Abstract
Biological neural networks with many plastic synaptic connections can store external input information in the map of synaptic weights as a form of unsupervised learning. However, the same neural network often produces dramatic reverberating events in which many neurons fire almost simultaneously – a phenomenon coined as ‘population burst.’ The autonomous bursting activity is a consequence of the delicate balance between recurrent excitation and self-inhibition; as such, any periodic sequences of burst-generating stimuli delivered even at a low frequency (~1 Hz) can easily suppress the entire network connectivity. Here we demonstrate that ‘Δt paired-pulse stimulation’, can be a novel way for encoding spatially-distributed high-frequency (~10 Hz) information into such a system without causing a complete suppression. The encoded memory can be probed simply by delivering multiple probing pulses and then estimating the precision of the arrival times of the subsequent evoked recurrent bursts.
Highlights
All STDP rules, developed so far, are for a pair of neurons: The rules consider the time difference(s) between two spikes, for example, one for the pre-synaptic neuron and the other for the post-synaptic neuron, and modifies the strength of the synapse connecting them as a nonlinear function of the time difference
The Δt paired-pulse stimulation protocol that we developed and implemented both in the earlier experiments[29] and the current model study is in a way a compromise between these two extremes and works in a regime causing significant changes to the network connectivity, yet, avoiding a complete depression
The Δt stimulation protocol includes a high-frequency component, which is relevant for the STDP rule, thereby enforcing a non-trivial interplay between the network plasticity and the external information
Summary
M. Plasticity of recurring spatiotemporal activity patterns in cortical networks. Simultaneous induction of pathway-specific potentiation and depression in networks of cortical neurons. Activity-dependent enhancement in the reliability of correlated spike timings in cultured cortical neurons. J. Modulating the precision of recurrent bursts in cultured neural networks. How adaptation shapes spike rate oscillations in recurrent neuronal networks. A. Dendritic and axonal propagation delays determine emergent structures of neuronal networks with plastic synapses. A. Delay-induced multistability and loop formation in neuronal networks with spike-timingdependent plasticity. Spike timing-dependent plasticity: a Hebbian learning rule.
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