  There is extensive evidence that biological neural networks encode
information in the precise timing of the spikes generated and transmitted by
neurons, which offers several advantages over rate-based codes. Here we adopt a
vector space formulation of spike train sequences and introduce a new liquid
state machine (LSM) network architecture and a new forward orthogonal
regression algorithm to learn an input-output signal mapping or to decode the
brain activity. The proposed algorithm uses precise spike timing to select the
presynaptic neurons relevant to each learning task. We show that using precise
spike timing to train the LSM and selecting the readout neurons leads to a
significant increase in performance on binary classification tasks as well as
in decoding neural activity from multielectrode array recordings, compared with
what is achieved using the standard architecture and training method.
