Electrical & Computer Engineering
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Item Code and Data for "Sparse high-dimensional decomposition of non-primary auditory cortical receptive fields"(2024) Mukherjee, Shoutik; Babadi, Behtash; Shamma, Shihab A.Characterizing neuronal responses to natural stimuli remains a central goal in sensory neuroscience. In auditory cortical neurons, the stimulus selectivity of elicited spiking activity is summarized by a spectrotemporal receptive field (STRF) that relates neuronal responses to the stimulus spectrogram. Though effective in characterizing primary auditory cortical responses, STRFs of non-primary auditory neurons can be quite intricate, reflecting their mixed selectivity. The complexity of non-primary STRFs hence impedes understanding how acoustic stimulus representations are transformed along the auditory pathway. Here, we focus on the relationship between ferret primary auditory cortex (A1) and a secondary region, dorsal posterior ectosylvian gyrus (PEG). We propose estimating receptive fields in PEG with respect to a well-established high-dimensional computational model of primary-cortical stimulus representations. These ``cortical receptive fields'' (CortRF) are estimated greedily to identify the salient primary-cortical features modulating spiking responses and in turn related to corresponding spectrotemporal features. Hence, they provide biologically plausible hierarchical decompositions of STRFs in PEG. Such CortRF analysis was applied to PEG neuronal responses to speech and temporally orthogonal ripple combination (TORC) stimuli and, for comparison, to A1 neuronal responses. CortRFs of PEG neurons captured their selectivity to more complex spectrotemporal features than A1 neurons; moreover, CortRF models were more predictive of PEG (but not A1) responses to speech. Our results thus suggest that secondary-cortical stimulus representations can be computed as sparse combinations of primary-cortical features that facilitate encoding natural stimuli. Thus, by adding the primary-cortical representation, we can account for PEG single-unit responses to natural sounds better than bypassing it and considering as input the auditory spectrogram. These results confirm with explicit details the presumed hierarchical organization of the auditory cortex.Item Statistical Models of Neural Computations and Network Interactions in High-Dimensional Neural Data(2023) Mukherjee, Shoutik; Babadi, Behtash; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent advances in neural recording technologies, like high-density electrodes and two-photon calcium imaging, now enable the simultaneous acquisition of several hundred neurons over large patches of cortex. The availability of high volumes of simultaneously acquired neural activity presents exciting opportunities to study the network-level properties that support the neural code. This dissertation consists of two themes in analyzing network-level neural coding in large populations, particularly in the context of audition. Namely, we address modeling the instantaneous and directed interactions in large neuronal assemblies; and modeling neural computations in the mammalian auditory system.In the first part of this dissertation, an algorithm for adaptively modeling higher-order coordinated spiking as a discretized mark point process is proposed. Analyzing coordinated spiking involves a large number of possible simultaneous spiking events and covariates. We propose the adaptive Orthogonal Matching Pursuit (AdOMP) to tractably model dynamic higher-order coordination of ensemble spiking. Moreover, we generalize an elegant procedure for constructing confidence intervals for sparsity-regularized estimates to greedy algorithms and subsequently derive an inference framework for detecting facilitation or suppression of coordinated spiking. Application to simulated and experimentally recorded multi-electrode data recordings reveals significant gains over several existing benchmarks. The second part pertains to functional network analysis of large neuronal ensembles using OMP to impose sparsity constraints on models of neuronal responses. The efficacy of functional network analysis based on greedy model estimation is first demonstrated in two sets of two-photon calcium imaging data of mouse primary auditory cortex. The first dataset was collected during a tone discrimination task, where we additionally show that properties of the functional network structure encode information relevant to the animal’s task performance. The second dataset was collected from a cohort of young and aging mice during passive presentations of pure-tones in noise to study aging-related network changes in A1. The constituency of neurons engaged in functional networks changed by age; we characterized these changes and their correspondence to differences in functional network structure. We next demonstrated the efficacy of greedy estimation in functional network analysis in application to electrophysiological spiking recordings across multiple areas of songbird auditory cortex, and present initial findings on interareal network structure differences between responses to tutor songs and non-tutor songs that suggest the learning-related effects on functional networks. The third part of this dissertation concerns neural system identification. Neu- rons in ferret primary auditory cortex are known to exhibit stereotypical spectrotem- poral specificity in their responses. However, spectrotemporal receptive fields (STRF) measured in non-primary areas can be intricate, reflecting mixed spectrotemporal selectivity, and hence be challenging to interpret. We propose a point process model of spiking responses of neurons in PEG, a secondary auditory area, where neurons’ spiking rates are modulated by a high-dimensional biologically inspired stimulus rep- resentation. The proposed method is shown to accurately model a neuron’s response to speech and artificial stimuli, and offers the interpretation of complex STRFs as the sparse combination of higher-dimensional features. Moreover, comparative analyses between PEG and A1 neurons suggest the role of such an hierarchical model is to facilitate encoding natural stimuli.The fourth part of this dissertation is a study in computational auditory scene analysis that seeks to model the role of selective attention in binaural segregation within the framework of a temporal coherence model of auditory streaming. Masks can be obtained by clustering cortical features according to their instantaneous coincidences with pitch and interaural cues. We model selective attention by restrict- ing the ranges of pitch or interaural timing differences used to obtain masks, and evaluate the robustness of the selective attention model in comparison to the baseline model that uses all perceptual cues. Selective attention was as robust to noise and reverberation as the baseline, suggesting the proposed attentive temporal coherence model, in the context of prior experimental findings, may describe the computations by which downstream unattended-speaker representations are suppressed in scene analysis. Finally, the fifth part of this dissertation discusses future directions in studying network interactions in large neural datasets, especially in consideration of current trends towards the adoption of optogenetic stimulation to study neural coding. As a first step in these new directions, a simulation study introducing a reinforcement learning-guided approach to optogenetic stimulation target selection is presented.Item Granger Causality analysis codes for "Sequential Transmission of Task-Relevant Information in Cortical Neuronal Networks"(2022) Mukherjee, Shoutik; Babadi, BehtashDuring auditory task performance, cortical processing of task-relevant information enables mammals to recognize sensory input and flexibly select behavioral responses. In mouse auditory cortex, small neuronal networks encode behavioral choice during a pure-tone detection task, but it is poorly understood how neuronal networks encode behavioral choice during a pure-tone discrimination task where tones have to be categorized into targets and non-targets. While the interactions between networked neurons are thought to encode behavioral choice, it remains unclear how patterns of neuronal network activity indicate the transmission of task-relevant information within the network. To this end, we trained mice to behaviorally discriminate target vs. non-target pure-tones while we used in vivo 2-photon imaging to record neuronal population activity in primary auditory cortex layer 2/3. We found that during task performance, a specialized subset of neurons transiently encoded intersection information, i.e., sensory information that was used to inform behavioral choice. Granger causality analysis showed that these neurons formed functional networks in which task-relevant information was transmitted sequentially between neurons. Differences in network structure between target and non-target sounds encoded behavioral choice. Correct behavioral choices were associated with shorter timescale communication between neurons. In summary, we find that specialized neuronal populations in auditory cortex form functional networks during auditory task performance whose structures depend on both sensory input and behavioral choice.Item Experimental data from "Sequential Transmission of Task-Relevant Information in Cortical Neuronal Networks"(2022) Francis, Nikolas; Mukherjee, Shoutik; Koçillari, Loren; Panzeri, Stefano; Babadi, Behtash; Kanold, PatrickDuring auditory task performance, cortical processing of task-relevant information enables mammals to recognize sensory input and flexibly select behavioral responses. In mouse auditory cortex, small neuronal networks encode behavioral choice during a pure-tone detection task, but it is poorly understood how neuronal networks encode behavioral choice during a pure-tone discrimination task where tones have to be categorized into targets and non-targets. While the interactions between networked neurons are thought to encode behavioral choice, it remains unclear how patterns of neuronal network activity indicate the transmission of task-relevant information within the network. To this end, we trained mice to behaviorally discriminate target vs. non-target pure-tones while we used in vivo 2-photon imaging to record neuronal population activity in primary auditory cortex layer 2/3. We found that during task performance, a specialized subset of neurons transiently encoded intersection information, i.e., sensory information that was used to inform behavioral choice. Granger causality analysis showed that these neurons formed functional networks in which task-relevant information was transmitted sequentially between neurons. Differences in network structure between target and non-target sounds encoded behavioral choice. Correct behavioral choices were associated with shorter timescale communication between neurons. In summary, we find that specialized neuronal populations in auditory cortex form functional networks during auditory task performance whose structures depend on both sensory input and behavioral choice.