Syntax generation through synfire propagation

Abstract:

Adult Bengalese finches generate a variable song that obeys a distinct and individual syntax. The syntax is gradually lost over a period of days after deafening and is recovered when hearing is restored. In the first part of this talk I will present a spiking neuronal network model of the song syntax generation and its loss, based on the assumption that the syntax is stored in reafferent connections from the auditory to the motor control area. Propagating synfire activity in the HVC (high vocal center) codes for individual syllables of the song and priming signals from the auditory network reduce the competition between syllables to allow only those transitions that are permitted by the syntax. Both imprinting of song syntax within HVC and the interaction of the reafferent signal with an efference copy of the motor command are sufficient to explain the gradual loss of syntax in the absence of auditory feedback. This study illustrates how sequential compositionality following a defined syntax can be realized in networks of spiking neurons.

In the second part of this talk I will consider how the synfire chains assumed in the first part could develop. It has long been though that spike-timing dependent plasticity (STDP) provides an answer to the question of how the brain can develop functional structure in response to repeated stimuli. However, convincing demonstrations of this capacity in large, initially random networks have not been forthcoming; such demonstrations as there are typically rely on constraining the problem artificially. I will present a theoretical analysis based on a mean field approach of the development of feed-forward structure in random networks. An unstable fixed point in the recruitment dynamics prevents the stable propagation of structure in recurrent networks with weight-dependent STDP. We demonstrate that the key predictions of the theory hold in large-scale simulations. The theory provides insight into the reasons why such development does not take place in unconstrained systems and enables us to identify candidate biologically motivated adaptations to the balanced random network model that might resolve the issue.

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