Abstract
The storage of temporally precise spike patterns can be realized by a single neuron. A spiking neural network (SNN) model is utilized to demonstrate the ability to precisely recall a spike pattern after presenting a single input. We show by using a simulation study that the temporal properties of input patterns can be transformed into spatial patterns of local dendritic spikes. The localization of time-points of spikes is facilitated by phase-shift of the subthreshold membrane potential oscillations (SMO) in the dendritic branches, which modifies their excitability. In reference to the points in time of the arriving input, the dendritic spikes are triggered in different branches. To store spatially distributed patterns, two unsupervised learning mechanisms are utilized. Either synaptic weights to the branches, spatial representation of the temporal input pattern, are enhanced by spike-timing-dependent plasticity (STDP) or the oscillation power of SMOs in spiking branches is increased by dendritic spikes. For retrieval, spike bursts activate stored spatiotemporal patterns in dendritic branches, which reactivate the original somatic spike patterns. The simulation of the prototypical model demonstrates the principle, how linking time to space enables the storage of temporal features of an input. Plausibility, advantages, and some variations of the proposed model are also discussed.
Highlights
In daily life, we can distinguish between temporal and spatial properties of our world
Larson et al (2010) explicitly spread time components into spatial components of an input as they investigated the question how sensory systems recognize time varying stimuli by spiking activity. Their model consisted of a succession of end-to-end excitatory neurons in combination with spike-timing-dependent plasticity (STDP) to preserve the temporal features of spike patterns via their spatial distribution
A major goal of this study is to show that a single neuron would be able to store temporal properties of an input by the spatial pattern of dendritic branch activation
Summary
We can distinguish between temporal and spatial properties of our world. The temporal as well as spatial properties of the world is largely represented by spatiotemporal patterns of neural spikes. An important question currently facing scientists is: How the temporal dimension of the physical world is represented in the brain? Larson et al (2010) explicitly spread time components into spatial components of an input as they investigated the question how sensory systems recognize time varying stimuli by spiking activity. Their model consisted of a succession of end-to-end excitatory neurons (neuronal chain) in combination with STDP to preserve the temporal features of spike patterns via their spatial distribution. In the neuronal chain model, the sensory input activates the first neuron in a chain of neurons, following which the neighboring neurons were activated sequentially with a delay of 2 ms
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