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.

Neuropathic pain is a debilitating condition caused by the abnormal processing of somatosensory input. Synaptic inhibition in the spinal dorsal horn plays a key role in that processing. Mechanical allodynia – the misperception of light touch as painful – occurs when inhibition is compromised. Disinhibition is due primarily to chloride dysregulation caused by hypofunction of the potassium-chloride co-transporter KCC2. Here we show, in rats, that excitatory neurons are disproportionately affected. This is not because chloride is differentially dysregulated in excitatory and inhibitory neurons, but, rather, because excitatory neurons rely more heavily on inhibition to counterbalance strong excitation. Receptive fields in both cell types have a center-surround organization but disinhibition unmasks more excitatory input to excitatory neurons. Differences in intrinsic excitability also affect how chloride dysregulation affects spiking. These results deepen understanding of how excitation and inhibition are normally balanced in the spinal dorsal horn, and how their imbalance disrupts somatosensory processing.

Cognition is compromised by white matter (WM) injury but the neurophysiological alterations linking them remain unclear. We hypothesized that reduced neural synchronization caused by disruption of neural signal propagation is involved. To test this, we evaluated group differences in: diffusion tensor WM microstructure measures within the optic radiations, primary visual area (V1), and cuneus; neural phase synchrony to a visual attention cue during visual-motor task; and reaction time to a response cue during the same task between 26 pediatric patients (17/9: male/female) treated with cranial radiation treatment for a brain tumor (12.67 ± 2.76 years), and 26 healthy children (16/10: male/female; 12.01 ± 3.9 years). We corroborated our findings using a corticocortical computational model representing perturbed signal conduction from myelin. Patients show delayed reaction time, WM compromise, and reduced phase synchrony during visual attention compared with healthy children. Notably, using partial least-squares-path modeling we found that WM insult within the optic radiations, V1, and cuneus is a strong predictor of the slower reaction times via disruption of neural synchrony in visual cortex. Observed changes in synchronization were reproduced in a computational model of WM injury. These findings provide new evidence linking cognition with WM via the reliance of neural synchronization on propagation of neural signals. By comparing brain tumor patients to healthy children, we establish that changes in the microstructure of the optic radiations and neural synchrony during visual attention predict reaction time. Furthermore, by testing the directionality of these links through statistical modeling and verifying our findings with computational modeling, we infer a causal relationship, namely that changes in white matter microstructure impact cognition in part by disturbing the ability of neural assemblies to synchronize. Together, our human imaging data and computer simulations show a fundamental connection between WM microstructure and neural synchronization that is critical for cognitive processing.

Neurons regulate their excitability by adjusting their ion channel levels. Degeneracy - achieving equivalent outcomes (excitability) using different solutions (channel combinations) - facilitates this regulation by enabling a disruptive change in one channel to be offset by compensatory changes in other channels. But neurons must co-regulate many properties. Pleiotropy - the impact of one channel on more than one property - complicates regulation because a compensatory ion channel change that restores one property to its target value often disrupts other properties. How then does a neuron simultaneously regulate multiple properties? Here we demonstrate that of the many channel combinations producing the target value for one property (the single-output solution set), few combinations produce the target value for other properties. Combinations producing the target value for two or more properties (the multi-output solution set) correspond to the intersection between single-output solution sets. Properties can be effectively co-regulated only if the number of adjustable channels (nin) exceeds the number of regulated properties (nout). Ion channel correlations emerge during homeostatic regulation when the dimensionality of solution space (nin - nout) is low. Even if each property can be regulated to its target value when considered in isolation, regulation as a whole fails if single-output solution sets do not intersect. Our results also highlight that ion channels must be co-adjusted with different ratios to regulate different properties, which suggests that each error signal drives modulatory changes independently, despite those changes ultimately affecting the same ion channels.

Pain-related sensory input is processed in the spinal cord before being relayed to the brain. That processing profoundly influences whether stimuli are correctly or incorrectly perceived as painful.Significant advances have been made in identifying the types of excitatory and inhibitory neurons that comprise the spinal dorsal horn (SDH), and there is some information about how neuron types are connected, but it remains unclear how the overall circuit processes sensory input. To explore SDH circuit function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based neuron models that reproduce the characteristic firing patterns of excitatory and inhibitory neurons. Excitatory neuron subtypes defined by calretinin, somatostatin, delta opioid receptor, protein kinase C gamma, or vesicular glutamate transporter 3 expression or by transient/central spiking/morphology, and inhibitory neuron subtypes defined by parvalbumin or dynorphin expression or by islet morphology were synaptically connected according to available qualitative data. Synaptic weights were adjusted to produce firing in projection neurons, defined by neurokinin-1 expression, matching experimentally measured responses to a range of mechanical stimulus intensities. Input to the circuit was provided by three types of afferents whose firing rates were also matched to experimental data. To validate our model, we ablated specific neuron types or applied other changes and compared model output with experimental data after equivalent manipulations. The resulting model provides a valuable tool for testing hypotheses in silico to plan novel experiments on SDH circuit dynamics and function.

Primary afferent neurons convey somatosensory information to the CNS. Low-threshold mechanoreceptors are classified as slowadapting (SA) or rapid-adapting (RA) based on whether or not they spike repetitively during sustained tactile stimulation; the former are subclassified as Type 1 or 2 based on the regularity of their spiking. Recording in vivo from DRGs of mice, we observed irregular-and regular-spiking units consistent with SA1 and SA2 low-threshold mechanoreceptors, but some units, which we labeled "semiregular," did not fit cleanly into the existing classification scheme. Analysis of their spiking revealed integer-multiple patterning in which spike trains comprised a fundamental interspike interval and multiples thereof. Integer-multiple-patterned spiking was reproduced by randomly removing spikes from an otherwise regular spike train, suggesting that semiregular units represent SA2 units in which some spikes are "missing." We hypothesized that missing spikes arose from intermittent failure of spikes to initiate or to propagate. Intermittent failure of spike initiation was ruled out by several observations: integer-multiple-patterned spiking was not induced by intradermal lidocaine, was independent of stimulus modality (mechanical vs optogenetic), and could not be reproduced in a conductance-based model neuron given constant input. On the other hand, integer-multiple-patterned spiking was induced by application of lidocaine to the DRG, thus pinpointing intermittent failure of spike propagation as the basis for integer-multiple-patterned spiking. Indeed, half of all SA2 units exhibited some missing spikes, mostly at low rate (Ͻ5%), which suggests that axons are efficient in using the lowest safety factor capable of producing near-perfect propagation reliability.

Key points Distinct spiking patterns may arise from qualitative differences in ion channel expression (i.e. when different neurons express distinct ion channels) and/or when quantitative differences in expression levels qualitatively alter the spike generation process.We hypothesized that spiking patterns in neurons of the superficial dorsal horn (SDH) of spinal cord reflect both mechanisms.We reproduced SDH neuron spiking patterns by varying densities of KV1‐ and A‐type potassium conductances. Plotting the spiking patterns that emerge from different density combinations revealed spiking‐pattern regions separated by boundaries (bifurcations).This map suggests that certain spiking pattern combinations occur when the distribution of potassium channel densities straddle boundaries, whereas other spiking patterns reflect distinct patterns of ion channel expression. The former mechanism may explain why certain spiking patterns co‐occur in genetically identified neuron types.We also present algorithms to predict spiking pattern proportions from ion channel density distributions, and vice versa. AbstractNeurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by varying the densities of a low‐threshold (KV1‐type) potassium conductance and an inactivating (A‐type) potassium conductance and found that single, gap, delayed and reluctant spiking arise when the joint probability distribution of those channel densities spans two intersecting bifurcations that divide the parameter space into quadrants, each associated with a different spiking pattern. Tonic spiking likely arises from a separate distribution of potassium channel densities. These results argue in favour of two cell populations, one characterized by tonic spiking and the other by heterogeneous spiking patterns. We present algorithms to predict spiking pattern proportions based on ion channel density distributions and, conversely, to estimate ion channel density distributions based on spiking pattern proportions. The implications for classifying cells based on spiking pattern are discussed.

Continuation of spiking after a stimulus ends (i.e. persistent spiking) is thought to support working memory. Muscarinic receptor activation enables persistent spiking among synaptically isolated pyramidal neurons in anterior cingulate cortex (ACC), but a detailed characterization of that spiking is lacking and the underlying mechanisms remain unclear. Here, we show that the rate of persistent spiking in ACC neurons is insensitive to the intensity and number of triggers, but can be modulated by injected current, and that persistent spiking can resume after several seconds of hyperpolarization-imposed quiescence. Using electrophysiology and calcium imaging in brain slices from male rats, we determined that canonical transient receptor potential (TRPC) channels are necessary for persistent spiking and that TRPC-activating calcium enters in a spike-dependent manner via voltage-gated calcium channels. Constrained by these biophysical details, we built a computational model that reproduced the observed pattern of persistent spiking. Nonlinear dynamical analysis of that model revealed that TRPC channels become fully activated by the small rise in intracellular calcium caused by evoked spikes. Calcium continues to rise during persistent spiking, but because TRPC channel activation saturates, firing rate stabilizes. By calcium rising higher than required for maximal TRPC channel activation, TRPC channels are able to remain active during periods of hyperpolarization-imposed quiescence (until calcium drops below saturating levels) such that persistent spiking can resume when hyperpolarization is discontinued. Our results thus reveal that the robust intrinsic bistability exhibited by ACC neurons emerges from the nonlinear positive feedback relationship between spike-dependent calcium influx and TRPC channel activation. Neurons use action potentials, or spikes, to encode information. Some neurons can store information for short periods (seconds to minutes) by continuing to spike after a stimulus ends, thus enabling working memory. This so-called "persistent" spiking occurs in many brain areas and has been linked to activation of canonical transient receptor potential (TRPC) channels. However, TRPC activation alone is insufficient to explain many aspects of persistent spiking such as resumption of spiking after periods of imposed quiescence. Using experiments and simulations, we show that calcium influx caused by spiking is necessary and sufficient to activate TRPC channels and that the ensuing positive feedback interaction between intracellular calcium and TRPC channel activation can account for many hitherto unexplained aspects of persistent spiking.