Abstract
Introduction The activity of pyramidal cells in the hippocampus has been empirically demonstrated to encode both spatial and non-spatial cues by means of a dual code [1]. The phase of place cell firing with respect to the theta oscillation encodes spatial information: primarily the position of an animal and its current heading [2]. Conversely, firing rate has been demonstrated to encode a variety of non-spatial cues, including running speed, complex visual stimuli and concepts [3-5]. Here we present a novel spiking neural network model which is, to our knowledge, the first to use a dual coding system in order to learn and recall associations between both temporally coded (spatial) and rate-coded (non-spatial) activity patterns.
Highlights
The activity of pyramidal cells in the hippocampus has been empirically demonstrated to encode both spatial and non-spatial cues by means of a dual code [1]
Place cell activity consists of a compressed temporal sequence of neural firing within each theta phase
We demonstrate that: 1. The external stimulation of any place cell generates the sequential recall of upcoming place fields on the learned route; 2
Summary
The activity of pyramidal cells in the hippocampus has been empirically demonstrated to encode both spatial and non-spatial cues by means of a dual code [1]. The phase of place cell firing with respect to the theta oscillation encodes spatial information: primarily the position of an animal and its current heading [2]. Firing rate has been demonstrated to encode a variety of non-spatial cues, including running speed, complex visual stimuli and concepts [3,4,5]. We present a novel spiking neural network model which is, to our knowledge, the first to use a dual coding system in order to learn and recall associations between both temporally coded (spatial) and rate-coded (non-spatial) activity patterns
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