Abstract
A synaptic architecture featuring both excitatory and inhibitory neurons is assembled aiming to build up an associative memory system. The connections follow a hebbian-like rule. The network activity is analyzed using a multidimensional reduction method, Principal Component Analysis (PCA), applied to neuron firing rates. The patterns are discriminated and recognized by well defined paths that emerge within PCA subspaces, one for each pattern. Detailed comparisons among these subspaces are used to evaluate the network storage capacity. We show a transition from a retrieval to a non-retrieval regime as the number of stored patterns increases. When gap junctions are implemented together with the chemical synapses, this transition is shifted and a larger number of memories is associated to the network.
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Agnes, E.J., Erichsen, R., Brunnet, L.G. (2012). Associative Memory in Neuronal Networks of Spiking Neurons: Architecture and Storage Analysis. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_19
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DOI: https://doi.org/10.1007/978-3-642-33269-2_19
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