scholarly journals Cell type specific information transfer for sparse coding

2020 ◽  
Author(s):  
Fleur Zeldenrust ◽  
Niccolò Calcini ◽  
Xuan Yan ◽  
Ate Bijlsma ◽  
Tansu Celikel
Keyword(s):  
Sparse Coding ◽  
Information Transfer ◽  
Spike Trains ◽  
Inhibitory Neurons ◽  
Specific Information ◽  
Cell Type ◽  
Sensory Stimuli ◽  
Firing Rates ◽  
Excitatory Neurons ◽  
Cell Type Specific

AbstractSensory neurons reconstruct the world from action potentials (spikes) impinging on them. Recent work argues that the formation of sensory representations are cell-type specific, as excitatory and inhibitory neurons use complementary information available in spike trains to represent sensory stimuli. Here, by measuring the mutual information between synaptic input and spike trains, we show that inhibitory and excitatory neurons in the barrel cortex transfer information differently: excitatory neurons show strong threshold adaptation and a reduction of intracellular information transfer with increasing firing rates. Inhibitory neurons, on the other hand, show threshold behaviour that facilitates broadband information transfer. We propose that cell-type specific intracellular information transfer is the rate-limiting step for neuronal communication across synaptically coupled networks. Ultimately, at high firing rates, the reduction of information transfer by excitatory neurons and its facilitation by inhibitory neurons together provides a mechanism for sparse coding and information compression in cortical networks.

Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding

2021 ◽  
pp. 1-34
Author(s):  
Xiaolin Hu ◽  
Zhigang Zeng
Keyword(s):  
Sparse Coding ◽  
Hebbian Learning ◽  
Inhibitory Neuron ◽  
Learning Rule ◽  
Orientation Selectivity ◽  
Inhibitory Neurons ◽  
Natural Scenes ◽  
Firing Rates ◽  
V1 Neurons ◽  
Excitatory Neurons

Abstract The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.

scholarly journals Mechanisms underlying contrast-dependent orientation selectivity in mouse V1

2018 ◽  
Vol 115 (45) ◽  
pp. 11619-11624 ◽  
Author(s):  
Wei P. Dai ◽  
Douglas Zhou ◽  
David W. McLaughlin ◽  
David Cai
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Orientation Selectivity ◽  
Inhibitory Neurons ◽  
Orientation Tuning ◽  
Response Properties ◽  
Firing Rates ◽  
Excitatory Neurons ◽  
Cortical Excitation ◽  
Contrast Invariance

Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties—one of which is that contrast invariance in the orientation selectivity (OS) of the neurons’ firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties—for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance—providing insights for future studies on contrast dependence (invariance).

scholarly journals Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex

2018 ◽  
Author(s):  
Petr Znamenskiy ◽  
Mean-Hwan Kim ◽  
Dylan R. Muir ◽  
Maria Florencia Iacaruso ◽  
Sonja B. Hofer ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Pyramidal Cells ◽  
Fine Tuning ◽  
Inhibitory Neurons ◽  
Local Network ◽  
Cortical Circuits ◽  
Sensory Stimuli ◽  
Excitatory Neurons ◽  
Strong Excitation

In the cerebral cortex, the interaction of excitatory and inhibitory synaptic inputs shapes the responses of neurons to sensory stimuli, stabilizes network dynamics1 and improves the efficiency and robustness of the neural code2–4. Excitatory neurons receive inhibitory inputs that track excitation5–8. However, how this co-tuning of excitation and inhibition is achieved by cortical circuits is unclear, since inhibitory interneurons are thought to pool the inputs of nearby excitatory cells and provide them with non-specific inhibition proportional to the activity of the local network9–13. Here we show that although parvalbumin-expressing (PV) inhibitory cells in mouse primary visual cortex make connections with the majority of nearby pyramidal cells, the strength of their synaptic connections is structured according to the similarity of the cells’ responses. Individual PV cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This fine-tuning of synaptic weights supports co-tuning of inhibitory and excitatory inputs onto individual pyramidal cells despite dense connectivity between inhibitory and excitatory neurons. Our results indicate that individual PV cells are preferentially integrated into subnetworks of inter-connected, co-tuned pyramidal cells, stabilising their recurrent dynamics. Conversely, weak but dense inhibitory connectivity between subnetworks is sufficient to support competition between them, de-correlating their output. We suggest that the history and structure of correlated firing adjusts the weights of both inhibitory and excitatory connections, supporting stable amplification and selective recruitment of cortical subnetworks.

Neuronal Circuits in Barrel Cortex for Whisker Sensory Perception

2021 ◽  
Vol 101 (1) ◽  
pp. 353-415
Author(s):  
Jochen F. Staiger ◽  
Carl C. H. Petersen
Keyword(s):  
Barrel Cortex ◽  
Specific Activity ◽  
Sensory Information ◽  
Inhibitory Neurons ◽  
Future Research ◽  
Cell Type ◽  
Molecular Features ◽  
Somatotopic Map ◽  
Cell Type Specific ◽  
The Impact

The array of whiskers on the snout provides rodents with tactile sensory information relating to the size, shape and texture of objects in their immediate environment. Rodents can use their whiskers to detect stimuli, distinguish textures, locate objects and navigate. Important aspects of whisker sensation are thought to result from neuronal computations in the whisker somatosensory cortex (wS1). Each whisker is individually represented in the somatotopic map of wS1 by an anatomical unit named a ‘barrel’ (hence also called barrel cortex). This allows precise investigation of sensory processing in the context of a well-defined map. Here, we first review the signaling pathways from the whiskers to wS1, and then discuss current understanding of the various types of excitatory and inhibitory neurons present within wS1. Different classes of cells can be defined according to anatomical, electrophysiological and molecular features. The synaptic connectivity of neurons within local wS1 microcircuits, as well as their long-range interactions and the impact of neuromodulators, are beginning to be understood. Recent technological progress has allowed cell-type-specific connectivity to be related to cell-type-specific activity during whisker-related behaviors. An important goal for future research is to obtain a causal and mechanistic understanding of how selected aspects of tactile sensory information are processed by specific types of neurons in the synaptically connected neuronal networks of wS1 and signaled to downstream brain areas, thus contributing to sensory-guided decision-making.

scholarly journals Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Janelle MP Pakan ◽  
Scott C Lowe ◽  
Evelyn Dylda ◽  
Sander W Keemink ◽  
Stephen P Currie ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Pyramidal Neurons ◽  
Visual Stimulation ◽  
Inhibitory Neurons ◽  
Behavioral State ◽  
Visual Responses ◽  
Cell Type ◽  
Sensory Stimuli ◽  
Context Dependent

Cortical responses to sensory stimuli are modulated by behavioral state. In the primary visual cortex (V1), visual responses of pyramidal neurons increase during locomotion. This response gain was suggested to be mediated through inhibitory neurons, resulting in the disinhibition of pyramidal neurons. Using in vivo two-photon calcium imaging in layers 2/3 and 4 in mouse V1, we reveal that locomotion increases the activity of vasoactive intestinal peptide (VIP), somatostatin (SST) and parvalbumin (PV)-positive interneurons during visual stimulation, challenging the disinhibition model. In darkness, while most VIP and PV neurons remained locomotion responsive, SST and excitatory neurons were largely non-responsive. Context-dependent locomotion responses were found in each cell type, with the highest proportion among SST neurons. These findings establish that modulation of neuronal activity by locomotion is context-dependent and contest the generality of a disinhibitory circuit for gain control of sensory responses by behavioral state.

scholarly journals A Disinhibitory Circuit for Contextual Modulation in Primary Visual Cortex

2020 ◽  
Author(s):  
Andreas J. Keller ◽  
Mario Dipoppa ◽  
Morgane M. Roth ◽  
Matthew S. Caudill ◽  
Alessandro Ingrosso ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Vasoactive Intestinal Peptide ◽  
Primary Visual Cortex ◽  
Computational Modelling ◽  
Visual Scene ◽  
Inhibitory Neurons ◽  
Contextual Modulation ◽  
Sensory Stimuli ◽  
Excitatory Neurons ◽  
Mouse Visual Cortex

Context guides perception by influencing the saliency of sensory stimuli. Accordingly, in visual cortex, responses to a stimulus are modulated by context, the visual scene surrounding the stimulus. Responses are suppressed when stimulus and surround are similar but not when they differ. The mechanisms that remove suppression when stimulus and surround differ remain unclear. Here we use optical recordings, manipulations, and computational modelling to show that a disinhibitory circuit consisting of vasoactive-intestinal-peptide-expressing (VIP) and somatostatin-expressing (SOM) inhibitory neurons modulates responses in mouse visual cortex depending on the similarity between stimulus and surround. When the stimulus and the surround are similar, VIP neurons are inactive and SOM neurons suppress excitatory neurons. However, when the stimulus and the surround differ, VIP neurons are active, thereby inhibiting SOM neurons and relieving excitatory neurons from suppression. We have identified a canonical cortical disinhibitory circuit which contributes to contextual modulation and may regulate perceptual saliency.

scholarly journals Distinct Spiking Patterns of Excitatory and Inhibitory Neurons and LFP Oscillations in Prefrontal Cortex During Sensory Discrimination

2021 ◽  
Vol 12 ◽  
Author(s):  
Hua-an Tseng ◽  
Xue Han
Keyword(s):  
Prefrontal Cortex ◽  
Discrimination Task ◽  
Field Potential ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Sensory Discrimination ◽  
Related Information ◽  
Entire Duration ◽  
Excitatory Neurons ◽  
Spike Waveforms

Prefrontal cortex (PFC) are broadly linked to various aspects of behavior. During sensory discrimination, PFC neurons can encode a range of task related information, including the identity of sensory stimuli and related behavioral outcome. However, it remains largely unclear how different neuron subtypes and local field potential (LFP) oscillation features in the mouse PFC are modulated during sensory discrimination. To understand how excitatory and inhibitory PFC neurons are selectively engaged during sensory discrimination and how their activity relates to LFP oscillations, we used tetrode recordings to probe well-isolated individual neurons, and LFP oscillations, in mice performing a three-choice auditory discrimination task. We found that a majority of PFC neurons, 78% of the 711 recorded individual neurons, exhibited sensory discrimination related responses that are context and task dependent. Using spike waveforms, we classified these responsive neurons into putative excitatory neurons with broad waveforms or putative inhibitory neurons with narrow waveforms, and found that both neuron subtypes were transiently modulated, with individual neurons’ responses peaking throughout the entire duration of the trial. While the number of responsive excitatory neurons remain largely constant throughout the trial, an increasing fraction of inhibitory neurons were gradually recruited as the trial progressed. Further examination of the coherence between individual neurons and LFPs revealed that inhibitory neurons exhibit higher spike-field coherence with LFP oscillations than excitatory neurons during all aspects of the trial and across multiple frequency bands. Together, our results demonstrate that PFC excitatory neurons are continuously engaged during sensory discrimination, whereas PFC inhibitory neurons are increasingly recruited as the trial progresses and preferentially coordinated with LFP oscillations. These results demonstrate increasing involvement of inhibitory neurons in shaping the overall PFC dynamics toward the completion of the sensory discrimination task.

scholarly journals Bayesian Inference for Single-cell Clustering and Imputing

2017 ◽  
Vol 3 (1) ◽  
pp. 46 ◽  
Author(s):  
Elham Azizi ◽  
Sandhya Prabhakaran ◽  
Ambrose Carr ◽  
Dana Pe'er
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Superior Performance ◽  
Underlying Structure ◽  
Specific Information ◽  
Cell Type ◽  
Cell Clustering ◽  
Bayesian Probabilistic Model ◽  
Cell Type Specific ◽  
Cell Data

Single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is noise-prone due to experimental errors and cell type-specific biases. Current computational approaches for analyzing single-cell data involve a global normalization step which introduces incorrect biases and spurious noise and does not resolve missing data (dropouts). This can lead to misleading conclusions in downstream analyses. Moreover, a single normalization removes important cell type-specific information. We propose a data-driven model, BISCUIT, that iteratively normalizes and clusters cells, thereby separating noise from interesting biological signals. BISCUIT is a Bayesian probabilistic model that learns cell-specific parameters to intelligently drive normalization. This approach displays superior performance to global normalization followed by clustering in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

scholarly journals A unique role for DNA (hydroxy)methylation in epigenetic regulation of human inhibitory neurons

2018 ◽  
Vol 4 (9) ◽  
pp. eaau6190 ◽  
Author(s):  
Alexey Kozlenkov ◽  
Junhao Li ◽  
Pasha Apontes ◽  
Yasmin L. Hurd ◽  
William M. Byne ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Brain Cell ◽  
Inhibitory Neurons ◽  
Brain Diseases ◽  
Unique Role ◽  
Neuropsychiatric Diseases ◽  
Excitatory Neurons ◽  
Cell Type Specific

Brain function depends on interaction of diverse cell types whose gene expression and identity are defined, in part, by epigenetic mechanisms. Neuronal DNA contains two major epigenetic modifications, methylcytosine (mC) and hydroxymethylcytosine (hmC), yet their cell type–specific landscapes and relationship with gene expression are poorly understood. We report high-resolution (h)mC analyses, together with transcriptome and histone modification profiling, in three major cell types in human prefrontal cortex: glutamatergic excitatory neurons, medial ganglionic eminence–derived γ-aminobutyric acid (GABA)ergic inhibitory neurons, and oligodendrocytes. We detected a unique association between hmC and gene expression in inhibitory neurons that differed significantly from the pattern in excitatory neurons and oligodendrocytes. We also found that risk loci associated with neuropsychiatric diseases were enriched near regions of reduced hmC in excitatory neurons and reduced mC in inhibitory neurons. Our findings indicate differential roles for mC and hmC in regulation of gene expression in different brain cell types, with implications for the etiology of human brain diseases.

scholarly journals Bioinformatics Approaches to Profile the Tumor Microenvironment for Immunotherapeutic Discovery

2017 ◽  
Vol 23 (32) ◽  
pp. 4716-4725 ◽  
Author(s):  
Trevor Clancy ◽  
Ruth Dannenfelser ◽  
Olga Troyanskaya ◽  
Karl Johan Malmberg ◽  
Eivind Hovig ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Immune Cell ◽  
Cell Types ◽  
Specific Information ◽  
Cell Type ◽  
Signaling Systems ◽  
Important Challenge ◽  
Cell Type Specific ◽  
Tissue Systems ◽  
Heterogeneous Tumor

In the microenvironment of a malignancy, tumor cells do not exist in isolation, but rather in a diverse ecosystem consisting not only of heterogeneous tumor-cell clones, but also normal cell types such as fibroblasts, vasculature, and an extensive pool of immune cells at numerous possible stages of activation and differentiation. This results in a complex interplay of diverse cellular signaling systems, where the immune cell component is now established to influence cancer progression and therapeutic response. It is experimentally difficult and laborious to comprehensively and systematically profile these distinct cell types from heterogeneous tumor samples in order to capitalize on potential therapeutic and biomarker discoveries. One emerging solution to address this challenge is to computationally extract cell-type specific information directly from bulk tumors. Such in silico approaches are advantageous because they can capture both the cell-type specific profiles and the tissue systems level of cell-cell interactions. Accurately and comprehensively predicting these patterns in tumors is an important challenge to overcome, not least given the success of immunotherapeutic drug treatment of several human cancers. This is especially challenging for subsets of closely related immune cell phenotypes with relatively small gene expression differences, which have critical functional distinctions. Here, we outline the existing and emerging novel bioinformatics strategies that can be used to profile the tumor immune landscape.

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