Learning alters theta-nested gamma oscillations in inferotemporal cortex

How coupled brain rhythms influence cortical information processing to support learning is unresolved. Local field potential and neuronal activity recordings from 64- electrode arrays in sheep inferotemporal cortex showed that visual discrimination learning increased the amplitude of theta oscillations during stimulus presentation. Coupling between theta and gamma oscillations, the theta/gamma ratio and the regularity of theta phase were also increased, but not neuronal firing rates. A neural network model with fast and slow inhibitory interneurons was developed which generated theta nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity similar learning-evoked changes could be produced. The model revealed that altered theta nested gamma could potentiate downstream neuron responses by temporal desynchronization of excitatory neuron output independent of changes in overall firing frequency. This learning-associated desynchronization was also exhibited by inferotemporal cortex neurons. Changes in theta nested gamma may therefore facilitate learning-associated potentiation by temporal modulation of neuronal firing

Embedded Electronics In Medical Applications

Proceedings of"Conference on Recent Advances in Biomaterials Dec 17-18 '10"Held at Saveetha School of Engineering, Saveetha University, Thandalam, Chennai-602 105, Tamilnadu, IndiaTheme 10Embedded Electronics In Medical Application

Sex differences in variability across timescales in BALB/c mice.

BackgroundFemales are markedly underinvestigated in the biological and behavioral sciences due to the presumption that cyclic hormonal changes across the ovulatory cycle introduce excess variability to measures of interest in comparison to males. However, recent analyses indicate that male and female mice and rats exhibit comparable variability across numerous physiological and behavioral measures, even when the stage of the estrous cycle is not considered. Hormonal changes across the ovulatory cycle likely contribute cyclic, intra-individual variability in females, but the source(s) of male variability has, to our knowledge, not been investigated. It is unclear whether male variability, like that of females, is temporally structured and, therefore, quantifiable and predictable. Finally, whether males and females exhibit variability on similar time scales has not been explored.MethodsThese questions were addressed by collecting chronic, high temporal resolution locomotor activity (LA) and core body temperature (CBT) data from male and female BALB/c mice.ResultsContrary to expectation, males are more variable than females over the course of the day (diel variability) and exhibit higher intra-individual daily range than females in both LA and CBT. Between mice of a given sex, variability is comparable for LA but the inter-individual daily range in CBT is greater for males. To identify potential rhythmic processes contributing to these sex differences, we employed wavelet transformations across a range of periodicities (1-39 h).ConclusionsAlthough variability in circadian power is comparable between the sexes for both LA and CBT, infradian variability is greater in females and ultradian variability is greater in males. Thus, exclusion of female mice from studies because of estrous cycle variability may increase variance in investigations where only male measures are collected over a span of several hours and limit generalization of findings from males to females

The Dynamic Brain in Action: Cortical Oscillations and Coordination Dynamics

Cortical oscillations are electrical activities with rhythmic and/or repetitive nature generated spontaneously and in response to stimuli. Study of cortical oscillations has become an area of converging interests since the last two decades and has deepened our understanding of its physiological basis across different behavioral states. Experimental and modeling work has taught us that there is a wide diversity of cellular and circuit mechanisms underlying the generation of cortical rhythms. A wildly diverse set of functions has pertained to synchronous oscillations but their significance in cognition should be better appraised in the more general framework of correlation between spike times of neurons. Oscillations are the core mechanism in adjusting neuronal interactions and shaping temporal coordination of neural activity. In the first part of this thesis, we review essential feature of cortical oscillations in membrane potentials and local field potentials recorded from turtle ex vivo preparation. Then we develop a simple computational model that reproduces the observed features. This modeling investigation suggests a plausible underlying mechanism for rhythmogenesis through cellular and circuit properties. The second part of the thesis is about temporal coordination dynamics quantified by signal and noise correlations. Here, again, we present a computational model to show how temporal coordination and synchronous oscillations can be sewn together. More importantly, what biophysical ingrediants are necessary for a network to reproduce the observed coordination dynamics

Power efficient dataflow design for a heterogeneous smart camera architecture

Visual attention modelling characterises the scene to segment regions of visual interest and is increasingly being used as a pre-processing step in many computer vision applications including surveillance and security. Smart camera architectures are an emerging technology and a foundation of security and safety frameworks in modern vision systems. In this paper, we present a dataflow design of a visual saliency based camera architecture targeting a heterogeneous CPU+FPGA platform to propose a smart camera network infrastructure. The proposed design flow encompasses image processing algorithm implementation, hardware & software integration and network connectivity through a unified model. By leveraging the properties of the dataflow paradigm, we iteratively refine the algorithm specification into a deployable solution, addressing distinct requirements at each design stage: from algorithm accuracy to hardware-software interactions, real-time execution and power consumption. Our design achieved real-time run time performance and the power consumption of the optimised asynchronous design is reported at only 0.25 Watt. The resource usages on a Xilinx Zynq platform remains significantly low

Investigating the effects of an on-chip pre-classifier on wireless ECG monitoring

In past years, heart disease has been the leading cause of death in most developed countries. Timely detection of a heart condition is necessary in order to prevent life threatening situations. Even when the problem is not a heart condition, the activity of the heart can supply vital information, which makes its monitoring extremely important. A new approach to patient monitoring was taken recently by introducing wireless sensor networks into medical care. The capability of monitoring multiple patients at once makes such a system ideal for pre-hospital and in-hospital emergency care. The main problems associated with wireless sensor networks are power consumption and scaling. The power consumption is a problem due to the need for increased mobility of such a system, while scaling is of concern because a large number of nodes is desired in order to monitor more patients. This thesis addresses the power and bandwidth problems associated with monitoring patients using wireless networks by introducing another level of signal processing at each node. The goal is to design a digital circuit that would detect any abnormality in the ECG signal and enable the data transmission only if such has occurred. Reducing the amount of data being transmitted reduces the necessary bandwidth for each node and with the introduction of the proposed chip, the power consumption of each node is affected as well

Low‐power global navigation satellite system‐enabled wireless sensor network for acoustic emission localisation in aerospace components

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Time course of information processing in visual and haptic object classification

Peer reviewedPublisher PD