Stimulus representation in anisotropically connected spiking neural networks

Abstract: Biological neuronal networks are a key object of study in the field of computational neuroscience, and recent studies have also shown their potential applicability within artificial intelligence and robotics [1]. They come in many shapes and forms, and a well known and widely studied example is the liquid state machine from 2004 [2]. In 2019, a novel and simple connectivity rule was presented with the introduction of the SpreizerNet [3]. The connectivity of the SpreizerNet is governed by a type of gradient noise known as Perlin noise, and as such the connectivity is anisotropic but correlated. The spiking activity produced in the SpreizerNet is possibly functionally relevant, e.g. for motor control or classification of input stimuli. In 2020, it was shown to be useful for motor control [4]. In this Master’s thesis, we inquire if the spiking activity of the SpreizerNet is functionally relevant in the context of stimulus representation. We investigate how input stimulus from the MNIST handwritten digits dataset is represented in the spatio-temporal activity sequences produced by the SpreizerNet, and whether this representation is sufficient for separation. Furthermore, we consider how the parameters governing the local structure of connectivity impacts representation and separation. We find that (1) the SpreizerNet separates input stimulus at the initial stage after stimulus and (2) that separation decreases with time when the activity from dissimilar inputs becomes unified.