Stochastic dynamics and delta-band oscillations in clustered spiking networks

Grant Sutcliffe
Declarations according to §8 (3) b) and c) of the doctoral degree regulations: a) I hereby declare that I have written the submitted dissertation myself and in this process have used no other sources or materials than those expressly indicated, b) I hereby declare that I have not applied to be examined at any other institution, nor have I used the dissertation in this or any other form at any other institution as an examination paper, nor submitted it to any other faculty as a dissertation. i
more » ... stract Following experimental measurements of clustered connectivity in the cortex, recent studies have found that clustering connections in simulated spiking networks causes transitions between high and low firing-rate states in subgroups of neurons. An open question is to what extent the sequence of transitions in such networks can be related to existing statistical and mechanical models of sequence generation. In this thesis we present several studies of the relationship between connection structure and network dynamics in balanced spiking networks. We investigate which qualities of the network connection matrix support the generation of state sequences, and which properties determine the specific structure of transitions between states. We find that adding densely overlapping clusters with equal levels of recurrent connectivity to a network with dense inhibition can produce sequential winner-takesall dynamics in which high-activity states pass between correlated clusters. This activity is reflected in the power spectrum of spiking activity as a peak in the lowfrequency delta range. We describe and verify sequence dynamics with a Markov chain framework, and compare them mechanically to "latching" models of sequence generation. Additionally we quantify the chaos of clustered networks and find that minimally separated states diverge in distinct stages. The results clarify the computational capabilities of clustered spiking networks and their relationship to experimental findings. We conclude that the results provide a supporting intermediate link between abstract models and biological instances of sequence generation. ii 65 J EI 6 J IE 18 J II 5 62
doi:10.11588/heidok.00023667 fatcat:wdxobwaf65c45bf3xooph2ys3i