Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability

Abstract

In this article we present a biologically inspired model of activation of memory items in a sequence. Our model produces two types of sequences, corresponding to two different types of cerebral functions: activation of regular or irregular sequences. The switch between the two types of activation occurs through the modulation of biological parameters, without altering the connectivity matrix. Some of the parameters included in our model are neuronal gain, strength of inhibition, synaptic depression and noise. We investigate how these parameters enable the existence of sequences and influence the type of sequences observed. In particular we show that synaptic depression and noise drive the transitions from one memory item to the next and neuronal gain controls the switching between regular and irregular (random) activations.

Statistical learning of unbalanced exclusive-or temporal sequences in humans

“…These same synapses between A and B would depress when one neuron A or B only is active, which is the case when encoding sequences AE or DB. Such encoding of first-order TP allows populations of neurons coding for the items in memory to activate each other in sequences, allowing the reproduction of learned sequences (see [ 31 , 56 , 58 ]). The learning of first-order TP by synapses indicates that temporal contiguity is a fundamental property of events to be encoded in memory.…”