To give paralyzed people hope for a normal life, Brain Machine Interfaces (BMI) record signals from the motor cortex and a decoder translates these 'thoughts' to action. A high accuracy decoder is needed for a seamless user experience. At the same time it needs to be compact and low-power to support its integration in an implant to enable the compression required in wireless implantable BMIs. Hence, a model with a good trade-off between accuracy and resource requirement is desirable. In the IEEE BioCAS 2024 conference, we organized the first grand challenge on neural decoding for motor control. The evaluations were performed using the recently developed Neurobench software suite for benchmarking neuromorphic systems. There were two tracks -one preferring solutions with highest accuracy while the other gave weightage to the tradeoff between accuracy and implementation complexity. Out of the 10 teams registered for this event, the top 3 teams are invited to present their works in the IEEE BioCAS 2024.