The location of the axon initial segment affects the bandwidth of spike initiation dynamics
Fig 7
Liquid-state machine computations. In order to test the functional impact of the AIS location and the bandwidth of the transfer gain, we simulated a network of exponential Integrate-and-Fire (eIF) units using parameters fit to models with different AIS locations (see Fig 6 and Table 3). (A) The input was fed to a pool of recurrently connected neurons (black and blue: excitatory, red: inhibitory). Neurons were connected randomly through dynamic synapses. The filtered spikes (liquid states) of a subset of excitatory neurons (output neurons, blue), was used as input to a linear classifier. (B) The network input consisted of jittered versions of two base spike templates. (C) The classifier was trained to compute a XOR of the last two shown templates (top) using the spikes of the output neurons (blue) in the liquid (middle). As performance criterion we recorded how often the readout response y (bottom) matched the target output (correct outputs are shown in green, incorrect outputs in red) for the parameters for different AIS locations (e.g. the AP slope ΔT). (D) The fitted ΔT values are shown versus the soma-AIS distance. The insets show the change of the slope at the AP onset from the first to the last AIS position. (E) As we varied the AIS locations, the Liquid State Machine performance improved in the classification task. The effect was significant for the first two distance increments (50 runs, Wilcoxon rank-sum test, * = p < 0.05, ** = p < 0.005, etc.).