Differentially synchronized spiking enables multiplexed neural coding

Abstract

Multiplexing refers to the simultaneous encoding of two or more signals. Neurons have been shown to multiplex, but different stimuli require different multiplexing strategies. Whereas the frequency and amplitude of periodic stimuli can be encoded by the timing and rate of the same spikes, natural scenes, which comprise areas over which intensity varies gradually and sparse edges where intensity changes abruptly, require a different multiplexing strategy. Recording in vivo from neurons in primary somatosensory cortex during tactile stimulation, we found that stimulus onset and offset (edges) evoked highly synchronized spiking, whereas other spikes in the same neurons occurred asynchronously. Stimulus intensity modulated the rate of asynchronous spiking, but did not affect the timing of synchronous spikes. From this, we hypothesized that spikes driven by high- and low-contrast stimulus features can be distinguished on the basis of their synchronization, and that differentially synchronized spiking can thus be used to form multiplexed representations. Applying a Bayesian decoding method, we verified that information about high- and low-contrast features can be recovered from an ensemble of model neurons receiving common input. Equally good decoding was achieved by distinguishing synchronous from asynchronous spikes and applying reverse correlation methods separately to each spike type. This result, which we verified with patch clamp recordings in vitro, demonstrates that neurons receiving common input can use the rate of asynchronous spiking to encode the intensity of low-contrast features while using the timing of synchronous spikes to encode the occurrence of high-contrast features. We refer to this strategy as synchrony-division multiplexing.

Orientation processing by synaptic integration across first-order tactile neurons

“…Therefore, NMDA inputs may mediate the discrimination of objects based on their macro-geometric featuresfor example, discriminating between your house key and car key when searching in your pocket. The differentiation between AMPA and NMDA input streams could be established for example via compartmentalized synaptic locations on the postsynaptic neuron dendrites [16] or by multiplexed neural coding [17]. Our models also suggest a tendency for sparseness and the involvement of a small set of key synaptic inputs from first-order neurons, supporting previous empirical work [18] and suggesting that imposing a sparseness constraint on the synaptic weights may improve discrimination performance [19].…”

Scanned optogenetic control of mammalian somatosensory input to map input-specific behavioral outputs

“…The intensity, size and location of a stimulus can be conveyed rapidly by this neural code. Relative arrival times of the first action potentials might also contribute to the code, as observed in the visual system (Gollisch & Meister, 2008), and subsequent action potentials could enable multiplexing (Lankarany et al, 2019). We use a broad-class nociceptor line and it is possible that its subpopulations exploit a diversity of coding strategies.…”

A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble

“…It has been suggested that the presence of both coding strategies, whose feasibility was demonstrated in different neural systems (e.g., [ 16 , 17 ]), can be considered as a unique way to convey multiple features of the stimulus, i.e., multiplexed coding. In fact, in addition to the rate code, which is largely observed in different neural systems, inter-neuronal correlations within many areas of the brain have a significant functional role in the neural code [ 18 , 19 , 20 ].…”

“…It is challenging to uncover the distinct roles of differentially correlated spikes—i.e., asynchronous spikes (rate code) and synchronous spikes (temporal code)—in a multiplexed code. To address this challenge, it is crucial to measure the information underlying different types of spikes [ 16 ]. Various information-theoretic techniques have been exploited to measure information carried by differentially correlated spikes [ 15 , 21 ].…”