Synergistic population encoding and precise coordinated variability across interlaminar ensembles in the early visual system

Denman, Daniel J.; Reid, R. Clay

doi:10.1101/812859

Cited by 7 publications

(7 citation statements)

References 91 publications

(108 reference statements)

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“…Previous studies have shown that variability shared within a neuronal population can constrain information propagation between processing stages (Averbeck et al 2006; Denman and Reid 2019; Kohn et al 2016; Lin et al 2015). This is because shared variance within a population may not average out (Azeredo da Silveira and Rieke 2021; Moreno-Bote et al 2014), leading to a deterioration of the population’s coding capacity.…”

Section: Resultsmentioning

confidence: 99%

“…Given the hierarchical organization of the visual cortex (Siegle, Jia, et al, 2021,Harris et al 2019), the response variance of a sensory neuron can potentially limit the amount of stimulus information available to downstream circuits (Denman and Reid 2019, Figures 4 G , 7). While past studies have shown the effects of pair-wise correlations on information encoded by a neuronal population (Averbeck et al 2006; Kohn et al 2016; Moreno-Bote et al 2014), a more comprehensive population-level perspective is essential to understanding the brain’s correlational structure (Recanatesi et al 2022; Shea-Brown et al 2008; Trousdale et al 2012).…”

Section: Discussionmentioning

confidence: 99%

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Deciphering neuronal variability across states reveals dynamic sensory encoding

Akella,

Ledochowitsch,

Siegle

et al. 2024

Preprint

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Influenced by factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. Because these factors are non-stationary, they dynamically impact the fidelity of sensory processing. However, it remains unclear how their relative impact on neuronal variability evolves over time. To address this question, we designed an encoding model with latent states to partition visual cortical variability across three crucial categories of sources: internal brain dynamics, behavior, and external visual stimulus. Applying a hidden Markov model to the rhythmic patterns of cortical local field potentials, we consistently identified three distinct oscillation states. Each state revealed a unique variability profile and a consistent descending trend of stimulus modulation across the visual hierarchy. Regression models within each state revealed a dynamic composition of factors contributing to the observed spiking variability, with the primary influencing factor switching within seconds. In the state dominated by high-frequency oscillations, sensory inputs and behavior exerted the most influence on population dynamics. Conversely, internal brain activity explained most of the variance in the state dominated by low-frequency oscillations. This heterogeneity across states underscores the importance of partitioning variability over time, particularly when considering the dynamic influence of non-stationary factors on sensory processing.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deciphering neuronal variability across states reveals dynamic sensory encoding

Akella,

Ledochowitsch,

Siegle

et al. 2024

Preprint

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“…Neuropixels is a single shaft multi-channel neural probe consisting of 384 recording channels selectively measuring from 960 recording sites with a spacing between the recording sites of ∼20 μm. For the neural data used in this paper to test the GEMsort algorithm, neural data were recorded by Neuropixels Phase 3A prototype probes (Jun et al 2017) in awake mice, following procedures described in detail elsewhere (Denman and Reid 2019). All animal procedures were approved by the Allen Institute for Brain Science Institutional Animal Care and Use Committee (IACUC).…”

Section: Resultsmentioning

confidence: 99%

Multichannel neural spike sorting with spike reduction and positional feature

Mohammadi

Denman

Klug

et al. 2022

Preprint

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Sorting neural voltages measured from a multichannel neural probe to extract the single unit activities of neuronal firing, especially in real-time, remains a significant technical challenge, largely due to the large amount of acquired data and the technical difficulties involved in processing and classifying these neural spikes promptly. Most neural spike sorting algorithms focus on sorting neural spikes post hoc for high sorting accuracy, and reducing the processing time generally is not the chief concern. Here we report on two signal processing modifications to our previously developed single-channel real-time spike sorting (Enhanced Growing Neural Gas) algorithm, which is largely based on graph network. Duplicated neural spikes were eliminated and represented by the neural spike with the strongest signal profile, significantly reducing the amount of neural data to be processed. In addition, the channel from which the representing neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features. With these two modifications, the Graph nEtwork Multichannel (GEMsort) neural spike sorting algorithm can rapidly sort neural spikes without requiring significant computer processing power and system memory storage. The parallel processing architecture of GEMsort is particularly suitable for digital hardware implementation to improve processing speed and recording channel scalability. Multichannel synthetic neural spikes and actual neural recordings with Neuropixels probes were used to evaluate the sorting accuracies of the GEMsort algorithm.

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“…Previous attempts at measuring how information scales with population size have frequently found noise correlations to either be beneficial (Denman & Reid, 2019) or to not affect information scaling (Cotton et al, 2018;Mendels & Shamir, 2018). These studies focused on smaller populations (<200 neurons in Denman & Reid, 2019; <100 neurons in Mendels & Shamir, 2018) in which sublinear scaling might be hard to identify (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…These studies focused on smaller populations (<200 neurons in Denman & Reid, 2019; <100 neurons in Mendels & Shamir, 2018) in which sublinear scaling might be hard to identify (Fig. 1), and in part included spike timing information (Denman & Reid, 2019) in addition to the spike counts used here. Recent recordings from ~20,000 neurons in mouse V1 suggest information about visual stimuli does saturate (Stringer, Michaelos, & Pachitariu, 2019; Fig.…”

Section: Discussionmentioning

confidence: 99%

Scaling of information in large neural populations reveals signatures of information-limiting correlations

Kafashan

Jaffe

Chettih

et al. 2020

Preprint

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How is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into informationlimiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations. Figure 1. Information scaling in large neural populations, and the impact of noise correlations on information.a. The information that a population of neurons can encode about some stimulus value is always a non-decreasing function of the population size. Information might on average increase with every added neuron (unbounded scaling; red) if the information is evenly distributed across all neurons. In contrast, information can rapidly saturate if information is redundant, and thus it is not strictly limited by population size, but by other factors. In general, it has only been possible to record from a very small subset of neurons of a particular area (grey shaded), from which it is hard to tell the difference between the two scenarios if the sampled population size is too small. b. The encoded information is modulated by noise correlations. This is illustrated using two neurons with different tunings to the stimulus value (top). The amount of information to discriminate between two stimulus values ( " /red and # /blue) depends on the difference in mean population activity (crosses) between stimuli, and the noise correlations (shaded ellipsoids) for either stimulus (bottom, showing joint neural activity of both neurons). The information is largest when the noise is smallest in the direction of the mean population activity difference (black arrow), which leads to the largest separation across the optimal discrimination boundary (grey line). In this example, positive correlations boost information (middle), whereas negative correlations lower it (right), when compared to uncorrelated neurons (left). In general, the impact of noise correlations depends on how they interact with the population's tuning curves.

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Synergistic population encoding and precise coordinated variability across interlaminar ensembles in the early visual system

Cited by 7 publications

References 91 publications

Deciphering neuronal variability across states reveals dynamic sensory encoding

Deciphering neuronal variability across states reveals dynamic sensory encoding

Multichannel neural spike sorting with spike reduction and positional feature

Scaling of information in large neural populations reveals signatures of information-limiting correlations

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