Srinjoy Mitra

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Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware(More)
We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a network of integrate-and-fire neurons. This biologically inspired synapse is highly effective in learning to classify complex stimuli in semisupervised fashion. The circuits presented are designed in subthreshold CMOS consuming extremely low power. The pulsebased(More)
We propose a compact, low power VLSI network of spiking neurons which can learn to classify complex patterns of mean firing rates on–line and in real–time. The network of integrate-and-fire neurons is connected by bistable synapses that can change their weight using a local spike–based plasticity mechanism. Learning is supervised by a teacher which provides(More)
A low-power analog signal processing IC is presented for the low-power heart rhythm analysis. The ASIC features 3 identical, but independent intra-ECG readout channels each equipping an analog QRS feature extractor for low-power consumption and fast diagnosis of the fatal case. A 16-level digitized sine-wave synthesizer together with a synchronous readout(More)
Multi-Electrode Arrays (MEA) have been widely used in neuroscience experiments. However, the reduction of their wireless transmission power consumption remains a major challenge. To resolve this challenge, an efficient on-chip signal compression method is essential. In this paper, we first introduce a signal-dependent Compressed Sensing (CS) approach that(More)
Current-mode log-domain CMOS filters have favorable properties, such as wide dynamic range at low supply voltage, compactness, linearity and low power consumption. These properties are becoming increasingly important for biomedical applications that require extremely low-power dissipation and neuromorphic circuits that attempt to reproduce the biophysics of(More)