Abstract
It is well accepted that the brain's computation relies on spatiotemporal activity of neural networks. In particular, there is growing evidence of the importance of continuously and precisely timed spiking activity. Therefore, it is important to characterize memory states in terms of spike-timing patterns that give both reliable memory of firing activities and precise memory of firing timings. The relationship between memory states and spike-timing patterns has been studied empirically with large-scale recording of neuron population in recent years. Here, by using a recurrent neural network model with dynamics at two time scales, we construct a dynamical memory network model which embeds both fast neural and synaptic variation and slow learning dynamics. A state vector is proposed to describe memory states in terms of spike-timing patterns of neural population, and a distance measure of state vector is defined to study several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli. We show that the distance measure can capture the timing difference of memory states. In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network's ability to embed more memory states. Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.
Highlights
Recurrent neural networks of the brain compute information through complexly spatiotemporal neural activity
Recent experimental observations and theoretical studies have proposed that spike-timing patterns (STPs) in the range of a few hundred milliseconds play a fundamental role in sensory, motor and highlevel cognitive behaviors such as learning and memory [1,2,3,4]
By proposing a state vector for the STP induced by each stimulus, we show the distance of state vectors can be used to characterize learning process and several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli
Summary
Recurrent neural networks of the brain compute information through complexly spatiotemporal neural activity. Recent experimental observations and theoretical studies have proposed that spike-timing patterns (STPs) in the range of a few hundred milliseconds play a fundamental role in sensory, motor and highlevel cognitive behaviors such as learning and memory [1,2,3,4]. Songbirds, one of the most studied neural systems, learn and memorize the crystallized song composed by precise individual syllables as STPs [5]. Firing rate is used to describe the activity of single neurons and neural networks. A memorized song as a STP contains firing activity, i.e., whether neurons fire or not, and firing timings, i.e., when neurons fire. Memory has to be both reliable in firing and precise in timing. Firing rate as a average measure is reliable but not precise [2]. The question is how to capture the precise timing of memory from STPs
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