A parabrachial hub for the prioritization of survival behavior.

Long-term sustained pain in the absence of acute physical injury is a prominent feature of chronic pain conditions. While neurons responding to noxious stimuli have been identified, understanding the signals that persist without ongoing painful stimuli remains a challenge. Using an ethological approach based on the prioritization of adaptive survival behaviors, we determined that neuropeptide Y (NPY) signaling from multiple sources converges on parabrachial neurons expressing the NPY Y1 receptor to reduce sustained pain responses. Neural activity recordings and computational modeling demonstrate that activity in Y1R parabrachial neurons is elevated following injury, predicts functional coping behavior, and is inhibited by competing survival needs. Taken together, our findings suggest that parabrachial Y1 receptor-expressing neurons are a critical hub for endogenous analgesic pathways that suppress sustained pain states.

Hypernegative GABAA Reversal Potential in Pyramidal Cells Contributes to Medial Prefrontal Cortex Deactivation in a Mouse Model of Neuropathic Pain.

Deactivation of the medial prefrontal cortex (mPFC) has been broadly reported in both neuropathic pain models and human chronic pain patients. Several cellular mechanisms may contribute to the inhibition of mPFC activity, including enhanced GABAergic inhibition. The functional effect of GABAA(γ-aminobutyric acid type A)-receptor activation depends on the concentration of intracellular chloride in the postsynaptic neuron, which is mainly regulated by the activity of Na-K-2Cl cotransporter isoform 1 (NKCC1) and K-Cl cotransporter isoform 2 (KCC2), 2 potassium-chloride cotransporters that import and extrude chloride, respectively. Recent work has shown that the NKCC1-KCC2 ratio is affected in numerous pathological conditions, and we hypothesized that it may contribute to the alteration of mPFC function in neuropathic pain. We used quantitative in situ hybridization to assess the level of expression of NKCC1 and KCC2 in the mPFC of a mouse model of neuropathic pain (spared nerve injury), and we found that KCC2 transcript is increased in the mPFC of spared nerve injury mice while NKCC1 is not affected. Perforated patch recordings further showed that this results in the hypernegative reversal potential of the GABAA current in pyramidal neurons of the mPFC. Computational simulations suggested that this change in GABAA reversal potential is sufficient to significantly reduce the overall activity of the cortical network. Thus, our results identify a novel pathological modulation of GABAA function and a new mechanism by which mPFC function is inhibited in neuropathic pain. Our data also help explain previous findings showing that activation of mPFC interneurons has proalgesic effect in neuropathic, but not in control conditions. PERSPECTIVE: Chronic pain is associated with the presence of depolarizing GABAA current in the spinal cord, suggesting that pharmacological NKCC1 antagonism has analgesic effects. However, our results show that in neuropathic pain, GABAA current is actually hyperinhibitory in the mPFC, where it contributes to the mPFC functional deactivation. This suggests caution in the use of NKCC1 antagonism to treat pain.

Long- and short-term history effects in a spiking network model of statistical learning.

The statistical structure of the environment is often important when making decisions. There are multiple theories of how the brain represents statistical structure. One such theory states that neural activity spontaneously samples from probability distributions. In other words, the network spends more time in states which encode high-probability stimuli. Starting from the neural assembly, increasingly thought of to be the building block for computation in the brain, we focus on how arbitrary prior knowledge about the external world can both be learned and spontaneously recollected. We present a model based upon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neurons and biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectations and signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoing learning.

Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons.

Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.

Learning spatiotemporal signals using a recurrent spiking network that discretizes time.

Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.