Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current

It is generally assumed that nerve cells optimize their performance to reflect the statistics of their input. Electronic circuit analogs of neurons require similar methods of self-optimization for stable and autonomous operation. We here describe and demonstrate a biologically plausible adaptive algorithm that enables a neuron to adapt the current threshold and the slope (or gain) of its current-frequency relationship to match the mean (or dc offset) and variance (or dynamic range or contrast) of the time-varying somatic input current. The adaptation algorithm estimates the somatic current signal from the spike train by way of the intracellular somatic calcium concentration, thereby continuously adjusting the neuronś firing dynamics. This principle is shown to work in an analog VLSI-designed silicon neuron

Multimodal Imaging of Alzheimer Pathophysiology in the Brain's Default Mode Network

The spatial correlations between the brain's default mode network (DMN) and the brain regions known to develop pathophysiology in Alzheimer's disease (AD) have recently attracted much attention. In this paper, we compare results of different functional and structural imaging modalities, including MRI and PET, and highlight different patterns of anomalies observed within the DMN. Multitracer PET imaging in subjects with and without dementia has demonstrated that [C-11]PIB- and [F-18]FDDNP-binding patterns in patients with AD overlap within nodes of the brain's default network including the prefrontal, lateral parietal, lateral temporal, and posterior cingulate cortices, with the exception of the medial temporal cortex (especially, the hippocampus) where significant discrepancy between increased [F-18]FDDNP binding and negligible [C-11]PIB-binding was observed. [F-18]FDDNP binding in the medial temporal cortex—a key constituent of the DMN—coincides with both the presence of amyloid and tau pathology, and also with cortical areas with maximal atrophy as demonstrated by T1-weighted MR imaging of AD patients

Resting-State Glucose Metabolism Level Is Associated with the Regional Pattern of Amyloid Pathology in Alzheimer's Disease

It has been suggested that glucose metabolism within the brain's default network is directly associated with—and may even cause—the amyloid pathology of Alzheimer's disease (AD). Here we performed 2-[18F]fluoro-2-deoxy-D-glucose (FDG) and [11C]-labeled Pittsburgh Compound B (PIB) positron emission tomography (PET) on cognitively normal elderly subjects and on AD patients and conducted quantitative regional analysis of FDG- and PIB-PET images using an automated region of interest technique. We confirmed that resting glucose metabolism within the posterior components of the brain's default network is high in normal elderly subjects and low in AD patients, which is partially in agreement with the regional pattern of PIB uptake within the default network of AD patients. However, in several regions outside the default network, glucose metabolism was high in normal elderly subjects but was not depressed in AD patients, who exhibited significantly increased PIB uptakes in these regions. In contrast, the level of resting glucose metabolism in the default network and in regions outside the default network in normal elderly subjects was significantly correlated with the level of regional PIB uptake in AD patients. These results are discussed with experimental evidence suggesting that beta amyloid production and amyloid precursor protein regulation are dependent on neuronal activity

Protocol for calcium imaging of dorsal and ventral CA1 neurons in head-fixed mice

Summary: In contrast to other techniques utilized in physiological studies, calcium imaging can visualize target neurons located deep in the brain. Here, we present a protocol for one-photon calcium imaging of dorsal and ventral CA1 neurons in head-fixed mice. We describe procedures for injecting GCaMP6f virus, implanting a gradient-index (GRIN) lens, and installing a baseplate for Inscopix microscope mounting.For complete details on the use and execution of this protocol, please refer to Yun et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics

Simulation of Spatiotemporal Variations in Cotton Lint Yield in the Texas High Plains

This study aimed to simulate the spatiotemporal variation in cotton (Gossypium hirsutum L.) growth and lint yield using a remote sensing-integrated crop model (RSCM) for cotton. The developed modeling scheme incorporated proximal sensing data and satellite imagery. We formulated this model and evaluated its accuracy using field datasets obtained in Lamesa in 1999, Halfway in 2002 and 2004, and Lubbock in 2003–2005 in the Texas High Plains in the USA. We found that RSCM cotton could reproduce the cotton leaf area index and lint yield across different locations and irrigation systems with a statistically significant degree of accuracy. RSCM cotton was also used to simulate cotton lint yield for the field circles in Halfway. The RSCM system could accurately reproduce the spatiotemporal variations in cotton lint yield when integrated with satellite images. From the results of this study, we predict that the proposed crop-modeling approach will be applicable for the practical monitoring of cotton growth and productivity by farmers. Furthermore, a user can operate the modeling system with minimal input data, owing to the integration of proximal and remote sensing information

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Neurocomputing 38}40 (2001) 1557}1566 Reading auditory discrimination behaviour of freely moving rats from hippocampal EE