Servizio Comunitario di Informazione in materia di Ricerca e Sviluppo - CORDIS

A mixed-signal neural network ASIC implementing spiking neurons and adapting synapses

The spiking neural network ASIC mimics neural behaviour to a large extend. First of all, its neurons are based on a membrane model. If the membrane potential reaches the threshold voltage, a spike generation process will be triggered. Contrary to the simple integrate-and-fire model, this process depends not only on the membrane voltage, but on its derivative as well.

The synapses are conductance based, with realistic levels for their reversal potentials. The shortening of the membrane time constant in the case that the total synaptic conductance reaches the high-conductance region can therefore be studied with the chip. Also, the exponential decay of the synaptic conductance is part of the design. This is important for the transformation of information from the spatial into the temporal realm.

Another important aspect is the statistical distribution of neural parameters. No two neurons are equal in nature; and this should be the case in an VLSI model. By looking closely at an analog circuit, it can be seen that this is also true for microelectronics. Fluctuations in the manufacturing process lead to parameter variations of each transistor, making it an individual as well. But we want to control these fluctuations to generate neural microcircuits with a known statistical distribution of their parameters. Therefore, each electronic neuron will contain several individually tunable parameters.

Plasticity is the key to understand how the brain can adapt to its environment. One important aspect of plasticity discovered in the recent years is the spike time dependent plasticity. In the spiking neural network chip each synapse measures the correlation between pre- and postsynaptic signal. These measurements are used to calculate changes in the synaptic weights.

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