Controllable audio synthesis is a core element of creative sound design. Recent advancements in AI have made high-fidelity neural audio synthesis achievable. However, the high temporal resolution of audio and our perceptual sensitivity to small irregularities in waveforms make synthesizing at high sampling rates a complex and computationally intensive task, prohibiting real-time, controllable synthesis within many approaches. In this work we aim to shed light on the potential of Conditional Implicit Neural Representations (CINRs) as lightweight backbones in generative frameworks for audio synthesis.Implicit neural representations (INRs) are neural networks used to approximate low-dimensional functions, trained to represent a single geometric object by mapping input coordinates to structural information at input locations. In contrast with other neural methods for representing geometric objects, the memory required to parameterize the object is independent of resolution, and only scales with its complexity. A corollary of this is that INRs have infinite resolution, as they can be sampled at arbitrary resolutions. To apply the concept of INRs in the generative domain we frame generative modelling as learning a distribution of continuous functions. This can be achieved by introducing conditioning methods to INRs.Our experiments show that small Periodic Conditional INRs (PCINRs) learn faster and generally produce quantitatively better audio reconstructions than Transposed Convolutional Neural Networks with equal parameter counts. However, their performance is very sensitive to activation scaling hyperparameters. When learning to represent more uniform sets, PCINRs tend to introduce artificial high-frequency components in reconstructions. We validate this noise can be minimized by applying standard weight regularization during training or decreasing the compositional depth of PCINRs, and suggest directions for future research.