Search for: All records

Analyzing the functional connectivity of the brain is an enormous challenge, as deciphering functional connectivity requires knowledge of functional responses and connections. One promising strategy is analyzing the spatial pattern of activity correlations across cell populations. In the primary auditory cortex (A1), cells respond to different sound features. On the large scale, there exists a tonotopic map, which is fractured at the small scale, raising the question of whether functional connections are spatially ordered or disordered. To test whether functional connectivity on a local and a global scale is also disordered, we first designed a robust statistical model to estimate parameters and test for the significance of the estimated correlation maps. We developed an inference method that allows efficient model fitting and statistical testing to project the correlation maps to 2D space. We then performed in vivo two-photon calcium imaging in layer 2/3 of A1 with pure tones (PT) or a combination of two tones (TT; harmonically related or not). We found that the spatial patterns of signal correlations (SCs) depend on the type of sound stimuli that were presented. The functional 2D maps of PT-driven SCs are more restricted to local neurons than TT signal correlations which showed more global textures. 2D SC patterns for harmonic stimuli showed spatially distinct relationships. TT SCs revealed spatially precise functional connectivity between harmonically related neurons. Thus, even though the frequency preference of neighboring neurons in A1 is functionally diverse, the functional connection pattern of these neurons is functionally precise and harmonically related. 

Optogenetic stimulation has opened up a new avenue to probe neuronal circuitry at high spatiotemporal resolutions. A key challenge in optogenetic stimulation is deciding which subset out of thousands of neurons should be stimulated to elicit a desired network activation or affect behavior. In this work, we introduce a reinforcement learning approach to adaptively narrow down the multitude of stimulation possibilities and robustly identify Granger causal networks that underlie neuronal activity. We use realistic simulations with different underlying circuitry to show the effectiveness of reinforcement learning in identifying an optimal policy for selecting stimulation targets. 

Abstract Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity—that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations. 

Neuronal activity correlations are key to understanding how populations of neurons collectively encode information. While two-photon calcium imaging has created a unique opportunity to record the activity of large populations of neurons, existing methods for inferring correlations from these data face several challenges. First, the observations of spiking activity produced by two-photon imaging are temporally blurred and noisy. Secondly, even if the spiking data were perfectly recovered via deconvolution, inferring network-level features from binary spiking data is a challenging task due to the non-linear relation of neuronal spiking to endogenous and exogenous inputs. In this work, we propose a methodology to explicitly model and directly estimate signal and noise correlations from two-photon fluorescence observations, without requiring intermediate spike deconvolution. We provide theoretical guarantees on the performance of the proposed estimator and demonstrate its utility through applications to simulated and experimentally recorded data from the mouse auditory cortex.