Abstract
Theories of neural coding seek to explain how states of the world are mapped onto states of the brain. Here, we compare how an animal's location in space can be encoded by two different kinds of brain states: population vectors stored by patterns of neural firing rates, versus synchronization vectors stored by patterns of synchrony among neural oscillators. It has previously been shown that a population code stored by spatially tuned ‘grid cells’ can exhibit desirable properties such as high storage capacity and strong fault tolerance; here it is shown that similar properties are attainable with a synchronization code stored by rhythmically bursting ‘theta cells’ that lack spatial tuning. Simulations of a ring attractor network composed from theta cells suggest how a synchronization code might be implemented using fewer neurons and synapses than a population code with similar storage capacity. It is conjectured that reciprocal connections between grid and theta cells might control phase noise to correct two kinds of errors that can arise in the code: path integration and teleportation errors. Based upon these analyses, it is proposed that a primary function of spatially tuned neurons might be to couple the phases of neural oscillators in a manner that allows them to encode spatial locations as patterns of neural synchrony.
Highlights
A fundamental aim of computational neuroscience research is to explain how patterns of brain activity store and process information about the world
We consider how the brain encodes information about an animal’s location in its environment, by comparing how locations in space can be represented by two kinds of neural activity patterns: population vectors, which are patterns of neural firing rates [1,2,3,4,5,6], versus synchronization vectors, which are patterns of phase alignment among neural oscillators [7,8,9,10,11,12,13,14,15,16,17,18,19,20]
In a moving animal, fcannot rest at a fixed attractor point because it must follow a trajectory through synchronization space that accurately encodes the animal’s navigational trajectory through the environment. This can be achieved by a microcircuit model of a ‘grid module’ consisting of multiple grids cells with different target synchronization vectors, so that different grid cells compete with one another to hold the ring oscillators in their own preferred phase alignments
Summary
A fundamental aim of computational neuroscience research is to explain how patterns of brain activity store and process information about the world. In rodents, many spatially tuned neurons exhibit rhythmic modulation of their spike trains by 4–12 Hz theta oscillations, which can shift phase against locally recorded electroencephalography rhythms as an animal travels through a cell’s firing field [9,26,27,28,29] Such observations have fuelled speculation that theta oscillations might support a temporal code for space [29,30,31,32,33,34], perhaps by storing synchronization vectors that encode locations in the environment as patterns of neural synchrony [10,11,12,13,14,15,16,17,18,19,20]. It is conjectured here that because of these differences, a spatial code might be stored more efficiently by synchronization than population vectors
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