Reactivating Memories for Consolidation

The consolidation of memory is thought to occur via a hippocampal-neocortical dialog involving reactivation of memory patterns in the hippocampus during sharp-wave ripples. In this issue of Neuron, Nakashiba et al. demonstrate that CA3 output is required for consolidation of contextual fear memory. They also show that lack of CA3 output results in a decrease in ripple-related reactivation, providing additional evidence for a role of ripple-related reactivation in the consolidation process

Biological Potential of Tribulus terrestris

Plants have a significant role in preserving human health and improving quality of life. gokshura (Tribulus terrestris Linn.) one of such plants, is mentioned in Ayurvedic texts for various therapeutic properties like balya(strengthening),  brimhana (nutritive), rasayana(rejuvenator), mootrala(diuretic), shothahara(anti-inflammatory), vajikarana (aphrodisiac) etc. and useful in the management of mutrakrichhra (dysurea), ashmari (renal calculi) etc. It is a perennial plant, grown predominantly in India and Africa. Its extract contains alkaloids, saponins, resins, flavanoids and nitrates. As its therapeutic values, a review has been done to gather information on different aspects of gokshura. Further Ayurvedic references, the present paper also emphasizes on recent researches carried out on this plant for its pharmacological evaluation. Keywords: Tribulus terrestris, Diuretic, Pharmacolog

Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populations

Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as neural recording technologies have advanced to produce simultaneous spiking data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analyses; however, they typically require massive training sets with balanced data, along with accurate labels to fit well. Additionally, model assessment and interpretation are often more challenging for ML than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to demonstrate this framework, we apply these methods to data from a population of neurons recorded from rat hippocampus to characterize the distribution of spatial receptive fields in this region

Psychometric Curve and Behavioral Strategies for Whisker-Based Texture Discrimination in Rats

The rodent whisker system is a major model for understanding neural mechanisms for tactile sensation of surface texture (roughness). Rats discriminate surface texture using the whiskers, and several theories exist for how texture information is physically sensed by the long, moveable macrovibrissae and encoded in spiking of neurons in somatosensory cortex. However, evaluating these theories requires a psychometric curve for texture discrimination, which is lacking. Here we trained rats to discriminate rough vs. fine sandpapers and grooved vs. smooth surfaces. Rats intermixed trials at macrovibrissa contact distance (nose >2 mm from surface) with trials at shorter distance (nose <2 mm from surface). Macrovibrissae were required for distant contact trials, while microvibrissae and non-whisker tactile cues were used for short distance trials. A psychometric curve was measured for macrovibrissa-based sandpaper texture discrimination. Rats discriminated rough P150 from smoother P180, P280, and P400 sandpaper (100, 82, 52, and 35 µm mean grit size, respectively). Use of olfactory, visual, and auditory cues was ruled out. This is the highest reported resolution for rodent texture discrimination, and constrains models of neural coding of texture information

Neural coding during active whisker sensation

A major goal in studies of sensory coding is to understand how neural activity represents stimuli in the external world. Rats actively palpate objects with their whiskers to discriminate tactile features of their environment. Although neural responses have been characterized in the whisker system in anesthetized animals for artificially applied whisker stimuli, circuit mechanisms underlying neural response properties and neural coding of sensory information in behaving animals are not well understood. Precise timing of spikes is thought to be important for many aspects of neural coding in the whisker system. Chapter 2 of this thesis elucidates the cellular mechanisms underlying precise spike timing in primary somatosensory cortex (S1). Feed-forward thalamocortical inhibition is shown to dynamically regulate the integration time window of cortical neurons, thus enforcing temporal fidelity of spiking. How surface properties are encoded by neural activity in awake and active animals is unknown. In Chapter 3, we describe an experiment to identify the fundamental features of whisker motion that are represented in S1 during natural surface exploration. We simultaneously measured whisker motion and spiking responses of neurons in S1 in awake, behaving rats whisking across textured surfaces. We show that transient slip-stick events are encoded by a majority of S1 neurons with precisely timed spikes, leading to an increase in firing rate. The timing and amplitude of these events is encoded by S1 neurons. Slip-stick responses occurred with low probability, but led to a transient increase in synchronous activity of neurons, resulting in a sparse probabilistic population code. A simulation of the experimental data showed that slip-stick events can be efficiently decoded by synchronous spiking activity on a ̃20 ms time scale across small (̃100 neuron) populations within a single S1 cortical column. These results demonstrate that slip-stick events are primary stimulus features encoded in S1 by a sparse ensemble representation during active surface whisking. Synchronous activity of a small subset of neurons efficiently represents slip-stick events, resulting in a population temporal code for surface propertie

Implementation Of Re-encryption Based Security Meachinsm to Authenticate Shared Access in Cloud Computing

Cloud&nbsp;&nbsp; computing&nbsp;&nbsp; provides&nbsp;&nbsp; facilities&nbsp;&nbsp; of&nbsp;&nbsp; shared computer processing resources and data to computers and other device on demand. System environment developed by using three key&nbsp;&nbsp; entities&nbsp;&nbsp; trusted&nbsp; third&nbsp;&nbsp; party,&nbsp;&nbsp; data&nbsp;&nbsp; owner&nbsp; and&nbsp;&nbsp; user.&nbsp;&nbsp; The concept&nbsp;&nbsp;&nbsp;&nbsp; of&nbsp;&nbsp;&nbsp;&nbsp; shared&nbsp;&nbsp;&nbsp;&nbsp; authority&nbsp;&nbsp;&nbsp;&nbsp; based&nbsp;&nbsp;&nbsp; privacy&nbsp;&nbsp;&nbsp;&nbsp; preserving authentication&nbsp; protocol&nbsp; i.e.,&nbsp; SAPA&nbsp; used&nbsp; to&nbsp; develop&nbsp; system&nbsp; to perform&nbsp; shared&nbsp; access&nbsp; in&nbsp; multiple&nbsp; user.&nbsp; Security&nbsp; and&nbsp; privacy issue as well as shared access authority achieved by using access request&nbsp; matching&nbsp; mechanism&nbsp; e.g.&nbsp; authentication,&nbsp; user&nbsp; privacy, user can only access its own data fields. The multiple users want to&nbsp; share&nbsp; data&nbsp; so&nbsp; that&nbsp; purpose&nbsp; re-encryption&nbsp; is&nbsp; used&nbsp; to&nbsp; provide high&nbsp; security&nbsp; for&nbsp; user&nbsp; private&nbsp; data.&nbsp; Privacy&nbsp; preserving&nbsp; data access authority sharing is attractive for multi user collaborative cloud applications

Hippocampal-Prefrontal Reactivation during Learning Is Stronger in Awake Compared with Sleep States

Hippocampal sharp-wave ripple (SWR) events occur during both behavior (awake SWRs) and slow-wave sleep (sleep SWRs). Awake and sleep SWRs both contribute to spatial learning and memory, thought to be mediated by the coordinated reactivation of behavioral experiences in hippocampal-cortical circuits seen during SWRs. Current hypotheses suggest that reactivation contributes to memory consolidation processes, but whether awake and sleep reactivation are suited to play similar or different roles remains unclear. Here we addressed that issue by examining the structure of hippocampal (area CA1) and prefrontal (PFC) activity recorded across behavior and sleep stages in male rats learning a spatial alternation task. We found a striking state difference: prefrontal modulation during awake and sleep SWRs was surprisingly distinct, with differing patterns of excitation and inhibition. CA1-PFC synchronization was stronger during awake SWRs, and spatial reactivation, measured using both pairwise and ensemble measures, was more structured for awake SWRs compared with post-task sleep SWRs. Stronger awake reactivation was observed despite the absence of coordination between network oscillations, namely hippocampal SWRs and cortical delta and spindle oscillations, which is prevalent during sleep. Finally, awake CA1-PFC reactivation was enhanced most prominently during initial learning in a novel environment, suggesting a key role in early learning. Our results demonstrate significant differences in awake and sleep reactivation in the hippocampal-prefrontal network. These findings suggest that awake SWRs support accurate memory storage and memory-guided behavior, whereas sleep SWR reactivation is better suited to support integration of memories across experiences during consolidation.SIGNIFICANCE STATEMENT Hippocampal sharp-wave ripples (SWRs) occur both in the awake state during behavior and in the sleep state after behavior. Awake and sleep SWRs are associated with memory reactivation and are important for learning, but their specific memory functions remain unclear. Here, we found profound differences in hippocampal-cortical reactivation during awake and sleep SWRs, with key implications for their roles in memory. Awake reactivation is a higher-fidelity representation of behavioral experiences, and is enhanced during early learning, without requiring coordination of network oscillations that is seen during sleep. Our findings suggest that awake reactivation is ideally suited to support initial memory formation, retrieval and planning, whereas sleep reactivation may play a broader role in integrating memories across experiences during consolidation

Hippocampal-Prefrontal Reactivation during Learning Is Stronger in Awake Compared with Sleep States

Hippocampal sharp-wave ripple (SWR) events occur during both behavior (awake SWRs) and slow-wave sleep (sleep SWRs). Awake and sleep SWRs both contribute to spatial learning and memory, thought to be mediated by the coordinated reactivation of behavioral experiences in hippocampal-cortical circuits seen during SWRs. Current hypotheses suggest that reactivation contributes to memory consolidation processes, but whether awake and sleep reactivation are suited to play similar or different roles remains unclear. Here we addressed that issue by examining the structure of hippocampal (area CA1) and prefrontal (PFC) activity recorded across behavior and sleep stages in male rats learning a spatial alternation task. We found a striking state difference: prefrontal modulation during awake and sleep SWRs was surprisingly distinct, with differing patterns of excitation and inhibition. CA1-PFC synchronization was stronger during awake SWRs, and spatial reactivation, measured using both pairwise and ensemble measures, was more structured for awake SWRs compared with post-task sleep SWRs. Stronger awake reactivation was observed despite the absence of coordination between network oscillations, namely hippocampal SWRs and cortical delta and spindle oscillations, which is prevalent during sleep. Finally, awake CA1-PFC reactivation was enhanced most prominently during initial learning in a novel environment, suggesting a key role in early learning. Our results demonstrate significant differences in awake and sleep reactivation in the hippocampal-prefrontal network. These findings suggest that awake SWRs support accurate memory storage and memory-guided behavior, whereas sleep SWR reactivation is better suited to support integration of memories across experiences during consolidation.SIGNIFICANCE STATEMENT Hippocampal sharp-wave ripples (SWRs) occur both in the awake state during behavior and in the sleep state after behavior. Awake and sleep SWRs are associated with memory reactivation and are important for learning, but their specific memory functions remain unclear. Here, we found profound differences in hippocampal-cortical reactivation during awake and sleep SWRs, with key implications for their roles in memory. Awake reactivation is a higher-fidelity representation of behavioral experiences, and is enhanced during early learning, without requiring coordination of network oscillations that is seen during sleep. Our findings suggest that awake reactivation is ideally suited to support initial memory formation, retrieval and planning, whereas sleep reactivation may play a broader role in integrating memories across experiences during consolidation