Abstract
The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.
Highlights
Sensory categorization is mediated, at least in part, by brain processes that extract information from the precise points in time at which neurons emit their first few spikes in response to the presentation of a sensory object [1,2,3,4,5,6,7]
We show that recruitment order is generally applicable as an ensemble code; it emerges spontaneously in a large ‘‘structureless’’ network of neurons as a functional code that is invariant to significant temporal variance in spike times and spike rates and flawlessly classifies inputs on a trial-to-trial basis
Perhaps the most severe constraint in that respect is the multiplicity and wide range of timescales that are characteristic of neuronal excitability and synaptic communication: At each and every level of observation, physiologists report an ever-increasing range of reaction time scales that are involved in the generation of action potentials and their transformation to post synaptic signals [34,35,36,37,38]
Summary
At least in part, by brain processes that extract information from the precise points in time at which neurons emit their first few spikes in response to the presentation of a sensory object [1,2,3,4,5,6,7]. A attractive candidate representation primitive makes use of the order of neuronal recruitment, computed from the latencies to first spikes. Order based representation can result from an underlying feed-forward network structure (e.g., [12]). Is recruitment order applicable for representing stimuli that are not temporally ordered, in complex large-scale recurrent neural networks? How much of the network’s classification capacity is conserved when absolute times of spikes evoked in response to a given stimulus are compacted to vectors of recruitment orders? The answers to these questions impact on the general applicability of recruitment order as an ensemble neural representation scheme What if these constraints are relaxed? Is recruitment order applicable for representing stimuli that are not temporally ordered, in complex large-scale recurrent neural networks? If applicable, how does it handle trial-to-trial variations in spike times of individual neurons? How sensitive is it to the temporal resolution of ordering and the number of sampled neurons? How much of the network’s classification capacity is conserved when absolute times of spikes evoked in response to a given stimulus are compacted to vectors of recruitment orders? The answers to these questions impact on the general applicability of recruitment order as an ensemble neural representation scheme
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