scholarly journals PV neurons improve cortical complex scene analysis by enhancing timing-based coding

2021 ◽  
Author(s):  
Jian Carlo Nocon ◽  
Howard J Gritton ◽  
Nicholas M James ◽  
Xue Han ◽  
Kamal Sen
Keyword(s):  
Cognitive Abilities ◽  
Temporal Dynamics ◽  
Spatial Competition ◽  
Spike Timing ◽  
Scene Analysis ◽  
Cocktail Party ◽  
Complex Scene ◽  
Sensory Stimuli ◽  
Wide Range ◽  
Complex Scenes

Cortical representations underlying a wide range of cognitive abilities, which employ both rate and spike timing-based coding, emerge from underlying cortical circuits with a tremendous diversity of cell types. However, cell-type specific contributions to cortical coding are not well-understood. Here, we investigate the role of parvalbumin (PV) neurons in cortical complex scene analysis. Many complex scenes contain sensory stimuli, e.g., natural sounds, images, odors or vibrations, which are highly dynamic in time, competing with stimuli at other locations in space. PV neurons are thought to play a fundamental role in sculpting cortical temporal dynamics; yet their specific role in encoding complex scenes via timing-based codes, and the robustness of such temporal representations to spatial competition, have not been investigated. Here, we address these questions in auditory cortex using a cocktail party-like paradigm; integrating electrophysiology, optogenetic manipulations, and a family of novel spike-distance metrics, to dissect the contributions of PV neurons towards rate and timing-based coding. We find that PV neurons improve cortical discrimination of dynamic naturalistic sounds in a cocktail party-like setting by enhancing rapid temporal modulations in rate and spike timing reproducibility. Moreover, this temporal representation is maintained in the face of competing stimuli at other spatial locations, providing a robust code for complex scene analysis. These findings provide novel insights into the specific contributions of PV neurons in cortical coding of complex scenes.

scholarly journals Evidence that loss of consciousness under anesthesia is not associated with impaired stimulus representation in auditory cortex

2017 ◽  
Author(s):  
Matthew I. Banks ◽  
Bryan M. Krause ◽  
Nicholas S. Moran ◽  
Sean M. Grady ◽  
Jeremiah Kakes ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Spike Timing ◽  
Loss Of Consciousness ◽  
Sensory Stimuli ◽  
Stimulus Representation ◽  
Loss Of Righting Reflex ◽  
Primary Sensory Cortex ◽  
Wide Range ◽  
Consistent Change ◽  
Stimulus Features

AbstractThe mechanism whereby anesthetics cause loss of consciousness (LOC) is poorly understood. Current theories suggest that impaired representation of information in cortico-thalamic networks contributes to LOC under anesthesia. We sought to determine whether such changes are present in auditory cortex using information theoretic analysis of multiunit responses in rats. We tested the effects of three agents with different molecular targets: isoflurane, which acts at multiple pre- and postsynaptic loci, propofol, which acts primarily on GABAA receptors, and dexmedetomidine, an α2 adrenergic agonist. We reasoned that changes in the representation of sensory stimuli causative for LOC would be present regardless of the molecular target of the anesthetic. All three agents caused LOC, as assayed by the loss of righting reflex (LORR). We presented acoustic stimuli that varied across a wide range of temporal and spectral dynamics under control, sub-hypnotic (i.e. dose too low to cause LORR), just-hypnotic (a dose just sufficient to cause LORR) and recovery conditions. Changes in mutual information (MI) between the stimulus and spike responses under anesthesia diverged in two ways from predictions of a model in which stimulus representation is impaired upon LOC. First, the sign of changes in MI was agent-specific: MI increased under dexmedetomidine, while it decreased under isoflurane and propofol. Second, there was no consistent change in MI when transitioning from sub-hyptnotic to just-hypnotic doses: for none of the agents did MI decrease at the higher dose, and in some cases MI actually increased relative to the sub-hypnotic dose. Changes in MI under anesthesia were strongly correlated with changes in precision and reliability of spike timing, consistent with the importance of temporal stimulus features in driving auditory cortical activity. These data indicate that primary sensory cortex is not the locus for changes in information representation causative for LOC under anesthesia.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Karim El-Laithy ◽  
Martin Bogdan
Keyword(s):  
Reinforcement Learning ◽  
Spike Timing ◽  
Neural Representation ◽  
Model Parameters ◽  
Learning Framework ◽  
Reference Target ◽  
Wide Range ◽  
Spiking Network ◽  
Dynamic Synapses ◽  
Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

scholarly journals Meta-analysis of epigenome-wide associations between DNA methylation at birth and childhood cognitive skills

2020 ◽  
Author(s):  
Doretta Caramaschi ◽  
Alexander Neumann ◽  
Andres Cardenas ◽  
Gwen Tindula ◽  
Silvia Alemany ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Cord Blood ◽  
Cognitive Abilities ◽  
Cognitive Skills ◽  
Strong Predictor ◽  
Meta Analysis ◽  
Later Life ◽  
Life Outcomes ◽  
Cognitive Domains ◽  
Wide Range

ABSTRACTCognitive skills are a strong predictor of a wide range of later life outcomes. Genetic and epigenetic associations across the genome explain some of the variation in general cognitive abilities in the general population and it is plausible that epigenetic associations might arise from prenatal environmental exposures and/or genetic variation early in life. We investigated the association between cord blood DNA methylation at birth and cognitive skills assessed in children from eight pregnancy cohorts (N=2196-3798) within the Pregnancy And Childhood Epigenetics (PACE) Consortium across overall, verbal and non-verbal cognitive scores. The associations at single CpG sites were weak for all of the cognitive domains investigated. One region near DUSP22 on chromosome 6 was associated with non-verbal cognition in a model adjusted for maternal IQ. We conclude that there is little evidence to support the idea that cord blood DNA methylation at single CpGs can predict cognitive skills and further studies are needed to confirm regional differences.

scholarly journals Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity

2020 ◽  
Author(s):  
Gabi Socolovsky ◽  
Maoz Shamir
Keyword(s):  
Mean Field ◽  
Rhythmic Activity ◽  
Gamma Oscillations ◽  
Underlying Mechanism ◽  
Spike Timing ◽  
Gamma Band ◽  
Fine Tuning ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wide Range

Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in the limit of slow learning. We drew on this approximation to determine which types of STDP rules drive the system to exhibit gamma oscillations, and demonstrate how the parameters that characterize the plasticity rule govern the rhythmic activity. Finally, we propose a novel mechanism that can ensure the robustness of self-developing processes, in general and for rhythmogenesis in particular.

scholarly journals Polygenic Scores for Cognitive Abilities and their Association with Different Aspects of General Intelligence – a Deep Phenotyping Approach

2020 ◽  
Author(s):  
Erhan Genç ◽  
Caroline Schlüter ◽  
Christoph Fraenz ◽  
Larissa Arning ◽  
Huu Phuc Nguyen ◽  
...  
Keyword(s):  
Cognitive Abilities ◽  
Large Scale ◽  
Short Term Memory ◽  
Life Outcomes ◽  
Short Term ◽  
Polygenic Scores ◽  
Wide Range ◽  
Dna Variants ◽  
Trait Prediction ◽  
Deep Phenotyping

AbstractIntelligence is a highly polygenic trait and GWAS have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured. We used deep-phenotyping to investigate the molecular determinantes of individual differences in cognitive ability. We therefore studied the association between PGS of educational attainment (EA-PGS) and intelligence (IQ-PGS) with a wide range of intelligence facets in a sample of 320 healthy adults. EA-PGS and IQ-PGS had the highest incremental R2s for general (3.25%; 1.78%), verbal (2.55%; 2.39%) and numerical intelligence (2.79%; 1.54%) and the weakest for non-verbal intelligence (0.50%; 0.19%) and short-term memory (0.34%; 0.22%). These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

scholarly journals Neuronal pattern separation of motion-relevant input in LIP activity

2017 ◽  
Vol 117 (2) ◽  
pp. 738-755 ◽  
Author(s):  
Nareg Berberian ◽  
Amanda MacPherson ◽  
Eloïse Giraud ◽  
Lydia Richardson ◽  
J.-P. Thivierge
Keyword(s):  
Neuronal Activity ◽  
Neural Activity ◽  
Population Model ◽  
Visual Motion ◽  
Temporal Dynamics ◽  
Strong Predictor ◽  
Pattern Separation ◽  
Sensory Stimuli ◽  
Sensory Discrimination

In various regions of the brain, neurons discriminate sensory stimuli by decreasing the similarity between ambiguous input patterns. Here, we examine whether this process of pattern separation may drive the rapid discrimination of visual motion stimuli in the lateral intraparietal area (LIP). Starting with a simple mean-rate population model that captures neuronal activity in LIP, we show that overlapping input patterns can be reformatted dynamically to give rise to separated patterns of neuronal activity. The population model predicts that a key ingredient of pattern separation is the presence of heterogeneity in the response of individual units. Furthermore, the model proposes that pattern separation relies on heterogeneity in the temporal dynamics of neural activity and not merely in the mean firing rates of individual neurons over time. We confirm these predictions in recordings of macaque LIP neurons and show that the accuracy of pattern separation is a strong predictor of behavioral performance. Overall, results propose that LIP relies on neuronal pattern separation to facilitate decision-relevant discrimination of sensory stimuli. NEW & NOTEWORTHY A new hypothesis is proposed on the role of the lateral intraparietal (LIP) region of cortex during rapid decision making. This hypothesis suggests that LIP alters the representation of ambiguous inputs to reduce their overlap, thus improving sensory discrimination. A combination of computational modeling, theoretical analysis, and electrophysiological data shows that the pattern separation hypothesis links neural activity to behavior and offers novel predictions on the role of LIP during sensory discrimination.

Development of innovative monitoring technologies in the framework of InnovaMare Project

2021 ◽  
Author(s):  
Angelo Odetti ◽  
Federica Braga ◽  
Fabio Brunetti ◽  
Massimo Caccia ◽  
Simone Marini ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
Shallow Water ◽  
Adriatic Sea ◽  
Temporal Dynamics ◽  
Supervised Machine Learning ◽  
Detection Techniques ◽  
Cross Border ◽  
Wide Range ◽  
Air Cushion ◽  
The Cross

<p>The IT-HR InnovaMare project, led by the Croatian Chamber of Economy, puts together policy instruments and key players for development of innovative technologies for the sustainable development of the Adriatic Sea (https://www.italy-croatia.eu/web/innovamare). The project aims at enhancing the cross-border cooperation among research, public and private stakeholders through creation of a Digital Innovation Hub (DIH). The goal is to increase effectiveness of innovation in underwater robotics and sensors to achieve and maintain a healthy and productive Adriatic Sea, as one of the crucial and strategic societal challenges existing at the cross-border level. Within InnovaMare, CNR ISMAR and INM institutes and OGS, in cooperation with the University of Zagreb and other project partners, contribute to developing a solution to access and monitor extremely shallow water by means of portable, modular, reconfigurable and highly maneuverable robotic vehicles. The identified vehicle is SWAMP, an innovative highly modular catamaran ASV recently developed by CNR-INM. SWAMP is characterised by small size, low draft, new materials, azimuth propulsion system for shallow waters and modular WiFi-based hardware&software architecture. Two SWAMP vehicles will be enhanced with a series of kits, tools and sensors to perform a series of strategic actions in the environmental monitoring of the Venice Lagoon: <br>i) An air-cushion-system-kit will be designed and developed. The vehicle will become a side-wall air-cushion-vehicle with reduction of drag and increase in speed. This will also increase the payload with a reduction of draft. <br>ii) An intelligent winch kit with a communication cable for the management of underwater sensors and tools.<br>iii) A GPS-RTK kit for highly accurate positioning in the range of centimeters.<br>iv) An Autonomous programmable device for image acquisition and processing based on the Guard1 camera. This camera acquires images content and, by means of a supervised machine learning approach, recognises/classifies features such as fish, zooplankton, seabed, infrastructures. The system is conceived for autonomous monitoring activities extended in time in fixed or mobile platforms.<br>v) A Multibeam Echo-sounder (MBES) coupled with an IMU (for pitch-roll compensation). MBES data can be used, also coupled with Cameras Imagery, through image-detection techniques for reconstruction and comprehensive knowledge of underwater environment and infrastructures. Possible analyses in coastal areas are: seabed mapping also for cultural heritage, offshore structures and resources and monitoring of biodiversity, hydrocarbon, marine litter, pollution.<br>vi) An underwater Radiometer for multiple analysis: temporal dynamics of optical properties of water; temporal dynamics of water turbidity from water reflectance; submerged vegetation and water depth mapping in optically shallow water; produce reference data for validation of satellite data.<br>vii) Automatic Nutrient Analyzer for real-time nutrient monitoring. This sensor measures nitrate with high accuracy over a wide range of environmental conditions (including extremely turbid and high CDOM conditions), from blue-ocean nitraclines to storm runoff in rivers and streams. <br>The final result of this pilot action is the creation of an innovative prototype platform for sea environmental monitoring. This will be validated through the analysis of results and draw up of guidelines for the improvement of underwater conditions.</p>

scholarly journals Transcranial alternating current stimulation entrains single-neuron activity in the primate brain

2019 ◽  
Vol 116 (12) ◽  
pp. 5747-5755 ◽  
Author(s):  
Matthew R. Krause ◽  
Pedro G. Vieira ◽  
Bennett A. Csorba ◽  
Praveen K. Pilly ◽  
Christopher C. Pack
Keyword(s):  
Human Brain ◽  
Electric Fields ◽  
Critical Role ◽  
Spike Timing ◽  
Model Systems ◽  
Neuron Activity ◽  
Transcranial Electrical Stimulation ◽  
Noninvasive Technique ◽  
Early Results ◽  
Wide Range

Spike timing is thought to play a critical role in neural computation and communication. Methods for adjusting spike timing are therefore of great interest to researchers and clinicians alike. Transcranial electrical stimulation (tES) is a noninvasive technique that uses weak electric fields to manipulate brain activity. Early results have suggested that this technique can improve subjects’ behavioral performance on a wide range of tasks and ameliorate some clinical conditions. Nevertheless, considerable skepticism remains about its efficacy, especially because the electric fields reaching the brain during tES are small, whereas the likelihood of indirect effects is large. Our understanding of its effects in humans is largely based on extrapolations from simple model systems and indirect measures of neural activity. As a result, fundamental questions remain about whether and how tES can influence neuronal activity in the human brain. Here, we demonstrate that tES, as typically applied to humans, affects the firing patterns of individual neurons in alert nonhuman primates, which are the best available animal model for the human brain. Specifically, tES consistently influences the timing, but not the rate, of spiking activity within the targeted brain region. Such effects are frequency- and location-specific and can reach deep brain structures; control experiments show that they cannot be explained by sensory stimulation or other indirect influences. These data thus provide a strong mechanistic rationale for the use of tES in humans and will help guide the development of future tES applications.

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