ex⁺ and Ex^–) with different intrinsic excitability. Spike threshold, leak conductance, and membrane capacitance parameters differed between the Ex^– and Ex⁺ units. Traces represent the response to 250 ms square waves of injected current. C, F-I curves comparing the spiking output of the Ex⁺, Ex^–, and Inh units. The difference in spike threshold and slope for the Ex^– and Ex⁺ units qualitatively match empirical findings. D, Sample Up-states in the experimental neural network. E, Average firing rates of Ex^– and Ex⁺ units during Up-states were significantly different. F, The mean pairwise correlations between the Ex^–/Ex^–, Ex^–/Ex⁺, and Ex⁺/Ex⁺ pairs were not significantly different (data from 10 simulations).

Figure 6.

Empirically observed changes in probability of connection are sufficient to account for cluster-specific differences in firing rates and correlations. A, Schematic of the changes made to the weight matrix compared with the baseline network in Figure 5A. B, Sample Up-states following manipulation of the synaptic coupling between Ex⁺ and Ex^– populations. C, Average firing rates of Ex^– and Ex⁺ units during Up-states were significantly different. D, There was a significant decrease in the mean pairwise Ex^–/Ex⁺ correlations compared with the Ex^–/Ex^– and Ex⁺/Ex⁺ correlations, as well as a weaker correlation in the Ex⁺/Ex⁺ compared with Ex^–/Ex^– pairs. Because the correlations are bounded between −1 and 1, we are using nonparametric sign-rank statistics; thus, all p values can be the same despite the differences in the group values.

Figure 1.

Spontaneous Up-state frequency is reduced in densely transduced cortical slices following 24 or 48 h of stimulation. A, Schematic of densely transduced cortical circuits in organotypic slice cultures (top) and image from auditory cortex densely transduced with AAV9-CaMKIIα-hChR2(H134R)-mCherry and chronic optogenetic stimulation paradigm (bottom). B, Example traces of spontaneous Up-states in Pyramidal neurons from unstimulated (black), 24 h stimulated (green), and 48 h stimulated (blue) slices. Up-states were rarely observed in the 24 and 48 h stimulated slices. Orange annotations represent the three quantitative measures of spontaneous activity shown in C–E. C, The SD of membrane voltage was significantly decreased by both 24 and 48 h of stimulation. STD_Vm was calculated over a 5 min period of spontaneous activity in Pyr neurons. D, Spontaneous Up-state frequency was significantly decreased by both 24 and 48 h of stimulation. E, Although Up-state frequency was decreased by stimulation, when Up-states occurred, on average, they were of the same duration.

Figure 2.

Pairwise differences in Up-state amplitude, firing rate, and voltage correlation between stimulated and nonstimulated pyramidal neurons in sparsely transduced slices. A, Example of cortical pyramidal neurons sparsely transduced with AAV9-CaMKIIα-Cre and EF1a-DIO-hChR2(H134R)-mCherry (left), and sample paired recordings of ChR⁺ and ChR^– neurons (right). B, Spontaneous Up-state amplitude was significantly reduced in ChR⁺ compared with ChR^– pyramidal neurons. Up-state amplitude was not significantly different between simultaneously recorded ChR⁺ pyramidal neurons grouped according to their resting membrane potential (ChR^– pyramidal neurons with the lower resting membrane potential of the pair was plotted on the left). C, Spontaneous Up-state firing rate was significantly reduced in ChR⁺ versus ChR^– pyramidal neurons. Up-state firing rate was not significantly different between simultaneously recorded ChR^– pyramidal neurons grouped according to their resting membrane potential. D, The correlation between the Up-state voltage dynamics of ChR⁺ and ChR^– neurons was significantly less than ChR^– and ChR^– pairs, indicating a decorrelation between the shared inputs to the ChR⁺ and ChR^– subpopulations.

Figure 3.

Cell-autonomous decreases in the intrinsic input–output function of ChR⁺ compared with ChR^– pyramidal neurons in sparsely transduced slices. A, Sample intrinsic excitability traces simultaneously recorded from ChR^– and ChR⁺ pyramidal neurons from sparsely transduced cortical circuits stimulated for 48 h (250 ms current steps ranged from −0.10 to 0.3 nA). B, F-I curves of the average input–output functions from ChR^– and ChR⁺ pyramidal neurons from sparsely transduced cortical slices stimulated for 48 h. C, Threshold-linear fits of the F-I curves of the ChR^– and ChR⁺ populations. Light gray lines indicate the fits of the F-I curves of individual neurons. Solid cyan or blue lines indicate the mean threshold-linear fit. The threshold (θ = 0.10 nA) of the ChR^– Pyr neurons was significantly lower than the ChR⁺ Pyr neurons (θ = 0.13 nA).

Figure 4.

Synaptic decoupling between ChR⁺ and ChR^– pyramidal neurons. A, Example traces of a paired recording between reciprocally connected ChR^– and ChR^– pyramidal neurons (<50 μm apart). B, EPSP amplitude and slope were significantly reduced between connected pairs of ChR⁺ and ChR^– pyramidal neurons compared with connected pairs of ChR^– pyramidal neurons following 48 h of stimulation. C, Connectivity ratio was higher between pairs of ChR^– pyramidal neurons compared with pairs of ChR⁺ and ChR^– pyramidal neurons.