ABSTRACT

Although biological neurons communicate using electro-chemical action potentials (or spikes), they also possess a variety of slower, chemical regulatory processes that analyze the statistical properties of information flow. Such processes ensure network stability, maximize information efficiency, and adapt neurons to incoming signals from the environment [1, 2]. The execution of higher functions such as learning, memory or pattern recognition require a collective orchestration of how the neural network is interconnected. Yet each neuron in the network makes decisions based on little information encoded on spikes originated from neighboring neurons. Thus, the challenge before neuro-scientists is to understand how such intelligent interconnection patterns can arise from local learning mechanisms.