Our vision is as yet unsurpassed by machines because of the sophisticated representations of objects in our brains. This representation is vastly different from a pixel-based representation used in machine storages. It is this sophisticated representation that enables us to perceive two faces as very different, i.e, they are far apart in the “perceptual space”, even though they are close to each other in their pixel-based representations. Neuroscientists have proposed distances between responses of neurons to the images (as measured in macaque monkeys) as a quantification of the “perceptual distance” between the images. Let us call these neuronal dissimilarity indices of perceptual distances. They have also proposed behavioural experiments to quantify these perceptual distances. Human subjects are asked to identify, as quickly as possible, an oddball image embedded among multiple distractor images. The reciprocal of the search times for identifying the oddball is taken as a measure of perceptual distance between the oddball and the distractor. Let us call such estimates as behavioural dissimilarity indices. In this thesis, we describe a decision-theoretic model for visual search that suggests a connection between these two notions of perceptual distances. In the first part of the thesis, we model visual search as an active sequential hypothesis testing problem. Our analysis suggests an appropriate neuronal dissimilarity index which correlates strongly with the reciprocal of search times. We also consider a number of alternative possibilities such as relative entropy (Kullback-Leibler divergence), the Chernoff entropy and the L1-distance associated with the neuronal firing rate profiles. We then come up with a means to rank the various neuronal dissimilarity indices based on how well they explain the behavioural observations. Our proposed dissimilarity index does better than the other three, followed by relative entropy, then Chernoff entropy and then L1 distance. In the second part of the thesis, we consider a scenario where the subject has to find an oddball image, but without any prior knowledge of the oddball and distractor images. Equivalently, in the neuronal space, the task for the decision maker is to find the image that elicits firing rates different from the others. Here, the decision maker has to “learn” the underlying statistics and then make a decision on the oddball. We model this scenario as one of detecting an odd Poisson point process having a rate different from the common rate of the others. The revised model suggests a new neuronal dissimilarity index. The new dissimilarity index is also strongly correlated with the behavioural data. However, the new dissimilarity index performs worse than the dissimilarity index proposed in the first part on existing behavioural data. The degradation in performance may be attributed to the experimental setup used for the current behavioural tasks, where search tasks associated with a given image pair were sequenced one after another, thereby possibly cueing the subject about the upcoming image pair, and thus violating the assumption of this part on the lack of prior knowledge of the image pairs to the decision maker. In conclusion, the thesis provides a framework for connecting the perceptual distances in the neuronal and the behavioural spaces. Our framework can possibly be used to analyze the connection between the neuronal space and the behavioural space for various other behavioural tasks.

Directing differential cellular response by manipulating the physical characteristics of the material is regarded as a key challenge in biomaterial implant design and tissue engineering. In developing various biomaterials, the influence of substrate properties, like surface topography, stiffness and wettability on the cell functionality has been investigated widely. However, such study to probe into the influence of substrate conductivity on cell fate processes is rather limited. The need for such an understanding is based on the fact that specific tissues in the body are electrically active in nature, such as in brain, heart and skeletal muscle. These tissues make use of electrical conductivity as an effective cue for tissue homeostasis, development, regeneration and so on. Moreover, understanding the importance of underlying conductivity in basic biological processes is essential in developing electrically conductive biomaterials with the ability to simulate normal electrophysiology of the body by interfacing with bioelectric fields in cells and tissues. Electrical stimulation and charge conduction can regulate numerous intracellular signalling pathways, can interact with cytoskeleton proteins to modulate the morphology, increase protein synthesis and on the more can favor the ECM protein conformational changes. On these grounds, the present dissertation illustrates that persistent electrical activation influences the multipotency of hMSCs and acts like a promoter towards selective differentiation of hMSCs into neural/cardiomyogenic or osteogenic lineage. Besides, continual exposure to electric field stimulated conducting culture environments lead to growth arrest while enhancing differentiation. In total, this dissertation suggests the dominant role of conductivity in inducing my oblast differentiation and hMSc lineage commitment that involves EF stimulated in vitro culture conditions. Also, a knowledge base with qualitative and quantitative understanding of stem cells and their response to substrate physical properties and external field effect was developed through this comprehensive study. Such an improved understanding of the ability of hMSCs in sensing electrical conductivity may lead to the development of culture additives/conditions that better induce directed stem cell differentiation.

To address the challenge of accurate, low cost and robust biosensors for diabetes management and early detection of diabetes complications, we have developed novel, robust sensing chemistry (or receptors) for electrochemical POC biosensors. The biosensors have been developed for the bio-markers associated with diabetes management such as glycated haemoglobin (HbA1c), glycated albumin, glucose, biomarkers associated with diabetes complications such as microalbuminuria, urine creatinine and albumin-to-creatinine ratio (ACR) and biomarkers associated with anaemia and malnutrition conditions such as haemoglobin and serum albumin. For haemoglobin detection, a new POC bio sensing technique has been developed based on Aza-heterocyclic chemicals. The repeatability and accuracy of the biosensor have been tested on real pathology samples. The glycated form of haemoglobin, called glycated haemoglobin or HbA1c, is the gold standard test in diabetes management as it gives the 90-days average blood glucose value. We demonstrate a simple method for electrochemical detection of HbA1c by combining bosonic affinity principle along with aza-heterocyclic receptors. The technique has been verified on the real clinical patient samples. Albumin is the most abundant protein in the human blood. Human serum albumin (HSA) is either alone or an associative biomarker in several chronic diseases like necrosis, nephrosis, hepatitis, malnutrition, arthritis, immune disorders, cancer, diabetes and in some severe infections. In pathology laboratories, the serum albumin is usually tested on serum samples and not in whole blood samples. Since albumin is not a metalloproteinase, it is very difficult to develop electrochemical POC biosensor. We have developed a novel technique for the electrochemical detection of serum albumin in whole blood samples, by exploiting its binding property with redox active copper salts. The accuracy of technique has been verified on both real human blood plasma as well as whole blood samples. Glycated albumin, which is the glycated form of serum albumin, is emerging as a novel biomarker for diabetes management, as it gives the average blood glucose value of 15-20 days. It is also extremely useful in chronic kidney disease patients and patients with hemoglobinopathies where HbA1c can give the erroneous results. By combining the copper chemistry along with bosonic affinity principle, we present the first ever demonstration of glycated albumin sensing. Instant blood glucose monitoring is an integral part of diabetes management. Most of the glucometers available in the market are based on glucose oxidase enzyme. We have demonstrated a low cost non-enzymatic electrochemical technique for blood glucose detection using alkaline methylene blue chemistry. The accuracy of the technique has been verified on real human blood plasma samples. Glucometer is one of the most easily available POC biosensor and a useful tool for diabetes population. India has second largest diabetes population in the world. To analyse the accuracy of the POC glucometers which are available in Indian market, a comprehensive study was conducted. The results were compared with clinical accuracy guidelines using exhaustive statistical analysis techniques. The shortcomings of the commercial glucometers are elucidated, regarding different international standards. Diabetic nephropathy is one of the major diabetes complications and is the primary cause of chronic kidney disease (CKD). The presence of albumin in urine is a well-established biomarker for the early detection of diabetic nephropathy. We have developed a technique for electrochemical detection of microalbuminuria for point of care applications by exploring the binding property of human albumin with electrochemically active molecules like copper and hemin. Methylene blue mediated sensing technique has also been proposed. Urine Albumin-to creatinine ratio (ACR) is another variant of the microalbumuria test that can be done any time and does not suffer from the dilution factor of urine. Iron binding property of creatinine is exploited to develop creatinine biosensor, thus enabling POC ACR tests.