1. A neural network, comprising: a plurality of electronic axons; a plurality of electronic neurons; a crossbar comprising a plurality of electronic synapses for interconnecting the axons with the neurons, wherein each synapse interconnects an axon with a neuron; and a column aggregation module for transposable access to one or more synapses of the crossbar using column aggregation; wherein column aggregation comprises: maintaining a synaptic weight of each synapse of the crossbar; receiving an axon vector identifying the axons that generated spike events; in response to the axon vector received, reading every row of the crossbar within one time step to obtain the synaptic weights of all synapses of the crossbar; generating a weight vector identifying the synaptic weights of synapses in each row read; and generating an update vector by multiplying using a bitwise AND operation each weight vector generated with the axon vector, wherein the update vector generated identifies the neurons that receive spike events and update to a new operational state.