Coordinated changes in a cortical circuit sculpt effects of novelty on neural dynamics

Abstract

Recent studies have found dramatic cell-type-specific responses to stimulus novelty, highlighting the importance of analyzing the cortical circuitry at this granularity to understand brain function. Although initial work characterized activity by cell type, the alterations in cortical circuitry due to interacting novelty effects remain unclear. We investigated circuit mechanisms underlying the observed neural dynamics in response to novel stimuli using a large-scale public dataset of electrophysiological recordings in behaving mice and a population network model. The model was constrained by multi-patch synaptic physiology and electron microscopy data. We found generally weaker connections under novel stimuli, with shifts in the balance between somatostatin (SST) and vasoactive intestinal polypeptide (VIP) populations and increased excitatory influences on parvalbumin (PV) and SST populations. These findings systematically characterize how cortical circuits adapt to stimulus novelty.

Keywords: CP: Neuroscience; cell types; computational neuroscience; mouse; network modeling; novelty; primary visual cortex; stabilized supralinear network; visual behavior.

Figure 1.. Illustration of the experimental setup and the phenomenon under investigation

(A) Schematics of the questions addressed in this study. The image set changes between the familiar and novel sessions to introduce stimulus novelty. We hypothesized that specific changes in V1 circuitry produce the changes in observed activity induced by stimulus novelty and investigated it using a population model. (B) Essential structure of the task relevant to this study. Stimuli are repeated at a regular cadence (250 ms on, 500 ms off). The stimulus is omitted 5% of the time, but the temporal cadence is preserved. (C) Average neural population activity in L2/3 under familiar and novel stimuli. We selected segments around the stimulus omission, where different types of novelty interact. The shaded area represents the SEM, with the denominator being the number of specimens. See STAR Methods for details.

Figure 2.. Schematic representation of the modeling workflow

The four-population stabilized supralinear network (SSN) is used to examine the effect of novelty on cortical dynamics. The multi-patch synaptic physiology dataset^, (https://portal.brain-map.org/explore/connectivity/synaptic-physiology) and the MICrONS electron microscopy dataset (https://www.microns-explorer.org/cortical-mm3) are used to construct a parameter cost that constrains the model connectivity parameters. The activity of the L4 Exc population from the Neuropixels data serves as the input drive (together with the baseline constant input), and the model output is compared with L2/3 activity from the Neuropixels to construct the fit cost. The optimization is repeated 100,000 times with different random initial parameters for each session to gather statistics on the solutions.

Figure 3.. Statistics of the solutions and acceptance criteria

(A) Histograms of the fitting cost values for all solutions passing the parameter cutoff criteria for the familiar condition. The cost function values for the best solution and the solution near the acceptance threshold are indicated by green and orange dashed lines, respectively. The shaded area represents the number of solutions with no visual response (see STAR Methods for details). (B) Firing rate traces of the target data, the best solution, and a solution near the acceptance threshold for each population. (C and D) Same as (A) and (B) but for the novel condition. (E and F) Number of accepted solutions as a function of varying acceptance thresholds for the familiar and novel conditions, respectively. The black line indicates the number of accepted solutions wherein all populations passed the acceptance threshold. (G–I) PCA projection of the parameters. Each image highlights different categories of solutions with color: those with poor fit (G), those with poor parameter cost (H), and those that are accepted (I). Solutions with a fit cost greater than 0.3 for any population are excluded from the PCA to enhance the visibility of the structure of the accepted solution. The accepted familiar and novel solutions form tight clusters in the PCA space, providing a basis for subsequent LDA.

Figure 4.. Summary of the parameter changes between the familiar and novel conditions

(A) Histograms of model parameters for each condition. Although some parameter distributions show significant shifts in their average values, most distributions overlap between the two conditions. (B) Heatmap illustrating the average parameter shifts from familiar to novel conditions. The confidence interval was calculated from the shifts in 10⁶ randomly sampled solution pairs. (C) Summary schematic of the transition from the familiar to the novel network. This figure illustrates key changes in the connections in our model networks. Connections exhibiting changes with wholly positive confidence intervals are labeled as “strengthened,” while those with wholly negative confidence intervals are labeled as “weakened” and further emphasized by colored arrows. The transition from the familiar to the novel condition is characterized by a general weakening of the connections, with increased control of the SST population by the VIP population.

Figure 5.. Linear response analysis of the resting-state networks in the familiar and novel conditions

(A) Schematic of the procedure. We injected small currents into one population at a time and observed the resulting changes in firing rates in every population. (B and C) Resting-state firing rates of each population. The median of all accepted solutions was used. The error bars represent the 5th and 95th percentiles. (D and E) Linear responses of the populations in the familiar and novel networks to small current injection. The SST and VIP populations are more susceptible than the Exc and PV populations. The PV population exhibits the “paradoxical effect” in the familiar condition, which is characterized by the negative firing rate change in response to a depolarizing current injection (red square in D), which is absent in the novel condition.

Figure 6.. Target data manipulation to understand how different parts of the data affect the parameters

(A and B) Firing rate traces of the familiar and novel datasets. (C and D) Modified versions of the data wherein the image response and inter-stimulus segments are swapped between sessions. (E) Projection of the parameters of accepted solutions in the 2D LDA space, along with a biplot of the eight most influential parameters. (F) Projection of each parameter into the 2D LDA space. The arrow denotes the direction in the 2D LDA space defined in (C), and the color indicates the weights of each element. (G) Summary schematics of the target manipulation study. Each swapped session can be viewed as a change in certain segments from the original familiar session. When different segments are swapped, they form orthogonal vectors in the 2D LDA space (left). The summation of these changes corresponds to the total transformation of the firing rate traces. Notably, alterations to inputs into the VIP population uniquely result in horizontal movement in the LDA space, corresponding to the swapping of different segments (right).

Figure 7.. The networks in the novel condition have higher gain and earlier saturation compared to those in the familiar condition

(A) Illustration of the stimuli applied with the baseline firing rate fixed at 2.7 Hz for both sessions and the stimulus amplitude indicating deviation from 2.7 Hz. (B and C) Population responses to the stimuli with different amplitudes for example solutions of the familiar and novel conditions, respectively. The colors of the traces correspond to those shown in (A). (D and E) Population responses as a function of the stimulus amplitude for solutions in the familiar and novel conditions, respectively. The shaded areas are between 5th and 95th percentiles. The dashed lines indicate the stimulus levels of the experimental familiar and novel sessions (activity of the L4 Exc population). (F) The derivative of the Exc population responses as a function of the stimulus amplitude (stimulus gain) for the familiar and novel solutions. (G and H) Firing rates of L2/3 populations evaluated as a function of L4 Exc population in the Neuropixels dataset. These curves were estimated from trial-by-trial fluctuations of the firing rates of simultaneously recorded neurons. The novel condition shows a higher response and a steeper slope. The PV population exhibits saturation of the L2/3 firing rate when the L4 Exc firing rate increases, and the timing of the saturation is earlier in the novel session. The shaded areas represent SEM. (I and J) The derivative of (G) and (H). The high gain of the Exc population in the novel session is more apparent. The saturation of the firing rates is also clearly seen as a decline in the slope in (J).