Ensemble Of Channels Research Articles

High-fidelity encoding of mechanostimuli by tactile food-sensing neurons requires an ensemble of ion channels

The nematode C.elegans uses mechanosensitive neurons to detect bacteria, which are food for worms. These neurons release dopamine to suppress foraging and promote dwelling. Through a screen of genes highly expressed in dopaminergic food-sensing neurons, we identify a K2P-family potassium channel-TWK-2-that damps their activity. Strikingly, loss of TWK-2 restores mechanosensation to neurons lacking the NOMPC-like channel transient receptor potential 4 (TRP-4), which was thought to be the primary mechanoreceptor for tactile food sensing. The alternate mechanoreceptor mechanism uncovered by TWK-2 mutation requires three Deg/ENaC channel subunits: ASIC-1, DEL-3, and UNC-8. Analysis of cell-physiological responses to mechanostimuli indicates that TRP and Deg/ENaC channels work together to set the range of analog encoding of stimulus intensity and to improve signal-to-noise characteristics and temporal fidelity of food-sensing neurons. We conclude that a specialized mechanosensory modality-tactile food sensing-emerges from coordination of distinct force-sensing mechanisms housed in one type of sensory neuron.

Inflammation differentially controls transport of depolarizing Nav versus hyperpolarizing Kv channels to drive rat nociceptor activity

Inflammation causes pain by shifting the balance of ionic currents in nociceptors toward depolarization, leading to hyperexcitability. The ensemble of ion channels within the plasma membrane is regulated by processes including biogenesis, transport, and degradation. Thus, alterations in ion channel trafficking may influence excitability. Sodium channel NaV1.7 and potassium channel KV7.2 promote and oppose excitability in nociceptors, respectively. We used live-cell imaging to investigate mechanisms by which inflammatory mediators (IM) modulate the abundance of these channels at axonal surfaces through transcription, vesicular loading, axonal transport, exocytosis, and endocytosis. Inflammatory mediators induced a NaV1.7-dependent increase in activity in distal axons. Further, inflammation increased the abundance of NaV1.7, but not of KV7.2, at axonal surfaces by selectively increasing channel loading into anterograde transport vesicles and insertion at the membrane, without affecting retrograde transport. These results uncover a cell biological mechanism for inflammatory pain and suggest NaV1.7 trafficking as a potential therapeutic target.

Nav1.7 P610T mutation in two siblings with persistent ocular pain after corneal axon transection: impaired slow inactivation and hyperexcitable trigeminal neurons.

Despite extensive study, the mechanisms underlying pain after axonal injury remain incompletely understood. Pain after corneal refractive surgery provides a model, in humans, of the effect of injury to trigeminal afferent nerves. Axons of trigeminal ganglion neurons that innervate the cornea are transected by laser-assisted in situ keratomileusis (LASIK). Although most patients do not experience postoperative pain, a small subgroup develop persistent ocular pain. We previously carried out genomic analysis and determined that some patients with persistent pain after axotomy of corneal axons during refractive surgery carry mutations in genes that encode the electrogenisome of trigeminal ganglion neurons, the ensemble of ion channels and receptors that regulate excitability within these cells, including SCN9A, which encodes sodium channel Nav1.7, a threshold channel abundantly expressed in sensory neurons that has been implicated in a number of pain-related disorders. Here, we describe the biophysical and electrophysiological profiling of the P610T Nav1.7 mutation found in two male siblings with persistent ocular pain after refractive surgery. Our results indicate that this mutation impairs the slow inactivation of Nav1.7. As expected from this proexcitatory change in channel function, we also demonstrate that this mutation produces increased spontaneous activity in trigeminal ganglion neurons. These findings suggest that this gain-of-function mutation in Nav1.7 may contribute to pain after injury to the axons of trigeminal ganglion neurons.NEW & NOTEWORTHY Mechanisms underlying pain after axonal injury remain elusive. A small subgroup of patients experience pain after corneal refractive surgery, providing a human pain model after well-defined injury to axons. Here we analyze a mutation (P610T) in Nav1.7, a threshold sodium channel expressed in nociceptors, found in two siblings with persistent ocular pain after refractive surgery. We show that it impairs channel slow inactivation, thereby triggering inappropriate repetitive activity in trigeminal ganglion axons that signal eye pain.

Photomodulation of reconstituted voltage-dependent K+ channels.

The use of optogenetics for light-controlled ion flow has significantly impacted neuroscience, enabling isolated cell excitation or suppression within complex neural tissues. Achieving the same effect without the prior need to modify membrane channels genetically may represent a significant advantage for various applications. Here we tested whether photo-susceptible lipids may modulate the open probability of voltage-gated ion channels in their vicinity. The approach uses the observation that channel gating may be affected by membrane tension and depends on the material properties of the embedding lipid bilayer, such as spontaneous membrane curvature and membrane thickness [1]. We reconstituted KvAP, the voltage-gated potassium channel from Aeropyrum pernix, into photoswitchable horizontal solvent-depleted planar lipid bilayers. KvAP open probability suddenly increased when light exposure switched the photolipids from their conical to their cylindrical state. The effect was fully reversible as the photolipids could be switched back to their original state by exposure to light of a different wavelength. Single channel recordings and current recordings of channel ensembles confirmed the multiple repeatabilities of the photocycle. Our observation paves the way for the specific modulation of cells in vivo. This project was supported by a grant from the FWF (Austrian Science Fund): P34826. 1. Pfeffermann et al., Journal of Photochemistry and Photobiology B: Biology, 2021: p. 112320.

KeepNMax: Keep N Maximum of Epoch-Channel Ensemble Method for Deep Learning Models

Computer vision (CV) application is becoming a crucial factor for the growth of developed economies in the world. The widespread use of CV applications has created a growing demand for accurate models. Therefore, the subfields of CV focus on improving existing models and developing new methods and algorithms to meet the demands of different sectors. Simultaneously, research on ensemble learning provides effective tools for increasing the accuracies of the models. Nevertheless, there is a significant gap in research using data representation and model features. This led us to develop KeepNMax—an ensemble of image channels and epochs using the top N maximum prediction probabilities at the final step. Using KeepNMax, the ensemble error was reduced and increased the amount of data knowledge of the ensemble model. Nine datasets were trained. As long as each dataset had three channels, the images were divided into three different channels and trained them separately using the same model architecture. In addition, the datasets were trained without dividing them into different channels using the same model architecture. After completing the training, some epochs of the training were ensembled to the best epoch in the training. In addition, two different model architectures were used to check the model dependency of the proposed method and achieved remarkable results in both cases. This method was proposed for deep-learning classification models. Despite its simplicity, proposed method improved the results of the CNN and ConvMixer models for the datasets used. Classic training, bootstrap aggregation, and random split methods were used as the baseline methods. For most datasets, significant results were obtained using KeepNMax. The success of the method was explained by the unique true prediction ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$UTP$ </tex-math></inline-formula> ) scope of each model. By ensembling the models, the prediction scope of the model was enlarged, allowing it to represent broader knowledge about datasets than a simple model.

Oxytocin-Modulated Ion Channel Ensemble Controls Depolarization, Integration and Burst Firing in CA2 Pyramidal Neurons.

Oxytocin (OXT) and OXT receptor (OXTR)-mediated signaling control excitability, firing patterns, and plasticity of hippocampal CA2 pyramidal neurons, which are pivotal in generation of brain oscillations and social memory. Nonetheless, the ionic mechanisms underlying OXTR-induced effects in CA2 neurons are not fully understood. Using slice physiology in a reporter mouse line and interleaved current-clamp and voltage-clamp experiments, we systematically identified the ion channels modulated by OXT signaling in CA2 pyramidal cells (PYRs) in mice of both sexes and explored how changes in channel conductance support altered electrical activity. Activation of OXTRs inhibits an outward potassium current mediated by inward rectifier potassium channels (IKir) and thus favoring membrane depolarization. Concomitantly, OXT signaling also diminishes inward current mediated by hyperpolarization-activated cyclic-nucleotide-gated (HCN) channels (Ih), providing a hyperpolarizing drive. The combined reduction in both IKir and Ih synergistically elevate the membrane resistance and favor dendritic integration while the membrane potential is restrained from quickly depolarizing from rest. As a result, the responsiveness of CA2 PYRs to synaptic inputs is highly sharpened during OXTR activation. Unexpectedly, OXTR signaling also strongly enhances a tetrodotoxin-resistant (TTX-R), voltage-gated sodium current that helps drive the membrane potential to spike threshold and thus promote rhythmic firing. This novel array of OXTR-stimulated ionic mechanisms operates in close coordination and underpins OXT-induced burst firing, a key step in CA2 PYRs' contribution to hippocampal information processing and broader influence on brain circuitry. Our study deepens our understanding of underpinnings of OXT-promoted social memory and general neuropeptidergic control of cognitive states.SIGNIFICANCE STATEMENT Oxytocin (OXT) plays key roles in reproduction, parenting and social and emotional behavior, and deficiency in OXT receptor (OXTR) signaling may contribute to neuropsychiatric disorders. We identified a novel array of OXTR-modulated ion channels that operate in close coordination to retune hippocampal CA2 pyramidal neurons, enhancing responsiveness to synaptic inputs and sculpting output. OXTR signaling inhibits both potassium conductance (IKir) and mixed cation conductance (Ih), engaging opposing influences on membrane potential, stabilizing it while synergistically elevating membrane resistance and electrotonic spread. OXT signaling also facilitates a tetrodotoxin-resistant (TTX-R) Na+ current, not previously described in hippocampus (HP), engaged on further depolarization. This TTX-R current lowers the spike threshold and supports rhythmic depolarization and burst firing, a potent driver of downstream circuitry.

Quantum channel-position finding using single photons

Channel-position finding is the task of determining the location of a single target channel among an ensemble of background channels. It has many potential applications, including quantum sensing, quantum reading, and quantum spectroscopy. In particular, it could allow for simple detection protocols to be extended to ones of measurement, for example, target ranging with quantum illumination. The use of quantum states and entanglement in such protocols has shown to yield quantum advantages over their optimal classical counterparts. Here we consider quantum channel-position finding using sources specified by at most one single photon on average per mode, using the discrete-variable formalism. By considering various quantum sources, it is shown through the derivation of performance bounds that a quantum enhancement may be realized.

Error Probability Mitigation in Quantum Reading Using Classical Codes.

A general framework describing the statistical discrimination of an ensemble of quantum channels is given by the name quantum reading. Several tools can be applied in quantum reading to reduce the error probability in distinguishing the ensemble of channels. Classical and quantum codes can be envisioned for this goal. The aim of this paper is to present a simple but fruitful protocol for this task using classical error-correcting codes. Three families of codes are considered: Reed–Solomon codes, BCH codes, and Reed–Muller codes. In conjunction with the use of codes, we also analyze the role of the receiver. In particular, heterodyne and Dolinar receivers are taken into consideration. The encoding and measurement schemes are connected by the probing step. As probes, we consider coherent states. In such a simple manner, interesting results are obtained. As we show, there is a threshold below which using codes surpass optimal and sophisticated schemes for any fixed rate and code. BCH codes in conjunction with Dolinar receiver turn out to be the optimal strategy for error mitigation in quantum reading.

Ultimate limits of thermal pattern recognition

Quantum Channel Discrimination (QCD) presents a fundamental task in quantum information theory, with critical applications in quantum reading, illumination, data-readout and more. The extension to multiple quantum channel discrimination has seen a recent focus to characterise potential quantum advantage associated with quantum enhanced discriminatory protocols. In this paper, we study thermal imaging as an environment localisation task, in which thermal images are modelled as ensembles of Gaussian phase insensitive channels with identical transmissivity, and pixels possess properties according to background (cold) or target (warm) thermal channels. Via the teleportation stretching of adaptive quantum protocols, we derive ultimate limits on the precision of pattern classification of abstract, binary thermal image spaces, and show that quantum enhanced strategies may be used to provide significant quantum advantage over known optimal classical strategies. The environmental conditions and necessary resources for which advantage may be obtained are studied and discussed. We then numerically investigate the use of quantum enhanced statistical classifiers, in which quantum sensors are used in conjunction with machine learning image classification methods. Proving definitive advantage in the low loss regime, this work motivates the use of quantum enhanced sources for short-range thermal imaging and detection techniques for future quantum technologies.

Enhancing detection of steady-state visual evoked potentials using channel ensemble method

Objective. This study proposed and evaluated a channel ensemble approach to enhance detection of steady-state visual evoked potentials (SSVEPs). Approach. Collected multi-channel electroencephalogram signals were classified into multiple groups of new analysis signals based on correlation analysis, and each group of analysis signals contained signals from a different number of electrode channels. These groups of analysis signals were used as the input of a training-free feature extraction model, and the obtained feature coefficients were converted into feature probability values using the softmax function. The ensemble value of multiple sets of feature probability values was determined and used as the final discrimination coefficient. Main results. Compared with canonical correlation analysis, likelihood ratio test, and multivariate synchronization index analysis methods using a standard approach, the recognition accuracies of the methods using a channel ensemble approach were improved by 5.05%, 3.87%, and 3.42%, and the information transfer rates (ITRs) were improved by 6.00%, 4.61%, and 3.71%, respectively. The channel ensemble method also obtained better recognition results than the standard algorithm on the public dataset. This study validated the efficiency of the proposed method to enhance the detection of SSVEPs, demonstrating its potential use in practical brain–computer interface (BCI) systems. Significance. A SSVEP-based BCI system using a channel ensemble method could achieve high ITR, indicating great potential of this design for various applications with improved control and interaction.

On quantum reading, quantum illumination, and other notions

In this brief review, we start by clarifying the crucial differences between three different protocols of quantum channel discrimination. In some recent literature, there has been confusion between the protocols of quantum illumination [, ], quantum reading [], and a scheme of communication within a discrete-variable quantum computer []. While all these protocols are based on the model of quantum channel discrimination, they have completely different applications and features, which is the reason why they have different names and should not be naively confused. We also discuss the notion of quantum reading capacity [] of an ensemble of quantum channels, clarifying how this is easily extended to an adaptive formulation and discussing the mathematical conditions under which it can be connected to the different notion of dense coding capacity [] of a quantum channel.

Quantum-Enhanced Barcode Decoding and Pattern Recognition

Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e.g., for target detection or memory cell readout. In this work, we generalize this theoretical model to the multi-partite setting of barcode decoding and pattern recognition. We start by defining a digital image as an array or grid of pixels, each pixel corresponding to an ensemble of quantum channels. Specializing each pixel to a black and white alphabet, we naturally define an optical model of barcode. In this scenario, we show that the use of quantum entangled sources, combined with suitable measurements and data processing, greatly outperforms classical coherent-state strategies for the tasks of barcode data decoding and classification of black and white patterns. Moreover, introducing relevant bounds, we show that the problem of pattern recognition is significantly simpler than barcode decoding, as long as the minimum Hamming distance between images from different classes is large enough. Finally, we theoretically demonstrate the advantage of using quantum sensors for pattern recognition with the nearest neighbor classifier, a supervised learning algorithm, and numerically verify this prediction for handwritten digit classification.

Open-Channel Capillary Trees and Capillary Pumping.

Velocity of capillary flow in closed or open channels decreases as the flow proceeds down the length of the channel, varying as the inverse of the square root of time or as the inverse of travel distance. In order to increase the flow rate-and extend the duration of the flow-capillary pumps have been designed by mimicking the pumping principle of paper or cotton fibers. These designs provide a larger volume available for the wicking of the liquids. In microsystems for biotechnology, different designs have been developed based on experimental observation. In the present paper, the mechanisms at the basis of capillary pumping are investigated using a theoretical model for the flow in an open-channel "capillary tree" (i.e., an ensemble of channels with bifurcations mimicking the shape of a tree). The model is checked against experiments. Rules for obtaining better designs of capillary pumps are proposed; specifically, we find (1) when using a capillary tree with identical channel cross-sectional areas throughout, it is possible to maintain nearly constant flow rates throughout the channel network, (2) flow rate can be increased at each branch point of a capillary tree by slightly decreasing the areas of the channel cross section and decreasing the channel lengths at each level of ramification within the tree, and (3) higher order branching (trifurcations vs bifurcations) amplify the flow rate effect. This work lays the foundation for increasing the flow rate in open microfluidic channels driven by capillary flow; we expect this to have broad impact across open microfluidics for biological and chemical applications such as cell culture, sample preparation, separations, and on-chip reactions.

Statistical feature and channel selection for upper limb classification using sEMG signal processing

Feature and channel selection stage plays an important role in identifying movement intent through surface electromyography (sEMG), once the best feature or features set and channel ensemble is still an open topic in this research field. In this work, we present three different strategies to feature and channel selection used in movement recognition through sEMG processing. This paper analyzes the feature selection stage, considering the relevance of each feature-channel pair to movement characterization. Three feature selection methods based on support vector machine recursive feature elimination (SVM-RFE), Monte Carlo feature selection, and singular value decomposition (SVD) entropy that are benchmark techniques for DNA microarray data analysis formed a ranking of feature/channel pairs. The ranking, organized according to their importance to movement classification, forms a heuristic for a wrapper method based feature selection algorithm, which uses the regularized extreme learning machine (RELM) classifier for the movement recognition. To test the proposed methods, we used the NINAPro Databases 2 and 3 (40 intact and 10 amputee volunteers, respectively). The results derived from the technique reached average accuracies of 84.9%, 84.0%, and 83.9% for the SVM-RFE, Monte Carlo, and SVD entropy ranking methods, respectively, for database 2. At the same time, the same methods achieved 74.8%, 74.5%, and 74.1% of accuracy for database 3, respectively. In terms of most selected features, zero crossings (TZC), mean frequency (FMN), signal slope changes (TSLPCH2), and power (TPWR) were the most frequently selected. The results obtained showed no statistically significant difference among the three feature selection algorithms implemented regarding the classification accuracy. The introduction of the pre-processing steps in the classification process provided promising classification accuracy levels, outperforming the results presented by related works with the same NINAPro database.

Distributed Recovery of a Gaussian Source in Interference with Successive Lattice Processing

A scheme for recovery of a signal by distributed listeners in the presence of Gaussian interference is constructed by exhausting an “iterative power reduction” property. An upper bound for the scheme’s achieved mean-squared-error distortion is derived. The strategy exposes a parameter search problem, which, when solved, causes the scheme to outperform others of its kind. Performance of a blocklength-one scheme is simulated and is seen to improve over plain source coding without compression in the presence of many interferers, and experiences less outages over ensembles of channels.

Multi-proportion channel ensemble model for retinal vessel segmentation

ObjectiveA novel supervised method that is based on the Multi-Proportion Channel Ensemble Model (MPC-EM) is proposed to obtain more vessel details with reduced computational complexity. MethodsExisting Retinal Vessel Segmentation (RVS) algorithms only work using the single G channel (Green Channel) of fundus images because that channel normally contains the most details with the least noise, while the red and blue channels are usually saturated and noisy. However, we find that the images that are composed of the αG-channel and (1-α) R-channel (Red Channel) with different values of α produce multiple particular global features. This enables the model to detect more local vessel details in fundus images. Therefore, we provide a detailed description and evaluation of the segmentation approach based on the MPC-EM for the RVS. The segmentation approach consists of five identical submodels. Each submodel can capture various vessel details by being trained using different composition images. These probabilistic maps that are produced by five submodels are averaged to achieve the final refined segmentation results. ResultsThe proposed approach is evaluated using 4 well-established datasets, i.e., DRIVE, STARE, HRF and CHASE_DB1, with accuracies of 95.74%, 96.95%, 96.31%, and 96.54%, respectively. Additionally, quantitative comparisons with other existing methods and cross-training results are included. ConclusionThe segmentation results showed that the proposed algorithm based on the MPC-EM with simple submodels can achieve state-of-the-art accuracy with reduced computational complexity. SignificanceCompared with other existing methods that are trained using only the G channel and raw images, the proposed approach based on the MPC-EM, submodels of which are trained using different proportional compositions of R and G channels, obtains better segmentation accuracy and robustness. Additionally, the experimental results show that the R channel of fundus images can also produce performance gains for RVS.

Deep Sequencing of Somatosensory Neurons Reveals Molecular Determinants of Intrinsic Physiological Properties

Dorsal root ganglion (DRG) sensory neuron subtypes defined by their invivo properties display distinct intrinsic electrical properties. We used bulk RNA sequencing of genetically labeled neurons and electrophysiological analyses to define ion channel contributions to the intrinsic electrical properties of DRG neuron subtypes. The transcriptome profiles of eight DRG neuron subtypes revealed differentially expressed and functionally relevant genes, including voltage-gated ion channels. Guided by these data, electrophysiological analyses using pharmacological and genetic manipulations as well as computational modeling of DRG neuron subtypes were undertaken to assess the functions of select voltage-gated potassium channels (Kv1, Kv2, Kv3, and Kv4) in shaping action potential (AP) waveforms and firing patterns. Our findings show that the transcriptome profiles have predictive value for defining ion channel contributions to sensory neuron subtype-specific intrinsic physiological properties. The distinct ensembles of voltage-gated ion channels predicted to underlie the unique intrinsic physiological properties of eight DRG neuron subtypes are presented.

Conditional channel simulation

In this work we design a specific simulation tool for quantum channels which is based on the use of a control system. This allows us to simulate an average quantum channel which is expressed in terms of an ensemble of channels, even when these channel-components are not jointly teleportation-covariant. This design is also extended to asymptotic simulations, continuous ensembles, and memory channels. As an application, we derive relative-entropy-of-entanglement upper bounds for private communication over various channels, including non-Gaussian mixtures of bosonic lossy channels. Among other results, we also establish the two-way quantum and private capacity of the so-called “dephrasure” channel.

The voltage‐gated potassium channel Kv1.3 is required for microglial pro‐inflammatory activation in vivo

Microglia show a rich repertoire of activation patterns regulated by a complex ensemble of surface ion channels, receptors, and transporters. We and others have investigated whether microglia vary their K+ channel expression as a means to achieve functional diversity. However, most of the prior studies were conducted using in vitro models such as BV2 cells, primary microglia, or brain slices in culture, which may not accurately reflect microglia physiology in adult individuals. Here we employed an in vivo mouse model of selective innate immune activation by intracerebroventricular injection of lipopolysaccharides (ICV-LPS) to determine the role of the voltage-gated Kv1.3 channel in LPS-induced M1-like microglial activation. Using microglia acutely isolated from adult brains, we detected Kv1.3 and Kir2.1 currents, and found that ICV-LPS increased the current density and RNA expression of Kv1.3 but did not affect those of Kir2.1. Genetic knockout of Kv1.3 abolished LPS-induced microglial activation exemplified by Iba-1 immunoreactivity and expression of pro-inflammatory mediators such as IL-1β, TNF-α, IL-6, and iNOS. Moreover, Kv1.3 knockout mitigated the LPS-induced impairment of hippocampal long-term potentiation (hLTP), suggesting that Kv1.3 activity regulates pro-inflammatory microglial neurotoxicity. Pharmacological intervention using PAP-1, a small molecule that selectively blocks homotetrameric Kv1.3 channels, achieved anti-inflammatory and hLTP-recovery effects similar to Kv1.3 knockout. We conclude that Kv1.3 is required for microglial M1-like pro-inflammatory activation in vivo. A significant implication of our in vivo data is that Kv1.3 blockers could be therapeutic candidates for neurological diseases where microglia-mediated neurotoxicity is implicated in the pathogenesis.

Entanglement, quantum randomness, and complexity beyond scrambling

Scrambling is a process by which the state of a quantum system is effectively randomized due to the global entanglement that “hides” initially localized quantum information. Closely related notions include quantum chaos and thermalization. Such phenomena play key roles in the study of quantum gravity, many-body physics, quantum statistical mechanics, quantum information etc. Scrambling can exhibit different complexities depending on the degree of randomness it produces. For example, notice that the complete randomization implies scrambling, but the converse does not hold; in fact, there is a significant complexity gap between them. In this work, we lay the mathematical foundations of studying randomness complexities beyond scrambling by entanglement properties. We do so by analyzing the generalized (in particular Rényi) entanglement entropies of designs, i.e. ensembles of unitary channels or pure states that mimic the uniformly random distribution (given by the Haar measure) up to certain moments. A main collective conclusion is that the Rényi entanglement entropies averaged over designs of the same order are almost maximal. This links the orders of entropy and design, and therefore suggests Rényi entanglement entropies as diagnostics of the randomness complexity of corresponding designs. Such complexities form a hierarchy between information scrambling and Haar randomness. As a strong separation result, we prove the existence of (state) 2-designs such that the Rényi entanglement entropies of higher orders can be bounded away from the maximum. However, we also show that the min entanglement entropy is maximized by designs of order only logarithmic in the dimension of the system. In other words, logarithmic-designs already achieve the complexity of Haar in terms of entanglement, which we also call max-scrambling. This result leads to a generalization of the fast scrambling conjecture, that max-scrambling can be achieved by physical dynamics in time roughly linear in the number of degrees of freedom. This paper is an extended version of Phys. Rev. Lett.120 (2018) 130502 [1].