Efficiency of the bio-inspired Leaky Integrate-and-Fire neuron for signal coding

The goal of this paper is to investigate whether purely neuro-mimetic architectures are more efficient for signal compression than architectures that combine neuroscience and state-of-the-art models. We are motivated to produce spikes, using the LIF model, in order to compress images. Seeking solutions to improve the efficiency of the LIF in terms of the memory cost, we compare two different quantization approaches; the Neuroinspired Quantization (NQ) and the Conventional Quantization (CQ). We present that when the LIF model and the NQ appear in the same architecture, the performance of the compression system is higher compared to an architecture that consists of the LIF model and the CQ. The main reason of this occurrence is the dynamic properties embedded in the neuro-mimetic models. As a consequence, we first study which are the dynamic properties of the recently released (NQ) which is an intuitive way of counting the number of spikes. Moreover, we show that some parameters of the NQ (i.e. the observation window and the resistance) strongly influence its behavior that ranges from non-uniform to uniform. As a result, the NQ is more flexible than the CQ when it is applied to real data while for the same bit rate it ensures higher reconstruction quality.