Real-time neuron detection and neural activity extraction are critical components of real-time neural decoding. In this paper, we propose a novel real-time neuron detection and activity extraction system using a dataflow framework to provide real-time performance and adaptability to new algorithms and hardware platforms. The proposed system was evaluated on simulated calcium imaging data, calcium imaging data with manual annotation, and calcium imaging data of the anterior lateral motor cortex. We found that the proposed system accurately detected neurons and extracted neural activities in real time without any requirement for expensive, cumbersome, or special-purpose computing hardware. We expect that the system will enable cost-effective, real-time calcium imaging-based neural decoding, leading to precise neuromodulation.

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In dataflow representations for signal processing systems, applications are represented as directed graphs in which vertices represent computations and edges correspond to buffers that store data as it passes among computations. The edges in the dataflow graph are single-input, single-output components that manage data transmission in a first-in, first-out (FIFO) fashion. In this paper, we formulate the vertices and edges into concepts called "active blocks" and "passive blocks", respectively in the graph representation. Computation in the dataflow graph is represented as "active blocks", while the concept of dataflow buffers is represented as "passive blocks". Like dataflow edges, passive blocks are used to store data during the intervals between its production and consumption by actors. However, passive blocks can have multiple inputs and multiple outputs, and can incorporate operations on and rearrangements of the stored data subject to certain constraints. We define a form of flowgraph representation that is based on replacing dataflow edges with the proposed concept of passive blocks. We present a structured design methodology for utilizing this new form of signal processing flowgraph, and demonstrate its application to improving memory management efficiency, and execution time performance.