Signal denoising in modular spiking networks Barna Zajzon1,2, Abigail Morrison1,3, Renato Duarte1 1 Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), J¨ulich Research Centre and JARA, J¨ulich, Germany 2 Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Germany 3 Software Engineering, Department of Computer Science 3, RWTH Aachen University, Germany Contact: b.zajzon@fz-juelich.de 1. Introduction • Topographic maps, comprising ordered projections among distinct neuronal populations, are a well-studied and pervasive structural feature of the cortical hierarchy, yet little is known about their functional relevance • We investigate the impact of key structural parameters of topography in the context of a signal reconstruction task in a sequential setup: modularity, map size and degree of overlap • Information transmission improves as topographic projections become more structured • This structuring, deﬁned here in terms of modularity, causes a disruption in the E-I balance and leads to the emergence of inhibition-dominated regimes • In some conditions, networks operating in such regimes exhibit spatial denoising properties and the task performance improves vastly with hierarchical depth 2. Network architecture & task νX m=0 m=0.8 m=1 neuron id (Ei) neuron id (Ei+1) additive Gaussian noise no noise ... encoder S1 Stimulus sequence S2 S1 S2 S1 S2 times [ms] M0 M2 M1 topographic modularity (feed-forward connections): m = 1 −p0 pc ∈[0, 1]. p0: inter-cluster connection probability pc: intra-cluster connection probability cluster: corresponding stimulus-speciﬁc populations • Modules are balanced random networks of LIF neurons (6 modules) • Structured stimuli drive speciﬁc neuronal clusters in M0, building a topographic map • Treat local microcircuits as state-dependent processing reservoirs (Reservoir Computing) [2] • Task: reconstruction of a continuous signal corrupted by Gaussian (spatial) noise 3.Signal reconstruction from noisy input • Modularity in the topographic projections is essential for accurate reconstruction of the input signal (see also [1]) • For m >∼0.82 gradual but signiﬁcant improvement in the decoding, in all modules • Spatial segregation is increasingly preserved along the topographic maps with larger m (denois- ing) ⇒information is encoded and transmitted primarily through the activity of these clusters 4. Modularity controls the E-I balance • Mean-ﬁeld analysis: ﬁring rates of the active (non-active) clusters increase (decrease) with m • For larger values of m, the E-I balance is disrupted. Neurons in clusters associated with the active stimulus receive signiﬁcantly more net overall excitation, the others become more inhibited • The spatially encoded signal is ampliﬁed through the network (in inhibition-dominated regimes) • Dynamical system’s perspective: these eﬀects arise due to a change in the eﬀective connectivity • As the population activity becomes increasingly entrained by input for larger m, the spectral radius contracts with network depth, suggesting a decrease in the richness of the dynamics • Modular projections onto the inhibitory populations stabilize the system 5. Constraints on the map size • Trade-oﬀbetween modularity and map size, lower bound on map size • Cluster overlap has a clear detrimental eﬀect on the task performance • Progressively larger clusters mimicking an anatomical feature of cortical maps [3] can lead to substantial performance improvement for moderate topographic precision Discussion • Structured connectivity, (possibly) in form of modular topographic projections, is required for accurate information transmission and can enhance spatial features of the input • The denoising phenomena relies on increasingly inhibition-dominated regimes in the hierarchy that emerge through modular projections • Role of topographic modularity in denoising can be understood as a transient, stimulus-speciﬁc change in the eﬀec