Spatiotemporal Code Research Articles

Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories.

Here, we demonstrate the first successful use of static neural stimulation patterns for specific information content. These static patterns were derived by a model that was applied to a subject's own hippocampal spatiotemporal neural codes for memory. We constructed a new model of processes by which the hippocampus encodes specific memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of targeted content into short-term memory. A memory decoding model (MDM) of hippocampal CA3 and CA1 neural firing was computed which derives a stimulation pattern for CA1 and CA3 neurons to be applied during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task. MDM electrical stimulation delivered to the CA1 and CA3 locations in the hippocampus during the sample phase of DMS trials facilitated memory of images from the DMS task during a delayed recognition (DR) task that also included control images that were not from the DMS task. Across all subjects, the stimulated trials exhibited significant changes in performance in 22.4% of patient and category combinations. Changes in performance were a combination of both increased memory performance and decreased memory performance, with increases in performance occurring at almost 2 to 1 relative to decreases in performance. Across patients with impaired memory that received bilateral stimulation, significant changes in over 37.9% of patient and category combinations was seen with the changes in memory performance show a ratio of increased to decreased performance of over 4 to 1. Modification of memory performance was dependent on whether memory function was intact or impaired, and if stimulation was applied bilaterally or unilaterally, with nearly all increase in performance seen in subjects with impaired memory receiving bilateral stimulation. These results demonstrate that memory encoding in patients with impaired memory function can be facilitated for specific memory content, which offers a stimulation method for a future implantable neural prosthetic to improve human memory.

Decoding the spatiotemporal regulation of transcription factors during human spinal cord development

The spinal cord is a crucial component of the central nervous system that facilitates sensory processing and motor performance. Despite its importance, the spatiotemporal codes underlying human spinal cord development have remained elusive. In this study, we have introduced an image-based single-cell transcription factor (TF) expression decoding spatial transcriptome method (TF-seqFISH) to investigate the spatial expression and regulation of TFs during human spinal cord development. By combining spatial transcriptomic data from TF-seqFISH and single-cell RNA-sequencing data, we uncovered the spatial distribution of neural progenitor cells characterized by combinatorial TFs along the dorsoventral axis, as well as the molecular and spatial features governing neuronal generation, migration, and differentiation along the mediolateral axis. Notably, we observed a sandwich-like organization of excitatory and inhibitory interneurons transiently appearing in the dorsal horns of the developing human spinal cord. In addition, we integrated data from 10× Visium to identify early and late waves of neurogenesis in the dorsal horn, revealing the formation of laminas in the dorsal horns. Our study also illuminated the spatial differences and molecular cues underlying motor neuron (MN) diversification, and the enrichment of Amyotrophic Lateral Sclerosis (ALS) risk genes in MNs and microglia. Interestingly, we detected disease-associated microglia (DAM)-like microglia groups in the developing human spinal cord, which are predicted to be vulnerable to ALS and engaged in the TYROBP causal network and response to unfolded proteins. These findings provide spatiotemporal transcriptomic resources on the developing human spinal cord and potential strategies for spinal cord injury repair and ALS treatment.

Optogenetic frequency scrambling of hippocampal theta oscillations dissociates working memory retrieval from hippocampal spatiotemporal codes

The precise temporal coordination of activity in the brain is thought to be fundamental for memory function. Inhibitory neurons in the medial septum provide a prominent source of innervation to the hippocampus and play a major role in controlling hippocampal theta (~8 Hz) oscillations. While pharmacological inhibition of medial septal neurons is known to disrupt memory, the exact role of septal inhibitory neurons in regulating hippocampal representations and memory is not fully understood. Here, we dissociate the role of theta rhythms in spatiotemporal coding and memory using an all-optical interrogation and recording approach. We find that optogenetic frequency scrambling stimulations abolish theta oscillations and modulate a portion of neurons in the hippocampus. Such stimulation decreased episodic and working memory retrieval while leaving hippocampal spatiotemporal codes intact. Our study suggests that theta rhythms play an essential role in memory but may not be necessary for hippocampal spatiotemporal codes.

Population coding strategies in human tactile afferents.

Sensory information is conveyed by populations of neurons, and coding strategies cannot always be deduced when considering individual neurons. Moreover, information coding depends on the number of neurons available and on the composition of the population when multiple classes with different response properties are available. Here, we study population coding in human tactile afferents by employing a recently developed simulator of mechanoreceptor firing activity. First, we highlight the interplay of afferents within each class. We demonstrate that the optimal afferent density to convey maximal information depends on both the tactile feature under consideration and the afferent class. Second, we find that information is spread across different classes for all tactile features and that each class encodes both redundant and complementary information with respect to the other afferent classes. Specifically, combining information from multiple afferent classes improves information transmission and is often more efficient than increasing the density of afferents from the same class. Finally, we examine the importance of temporal and spatial contributions, respectively, to the joint spatiotemporal code. On average, destroying temporal information is more destructive than removing spatial information, but the importance of either depends on the stimulus feature analyzed. Overall, our results suggest that both optimal afferent innervation densities and the composition of the population depend in complex ways on the tactile features in question, potentially accounting for the variety in which tactile peripheral populations are assembled in different regions across the body.

Dynamic spatiotemporal determinants modulate GPCR:G protein coupling selectivity and promiscuity

Recent studies have shown that G protein coupled receptors (GPCRs) show selective and promiscuous coupling to different Gα protein subfamilies and yet the mechanisms of the range of coupling preferences remain unclear. Here, we use Molecular Dynamics (MD) simulations on ten GPCR:G protein complexes and show that the location (spatial) and duration (temporal) of intermolecular contacts at the GPCR:Gα protein interface play a critical role in how GPCRs selectively interact with G proteins. We identify that some GPCR:G protein interface contacts are common across Gα subfamilies and others specific to Gα subfamilies. Using large scale data analysis techniques on the MD simulation snapshots we derive a spatio-temporal code for contacts that confer G protein selective coupling and validated these contacts using G protein activation BRET assays. Our results demonstrate that promiscuous GPCRs show persistent sampling of the common contacts more than G protein specific contacts. These findings suggest that GPCRs maintain contact with G proteins through a common central interface, while the selectivity comes from G protein specific contacts at the periphery of the interface.

Non-catalytic allostery in α-TAT1 by a phospho-switch drives dynamic microtubule acetylation.

Spatiotemporally dynamic microtubule acetylation underlies diverse physiological and pathological events. Despite its ubiquity, the molecular mechanisms that regulate the sole microtubule acetylating agent, α-tubulin-N-acetyltransferase-1 (α-TAT1), remain obscure. Here, we report that dynamic intracellular localization of α-TAT1 along with its catalytic activity determines efficiency of microtubule acetylation. Specifically, we newly identified a conserved signal motif in the intrinsically disordered C-terminus of α-TAT1, consisting of three competing regulatory elements-nuclear export, nuclear import, and cytosolic retention. Their balance is tuned via phosphorylation by CDK1, PKA, and CK2, and dephosphorylation by PP2A. While the unphosphorylated form binds to importins and resides both in cytosol and nucleus, the phosphorylated form binds to specific 14-3-3 adapters and accumulates in the cytosol for maximal substrate access. Unlike other molecules with a similar phospho-regulated signal motif, α-TAT1 uniquely uses the nucleus as a hideout. This allosteric spatial regulation of α-TAT1 function may help uncover a spatiotemporal code of microtubule acetylation in normal and aberrant cell behavior.

Phase coding of spatial representations in the human entorhinal cortex.

The grid-like activity pattern of cells in the mammalian entorhinal cortex provides an internal reference frame for allocentric self-localization. The same neurons maintain robust phase couplings with local field oscillations. We found that neurons of the human entorhinal cortex display consistent spatial and temporal phase locking between spikes and slow gamma band local field potentials (LFPs) during virtual navigation. The phase locking maintained an environment-specific map over time. The phase tuning of spikes to the slow gamma band LFP revealed spatially periodic phase grids with environment-dependent scaling and consistent alignment with the environment. Using a Bayesian decoding model, we could predict the avatar’s position with near perfect accuracy and, to a lesser extent, that of heading direction as well. These results imply that the phase of spikes relative to spatially modulated gamma oscillations encode allocentric spatial positions. We posit that a joint spatiotemporal phase code can implement the combined neural representation of space and time in the human entorhinal cortex.

Artificial SA-I, RA-I and RA-II/vibrotactile afferents for tactile sensing of texture.

Robot touch can benefit from how humans perceive tactile textural information, from the stimulation mode to which tactile channels respond, then the tactile cues and encoding. Using a soft biomimetic tactile sensor (the TacTip) based on the physiology of the dermal-epidermal boundary, we construct two biomimetic tactile channels based on slowly adapting SA-I and rapidly adapting RA-I afferents, and introduce an additional sub-modality for vibrotactile information with an embedded microphone interpreted as an artificial RA-II channel. These artificial tactile channels are stimulated dynamically with a set of 13 artificial rigid textures comprising raised-bump patterns on a rotating drum that vary systematically in roughness. Methods employing spatial, spatio-temporal and temporal codes are assessed for texture classification insensitive to stimulation speed. We find: (i) spatially encoded frictional cues provide a salient representation of texture; (ii) a simple transformation of spatial tactile features to model natural afferent responses improves the temporal coding; and (iii) the harmonic structure of induced vibrations provides a pertinent code for speed-invariant texture classification. Just as human touch relies on an interplay between slowly adapting (SA-I), rapidly adapting (RA-I) and vibrotactile (RA-II) channels, this tripartite structure may be needed for future robot applications with human-like dexterity, from prosthetics to materials testing, handling and manipulation.

An Open-Source Workflow for Spatiotemporal Studies with COVID-19 as an Example

Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code, and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository.

A 4D single-cell protein atlas of transcription factors delineates spatiotemporal patterning during embryogenesis.

Complex biological processes such as embryogenesis require precise coordination of cell differentiation programs across both space and time. Using protein-fusion fluorescent reporters and four-dimensional live imaging, we present a protein expression atlas of transcription factors (TFs) mapped onto developmental cell lineages during Caenorhabditis elegans embryogenesis, at single-cell resolution. This atlas reveals a spatiotemporal combinatorial code of TF expression, and a cascade of lineage-specific, tissue-specific and time-specific TFs that specify developmental states. The atlas uncovers regulators of embryogenesis, including an unexpected role of a skin specifier in neurogenesis and the critical function of an uncharacterized TF in convergent muscle differentiation. At the systems level, the atlas provides an opportunity to model cell state-fate relationships, revealing a lineage-dependent state diversity within functionally related cells and a winding trajectory of developmental state progression. Collectively, this single-cell protein atlas represents a valuable resource for elucidating metazoan embryogenesis at the molecular and systems levels.

An Efficient Organization Method for Large-Scale and Long Time-Series Remote Sensing Data in a Cloud Computing Environment

Historical earth observation (EO) data have played an important role in long-term scientific and environmental monitoring. The effective organization of large-scale and long-term remote-sensing data to achieve efficient retrieval and access has become one of the important issues. However, inherent big data characteristics, such as a large scale, and asymmetrical temporal and spatial distributions, have caused problems with the efficiency of data retrieval and access. Therefore, this study proposes an efficient data organization method for use in a cloud-computing environment that has two aims. First, it addresses the problem of low retrieval efficiency. An asymmetrical index model for the image metadata is constructed that is based on a unified spatio-temporal grid coding; a prepartitioning mechanism under the HBase architecture is established to realize the uniform storage of the metadata with an asymmetrical spatiotemporal distribution and to avoid retrieval efficiency bottlenecks caused by a load imbalance. Second, it addresses low access efficiency. By dividing the remote-sensing image into tiles, a unified spatio-temporal code is established for each tile, and a consistent hash operation is performed; tiles with similar hash values are stored in the same or adjacent Hadoop Distributed File System nodes. In this way, tiles with temporal or spatial correlations can be gathered and stored, and lots of disk seeks can be avoided during retrieval, thereby greatly improving the data access efficiency. Comparative experiments showed that the data organization method can effectively improve the retrieval and access efficiencies of large-scale and long time-series remote-sensing data in a cloud-computing environment.

A molecular odorant transduction model and the complexity of spatio-temporal encoding in the Drosophila antenna.

Over the past two decades, substantial amount of work has been conducted to characterize different odorant receptors, neuroanatomy and odorant response properties of the early olfactory system of Drosophila melanogaster. Yet many odorant receptors remain only partially characterized, and the odorant transduction process and the axon hillock spiking mechanism of the olfactory sensory neurons (OSNs) have yet to be fully determined. Identity and concentration, two key characteristics of the space of odorants, are encoded by the odorant transduction process. Detailed molecular models of the odorant transduction process are, however, scarce for fruit flies. To address these challenges we advance a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process (OTP) and a biophysical spike generator (BSG). We model odorant identity and concentration using an odorant-receptor binding rate tensor, modulated by the odorant concentration profile, and an odorant-receptor dissociation rate tensor, and quantitatively describe the mechanics of the molecular ligand binding/dissociation of the OTP. We model the BSG as a Connor-Stevens point neuron. The resulting spatio-temporal encoding model of the Drosophila antenna provides a theoretical foundation for understanding the neural code of both odorant identity and odorant concentration and advances the state-of-the-art in a number of ways. First, it quantifies on the molecular level the spatio-temporal level of complexity of the transformation taking place in the antennae. The concentration-dependent spatio-temporal code at the output of the antenna circuits determines the level of complexity of olfactory processing in the downstream neuropils, such as odorant recognition and olfactory associative learning. Second, the model is biologically validated using multiple electrophysiological recordings. Third, the model demonstrates that the currently available data for odorant-receptor responses only enable the estimation of the affinity of the odorant-receptor pairs. The odorant-dissociation rate is only available for a few odorant-receptor pairs. Finally, our model calls for new experiments for massively identifying the odorant-receptor dissociation rates of relevance to flies.

Circuit and Cellular Mechanisms Facilitate the Transformation from Dense to Sparse Coding in the Insect Olfactory System.

Transformations between sensory representations are shaped by neural mechanisms at the cellular and the circuit level. In the insect olfactory system, the encoding of odor information undergoes a transition from a dense spatiotemporal population code in the antennal lobe to a sparse code in the mushroom body. However, the exact mechanisms shaping odor representations and their role in sensory processing are incompletely identified. Here, we investigate the transformation from dense to sparse odor representations in a spiking model of the insect olfactory system, focusing on two ubiquitous neural mechanisms: spike frequency adaptation at the cellular level and lateral inhibition at the circuit level. We find that cellular adaptation is essential for sparse representations in time (temporal sparseness), while lateral inhibition regulates sparseness in the neuronal space (population sparseness). The interplay of both mechanisms shapes spatiotemporal odor representations, which are optimized for the discrimination of odors during stimulus onset and offset. Response pattern correlation across different stimuli showed a nonmonotonic dependence on the strength of lateral inhibition with an optimum at intermediate levels, which is explained by two counteracting mechanisms. In addition, we find that odor identity is stored on a prolonged timescale in the adaptation levels but not in the spiking activity of the principal cells of the mushroom body, providing a testable hypothesis for the location of the so-called odor trace.

Exploiting morphological data from Pulsar Wind Nebulae via a spatiotemporal leptonic transport code

ABSTRACT The next era of ground-based Cherenkov telescope development will see a great increase in both quantity and quality of γ-ray morphological data. This initiated the development of a spatiotemporal leptonic transport code to model pulsar wind nebulae. We present results from this code that predicts the evolution of the leptonic particle spectrum and radiation at different radii in a spherically symmetric source. We simultaneously fit the overall broad-band spectral energy distribution, the surface brightness profile, and the X-ray photon index versus radius for PWN 3C 58, PWN G21.5 − 0.9, and PWN G0.9 + 0.1. Such concurrent fitting of disparate data sets is non-trivial and we thus investigate the utility of different goodness-of-fit statistics, specifically the traditional χ2 test statistic and a newly developed scaled-flux-normalized test statistic to obtain best-fitting parameters. We find reasonable fits to the spatial and spectral data of all three sources, but note some remaining degeneracies that will have to be broken by future observations.

Studying β1 and β2 adrenergic receptor signals in cardiac cells using FRET-based sensors

Cyclic 3′-5′ adenosine monophosphate (cAMP) is a key modulator of cardiac function. Thanks to the sophisticated organization of its pathway in distinct functional units called microdomains, cAMP is involved in the regulation of both inotropy and chronotropy as well as transcription and cardiac death. While visualization of cAMP microdomains can be achieved thanks to cAMP-sensitive FRET-based sensors, the molecular mechanisms through which cAMP-generating stimuli are coupled to distinct functional outcomes are not well understood. One possibility is that each stimulus activates multiple microdomains in order to generate a spatiotemporal code that translates into function. To test this hypothesis here we propose a series of experimental protocols that allow to simultaneously follow cAMP or Protein Kinase A (PKA)-dependent phosphorylation in different subcellular compartments of living cells. We investigate the responses of β Adrenergic receptors (β1AR and β2AR) challenged with selective drugs that enabled us to measure the actions of each receptor independently. At the whole cell level, we used a combination of co-culture with selective βAR stimulation and were able to molecularly separate cardiac fibroblasts from neonatal rat ventricular myocytes based on their cAMP responses. On the other hand, at the subcellular level, these experimental protocols allowed us to dissect the relative weight of β1 and β2 adrenergic receptors on cAMP signalling at the cytosol and outer mitochondrial membrane of NRVMs. We propose that experimental procedures that allow the collection of multiparametric data are necessary in order to understand the molecular mechanisms underlying the coupling between extracellular signals and cellular responses.

Response: Commentary: The Developmental Trajectory of the Operational Momentum Effect.

Fischer et al. (2018) (henceforth: FMS Campbell and Metcalfe, 2007). Therefore, FMS Shaki et al., 2018) invalidates the compression account of the OM the logarithm of zero is not defined. This argumentation is flawed because FMS Harvey et al., 2013). In the latter case, the logarithmic function is used as approximation of the relation between external physical magnitude and its internal representation. However, it makes no sense to assume that cortical circuits actually compute the faithful mathematical log transformation of a given sensory information. The intensity of external physical stimuli is internally represented via non-linear spatio-temporal neural codes (e.g., rate code, population code). Basing their criticism on the restriction of the definition of the logarithm to positive real numbers, FM&S conflate the definition with the neural and cognitive representation of magnitudes. Moreover, even assuming that the cognitive

Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall

Objective. We demonstrate here the first successful implementation in humans of a proof-of-concept system for restoring and improving memory function via facilitation of memory encoding using the patient’s own hippocampal spatiotemporal neural codes for memory. Memory in humans is subject to disruption by drugs, disease and brain injury, yet previous attempts to restore or rescue memory function in humans typically involved only nonspecific, modulation of brain areas and neural systems related to memory retrieval. Approach. We have constructed a model of processes by which the hippocampus encodes memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of short-term memory. A nonlinear multi-input, multi-output (MIMO) model of hippocampal CA3 and CA1 neural firing is computed that predicts activation patterns of CA1 neurons during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task. Main results. MIMO model-derived electrical stimulation delivered to the same CA1 locations during the sample phase of DMS trials facilitated short-term/working memory by 37% during the task. Longer term memory retention was also tested in the same human subjects with a delayed recognition (DR) task that utilized images from the DMS task, along with images that were not from the task. Across the subjects, the stimulated trials exhibited significant improvement (35%) in both short-term and long-term retention of visual information. Significance. These results demonstrate the facilitation of memory encoding which is an important feature for the construction of an implantable neural prosthetic to improve human memory.

Neuronal cell fate specification by the molecular convergence of different spatio-temporal cues on a common initiator terminal selector gene.

The extensive genetic regulatory flows underlying specification of different neuronal subtypes are not well understood at the molecular level. The Nplp1 neuropeptide neurons in the developing Drosophila nerve cord belong to two sub-classes; Tv1 and dAp neurons, generated by two distinct progenitors. Nplp1 neurons are specified by spatial cues; the Hox homeotic network and GATA factor grn, and temporal cues; the hb -> Kr -> Pdm -> cas -> grh temporal cascade. These spatio-temporal cues combine into two distinct codes; one for Tv1 and one for dAp neurons that activate a common terminal selector feedforward cascade of col -> ap/eya -> dimm -> Nplp1. Here, we molecularly decode the specification of Nplp1 neurons, and find that the cis-regulatory organization of col functions as an integratory node for the different spatio-temporal combinatorial codes. These findings may provide a logical framework for addressing spatio-temporal control of neuronal sub-type specification in other systems.

Latent Dirichlet Allocation for Unsupervised Activity Analysis on an Autonomous Mobile Robot

For autonomous robots to collaborate on joint tasks with humans they require a shared understanding of an observed scene. We present a method for unsupervised learning of common human movements and activities on an autonomous mobile robot, which generalises and improves on recent results. Our framework encodes multiple qualitative abstractions of RGBD video from human observations and does not require external temporal segmentation. Analogously to information retrieval in text corpora, each human detection is modelled as a random mixture of latent topics. A generative probabilistic technique is used to recover topic distributions over an auto-generated vocabulary of discrete, qualitative spatio-temporal code words. We show that the emergent categories align well with human activities as interpreted by a human. This is a particularly challenging task on a mobile robot due to the varying camera viewpoints which lead to incomplete, partial and occluded human detections.

Reading out a spatiotemporal population code by imaging neighbouring parallel fibre axons in vivo

The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing.