In this chapter we discuss the implications of our research in the wider context of current models of brain function, endeavoring to understand the consequences of score-dependence and improvisation in terms of the ‘predicting brain’, the dual-stream model of perception and action, the procedural-declarative model of learning and memory, ideomotor learning and sensorimotor mapping, and the implicit acquisition of hierarchical music syntax.
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Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.
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The realization of one’s musical ideas at the keyboard is dependent on the ability to transform sound into movement, a process called audiomotor transformation. Using fMRI, we investigated cerebral activations while classically‐trained improvising and non‐improvising musicians imagined playing along with recordings of familiar and unfamiliar music excerpts. We hypothesized that audiomotor transformation would be associated with the recruitment of dedicated cerebral networks, facilitating aurally‐cued performance. Results indicate that while all classically‐trained musicians engage a left‐hemisphere network involved in motor skill and action recognition, only improvising musicians additionally recruit a right dorsal frontoparietal network dedicated to spatially‐driven motor control. Mobilization of this network, which plays a crucial role in the real‐time transformation of imagined or perceived music into goal‐directed action, may be held responsible not only for the stronger activation of auditory cortex we observed in improvising musicians in response to the aural perception of music, but also for the superior ability to play ‘by ear’ which they demonstrated in a follow‐up study. The results of this study suggest that the practice of improvisation promotes the implicit acquisition of hierarchical music syntax which is then recruited in top‐down manner via the dorsal stream during music performance.
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