Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles

Differential brainstem connectivity according to sex and menopausal status in healthy men and women

Background Brainstem nuclei play a critical role in both ascending monoaminergic modulation of cortical function and arousal, and in descending bulbospinal pain modulation. Even though sex-related differences in the function of both systems have been reported in animal models, a complete understanding of sex differences, as well as menopausal effects, in brainstem connectivity in humans is lacking. This study evaluated resting-state connectivity of the dorsal raphe nucleus (DRN), right and left locus coeruleus complex (LCC), and periaqueductal gray (PAG) according to sex and menopausal status in healthy individuals. In addition, relationships between systemic estrogen levels and brainstem-network connectivity were examined in a subset of participants. Methods Resting-state fMRI was performed in 50 healthy men (age, 31.2 ± 8.0 years), 53 healthy premenopausal women (age, 24.7 ± 7.3 years; 22 in the follicular phase, 31 in the luteal phase), and 20 postmenopausal women (age, 54.6 ± 7.2 years). Permutation Analysis of Linear Models (5000 permutations) was used to evaluate differences in brainstem-network connectivity according to sex and menopausal status, controlling for age. In 10 men and 17 women (9 premenopausal; 8 postmenopausal), estrogen and estrogen metabolite levels in plasma and stool were determined by liquid chromatography-mass spectrometry/mass spectrometry. Relationships between estrogen levels and brainstem-network connectivity were evaluated by partial least squares analysis. Results Left LCC-executive control network (ECN) connectivity showed an overall sex difference (p = 0.02), with higher connectivity in women than in men; however, this was mainly due to differences between men and pre-menopausal women (p = 0.008). Additional sex differences were dependent on menopausal status: PAG-default mode network (DMN) connectivity was higher in postmenopausal women than in men (p = 0.04), and PAG-sensorimotor network (SMN) connectivity was higher in premenopausal women than in men (p = 0.03) and postmenopausal women (p = 0.007). Notably, higher free 2-hydroxyestrone levels in stool were associated with higher PAG-SMN and PAG-DMN connectivity in premenopausal women (p < 0.01). Conclusions Healthy women show higher brainstem-network connectivity involved in cognitive control, sensorimotor function, and self-relevant processes than men, dependent on their menopausal status. Further, 2-hydroxyestrone, implicated in pain, may modulate PAG connectivity in premenopausal women. These findings may relate to differential vulnerabilities to chronic stress-sensitive disorders at different life stages.

A sticky Poisson Hidden Markov Model for spike data

Fitting a hidden Markov Model (HMM) to neural data is a powerful method to segment a spatiotemporal stream of neural activity into sequences of discrete hidden states. Application of HMM has allowed to uncover hidden states and signatures of neural dynamics that seem relevant for sensory and cognitive processes. This has been accomplished especially in datasets comprising ensembles of simultaneously recorded cortical spike trains. However, the HMM analysis of spike data is involved and requires a careful handling of model selection. Two main issues are: (i) the cross-validated likelihood function typically increases with the number of hidden states; (ii) decoding the data with an HMM can lead to very rapid state switching due to fast oscillations in state probabilities. The first problem is related to the phenomenon of over-segmentation and leads to overfitting. The second problem is at odds with the empirical fact that hidden states in cortex tend to last from hundred of milliseconds to seconds. Here, we show that we can alleviate both problems by regularizing a Poisson-HMM during training so as to enforce large self-transition probabilities. We call this algorithm the 'sticky Poisson-HMM' (sPHMM). When used together with the Bayesian Information Criterion for model selection, the sPHMM successfully eliminates rapid state switching, outperforming an alternative strategy based on an HMM with a large prior on the self-transition probabilities. The sPHMM also captures the ground truth in surrogate datasets built to resemble the statistical properties of the experimental data.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.

A scalable spiking amygdala model that explains fear conditioning, extinction, renewal and generalization

The amygdala (AMY) is widely implicated in fear learning and fear behaviour, but it remains unclear how the many biological components present within AMY interact to achieve these abilities. Building on previous work, we hypothesize that individual AMY nuclei represent different quantities and that fear conditioning arises from error-driven learning on the synapses between AMY nuclei. We present a computational model of AMY that (a) recreates the divisions and connections between AMY nuclei and their constituent pyramidal and inhibitory neurons; (b) accommodates scalable high-dimensional representations of external stimuli; (c) learns to associate complex stimuli with the presence (or absence) of an aversive stimulus; (d) preserves feature information when mapping inputs to salience estimates, such that these estimates generalize to similar stimuli; and (e) induces a diverse profile of neural responses within each nucleus. Our model predicts (1) defensive responses and neural activities in several experimental conditions, (2) the consequence of artificially ablating particular nuclei and (3) the tendency to generalize defensive responses to novel stimuli. We test these predictions by comparing model outputs to neural and behavioural data from animals and humans. Despite the relative simplicity of our model, we find significant overlap between simulated and empirical data, which supports our claim that the model captures many of the neural mechanisms that support fear conditioning. We conclude by comparing our model to other computational models and by characterizing the theoretical relationship between pattern separation and fear generalization in healthy versus anxious individuals.

Multisensory Integration Underlies the Distinct Representation of Odor-Taste Mixtures in the Gustatory Cortex of Behaving Rats

The perception of food relies on the integration of olfactory and gustatory signals originating from the mouth. This multisensory process generates robust associations between odors and tastes, significantly influencing the perceptual judgment of flavors. However, the specific neural substrates underlying this integrative process remain unclear. Previous electrophysiological studies identified the gustatory cortex as a site of convergent olfactory and gustatory signals, but whether neurons represent multimodal odor-taste mixtures as distinct from their unimodal odor and taste components is unknown. To investigate this, we recorded single-unit activity in the gustatory cortex of behaving female rats during the intraoral delivery of individual odors, individual tastes, and odor-taste mixtures. Our results demonstrate that chemoselective neurons in the gustatory cortex are broadly responsive to intraoral chemosensory stimuli, exhibiting time-varying multiphasic changes in activity. In a subset of these chemoselective neurons, odor-taste mixtures elicit nonlinear cross-modal responses that distinguish them from their olfactory and gustatory components. These findings provide novel insights into multimodal chemosensory processing by the gustatory cortex, highlighting the distinct representation of unimodal and multimodal intraoral chemosensory signals. Overall, our findings suggest that olfactory and gustatory signals interact nonlinearly in the gustatory cortex to enhance the identity coding of both unimodal and multimodal chemosensory stimuli.

Neural correlates of rapid familiarization to novel taste

The gustatory cortex (GC) plays a pivotal role in taste perception, with neural ensemble responses reflecting taste quality and influencing behavior. Recent work, however, has shown that GC taste responses change across sessions of novel taste exposure in taste-naïve rats. Here, we use single-trial analyses to explore changes in the cortical taste-code on the scale of individual trials. Contrary to the traditional view of taste perception as innate, our findings suggest rapid, experience-dependent changes in GC responses during initial taste exposure trials. Specifically, we find that early responses to novel taste are less "stereotyped" and encode taste identity less reliably compared to later responses. These changes underscore the dynamic nature of sensory processing and provides novel insights into the real-time dynamics of sensory processing across novel-taste familiarization.

Learning enhances representations of taste-guided decisions in the mouse gustatory insular cortex

Learning to discriminate overlapping gustatory stimuli that predict distinct outcomes-a feat known as discrimination learning-can mean the difference between ingesting a poison or a nutritive meal. Despite the obvious importance of this process, very little is known about the neural basis of taste discrimination learning. In other sensory modalities, this form of learning can be mediated by either the sharpening of sensory representations or the enhanced ability of "decision-making" circuits to interpret sensory information. Given the dual role of the gustatory insular cortex (GC) in encoding both sensory and decision-related variables, this region represents an ideal site for investigating how neural activity changes as animals learn a novel taste discrimination. Here, we present results from experiments relying on two-photon calcium imaging of GC neural activity in mice performing a taste-guided mixture discrimination task. The task allows for the recording of neural activity before and after learning induced by training mice to discriminate increasingly similar pairs of taste mixtures. Single-neuron and population analyses show a time-varying pattern of activity, with early sensory responses emerging after taste delivery and binary, choice-encoding responses emerging later in the delay before a decision is made. Our results demonstrate that, while both sensory and decision-related information is encoded by GC in the context of a taste mixture discrimination task, learning and improved performance are associated with a specific enhancement of decision-related responses.

Taking time to compose thoughts with prefrontal schemata

Under what conditions can prefrontal cortex direct the composition of brain states, to generate coherent streams of thoughts? Using a simplified Potts model of cortical dynamics, crudely differentiated into two halves, we show that once activity levels are regulated, so as to disambiguate a single temporal sequence, whether the contents of the sequence are mainly determined by the frontal or by the posterior half, or by neither, depends on statistical parameters that describe its microcircuits. The frontal cortex tends to lead if it has more local attractors, longer lasting and stronger ones, in order of increasing importance. Its guidance is particularly effective to the extent that posterior cortices do not tend to transition from state to state on their own. The result may be related to prefrontal cortex enforcing its temporally-oriented schemata driving coherent sequences of brain states, unlike the atemporal "context" contributed by the hippocampus. Modelling a mild prefrontal (vs. posterior) lesion offers an account of mind-wandering and event construction deficits observed in prefrontal patients.

Stereotyped goal-directed manifold dynamics in the insular cortex

The insular cortex is involved in diverse processes, including bodily homeostasis, emotions, and cognition. However, we lack a comprehensive understanding of how it processes information at the level of neuronal populations. We leveraged recent advances in unsupervised machine learning to study insular cortex population activity patterns (i.e., neuronal manifold) in mice performing goal-directed behaviors. We find that the insular cortex activity manifold is remarkably consistent across different animals and under different motivational states. Activity dynamics within the neuronal manifold are highly stereotyped during rewarded trials, enabling robust prediction of single-trial outcomes across different mice and across various natural and artificial motivational states. Comparing goal-directed behavior with self-paced free consumption, we find that the stereotyped activity patterns reflect task-dependent goal-directed reward anticipation, and not licking, taste, or positive valence. These findings reveal a core computation in insular cortex that could explain its involvement in pathologies involving aberrant motivations.

A machine learning approach for real-time cortical state estimation

Objective.Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as 'cortical state'. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation.Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments.Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data.Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

The role of the mediodorsal thalamus in chemosensory preference and consummatory behavior in rats

Experience plays a pivotal role in determining our food preferences. Consuming food generates odor-taste associations that shape our perceptual judgements of chemosensory stimuli, such as their intensity, familiarity, and pleasantness. The process of making consummatory choices relies on a network of brain regions to integrate and process chemosensory information. The mediodorsal thalamus is a higher-order thalamic nucleus involved in many experience-dependent chemosensory behaviors, including olfactory attention, odor discrimination, and the hedonic perception of flavors. Recent research has shown that neurons in the mediodorsal thalamus represent the sensory and affective properties of experienced odors, tastes, and odor-taste mixtures. However, its role in guiding consummatory choices remains unclear. To investigate the influence of the mediodorsal thalamus in the consummatory choice for experienced odors, tastes, and odor-taste mixtures, we pharmacologically inactivated the mediodorsal thalamus during 2-bottle brief-access tasks. We found that inactivation altered the preference for specific odor-taste mixtures, significantly reduced consumption of the preferred taste and increased within-trial sampling of both chemosensory stimulus options. Our results show that the mediodorsal thalamus plays a crucial role in consummatory decisions related to chemosensory preference and attention.

A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies

The temporal activity of many physical and biological systems, from complex networks to neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. Long-tailed distributions of intrinsic timescales have been observed across neurons simultaneously recorded within the same cortical circuit. The mechanisms leading to this striking temporal heterogeneity are yet unknown. Here, we show that neural circuits, endowed with heterogeneous neural assemblies of different sizes, naturally generate multiple timescales of activity spanning several orders of magnitude. We develop an analytical theory using rate networks, supported by simulations of spiking networks with cell-type specific connectivity, to explain how neural timescales depend on assembly size and show that our model can naturally explain the long-tailed timescale distribution observed in the awake primate cortex. When driving recurrent networks of heterogeneous neural assemblies by a time-dependent broadband input, we found that large and small assemblies preferentially entrain slow and fast spectral components of the input, respectively. Our results suggest that heterogeneous assemblies can provide a biologically plausible mechanism for neural circuits to demix complex temporal input signals by transforming temporal into spatial neural codes via frequency-selective neural assemblies.

Multistability in neural systems with random cross-connections

Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems using a firing rate model framework, in which clusters of similarly responsive neurons are represented as single units, which interact with each other through independent random connections. We explore the range of conditions in which multistability arises via recurrent input from other units while individual units, typically with some degree of self-excitation, lack sufficient self-excitation to become bistable on their own. We find many cases of multistability-defined as the system possessing more than one stable fixed point-in which stable states arise via a network effect, allowing subsets of units to maintain each others' activity because their net input to each other when active is sufficiently positive. In terms of the strength of within-unit self-excitation and standard deviation of random cross-connections, the region of multistability depends on the response function of units. Indeed, multistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the response function rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which produces Zipf's Law when enumerating the proportion of trials within which random initial conditions lead to a particular stable state of the system.

Inhibitory Gating of Thalamocortical Inputs onto Rat Gustatory Insular Cortex

In primary gustatory cortex (GC), a subregion of the insular cortex, neurons show anticipatory activity, encode taste identity and palatability, and their activity is related to decision-making. Inactivation of the gustatory thalamus, the parvicellular region of the ventral posteromedial thalamic nucleus (VPMpc), dramatically reduces GC taste responses, consistent with the hypothesis that VPMpc-GC projections carry taste information. Recordings in awake rodents reported that taste-responsive neurons can be found across GC, without segregated spatial mapping, raising the possibility that projections from the taste thalamus may activate GC broadly. In addition, we have shown that cortical inhibition modulates the integration of thalamic and limbic inputs, revealing a potential role for GABA transmission in gating sensory information to GC. Despite this wealth of information at the system level, the synaptic organization of the VPMpc-GC circuit has not been investigated. Here, we used optogenetic activation of VPMpc afferents to GC in acute slice preparations from rats of both sexes to investigate the synaptic properties and organization of VPMpc afferents in GC and their modulation by cortical inhibition. We hypothesized that VPMpc-GC synapses are distributed across GC, but show laminar- and cell-specific properties, conferring computationally flexibility to how taste information is processed. We also found that VPMpc-GC synaptic responses are strongly modulated by the activity regimen of VPMpc afferents, as well as by cortical inhibition activating GABAA and GABAB receptors onto VPMpc terminals. These results provide a novel insight into the complex features of thalamocortical circuits for taste processing.SIGNIFICANCE STATEMENT We report that the input from the primary taste thalamus to the primary gustatory cortex (GC) shows distinct properties compared with primary thalamocortical synapses onto other sensory areas. Ventral posteromedial thalamic nucleus afferents in GC make synapses with excitatory neurons distributed across all cortical layers and display frequency-dependent short-term plasticity to repetitive stimulation; thus, they do not fit the classic distinction between drivers and modulators typical of other sensory thalamocortical circuits. Thalamocortical activation of GC is gated by cortical inhibition, providing local corticothalamic feedback via presynaptic ionotropic and metabotropic GABA receptors. The connectivity and inhibitory control of thalamocortical synapses in GC highlight unique features of the thalamocortical circuit for taste.

Learning enhances representations of taste-guided decisions in the mouse gustatory insular cortex

Learning to discriminate overlapping gustatory stimuli that predict distinct outcomes - a feat known as discrimination learning - can mean the difference between ingesting a poison or a nutritive meal. Despite the obvious importance of this process, very little is known on the neural basis of taste discrimination learning. In other sensory modalities, this form of learning can be mediated by either sharpening of sensory representations, or enhanced ability of "decision-making" circuits to interpret sensory information. Given the dual role of the gustatory insular cortex (GC) in encoding both sensory and decision-related variables, this region represents an ideal site for investigating how neural activity changes as animals learn a novel taste discrimination. Here we present results from experiments relying on two photon calcium imaging of GC neural activity in mice performing a taste-guided mixture discrimination task. The task allows for recording of neural activity before and after learning induced by training mice to discriminate increasingly similar pairs of taste mixtures. Single neuron and population analyses show a time-varying pattern of activity, with early sensory responses emerging after taste delivery and binary, choice encoding responses emerging later in the delay before a decision is made. Our results demonstrate that while both sensory and decision-related information is encoded by GC in the context of a taste mixture discrimination task, learning and improved performance are associated with a specific enhancement of decision-related responses.

Multistability in neural systems with random cross-connections

Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems, using a firing-rate model framework, in which clusters of neurons with net self-excitation are represented as units, which interact with each other through random connections. We focus on conditions in which individual units lack sufficient self-excitation to become bistable on their own. Rather, multistability can arise via recurrent input from other units as a network effect for subsets of units, whose net input to each other when active is sufficiently positive to maintain such activity. In terms of the strength of within-unit self-excitation and standard-deviation of random cross-connections, the region of multistability depends on the firing-rate curve of units. Indeed, bistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the firing-rate curve rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which can appear as Zipf's Law when sampled as the proportion of trials within which random initial conditions lead to a particular stable state of the system.

Taste

In his book on the ecology of perception (An Immense World: How Animal Senses Reveal the Hidden, 2022), science writer Ed Yong emphasizes the simplicity of taste. He writes: "Taste, then, is the simpler sense. As we've seen, smell covers a practically infinite selection of molecules with an indescribably vast range of characteristics, which the nervous system represents through a combinatorial code so fiendish that scientists have barely begun to crack it. Taste, by contrast, boils down to just five basic qualities in humans - salt, sweet, bitter, sour, and umami (savory) - and perhaps a few more in other animals, which are detected through a small number of receptors. And while smell can be put to complex uses - navigating the open oceans, finding prey, and coordinating herds or colonies - taste is almost always used to make binary decisions about food. Yes or no? Good or bad? Consume or spit? It's ironic that we associate taste with connoisseurship, subtlety and fine discrimination when it is among the coarsest of senses."

Temporal progression along discrete coding states during decision-making in the mouse gustatory cortex

The mouse gustatory cortex (GC) is involved in taste-guided decision-making in addition to sensory processing. Rodent GC exhibits metastable neural dynamics during ongoing and stimulus-evoked activity, but how these dynamics evolve in the context of a taste-based decision-making task remains unclear. Here we employ analytical and modeling approaches to i) extract metastable dynamics in ensemble spiking activity recorded from the GC of mice performing a perceptual decision-making task; ii) investigate the computational mechanisms underlying GC metastability in this task; and iii) establish a relationship between GC dynamics and behavioral performance. Our results show that activity in GC during perceptual decision-making is metastable and that this metastability may serve as a substrate for sequentially encoding sensory, abstract cue, and decision information over time. Perturbations of the model's metastable dynamics indicate that boosting inhibition in different coding epochs differentially impacts network performance, explaining a counterintuitive effect of GC optogenetic silencing on mouse behavior.

Coupled Dynamics of Stimulus-Evoked Gustatory Cortical and Basolateral Amygdalar Activity

Gustatory cortical (GC) single-neuron taste responses reflect taste quality and palatability in successive epochs. Ensemble analyses reveal epoch-to-epoch firing-rate changes in these responses to be sudden, coherent transitions. Such nonlinear dynamics suggest that GC is part of a recurrent network, producing these dynamics in concert with other structures. Basolateral amygdala (BLA), which is reciprocally connected to GC and central to hedonic processing, is a strong candidate partner for GC, in that BLA taste responses evolve on the same general clock as GC and because inhibition of activity in the BLA→GC pathway degrades the sharpness of GC transitions. These facts motivate, but do not test, our overarching hypothesis that BLA and GC act as a single, comodulated network during taste processing. Here, we provide just this test of simultaneous (BLA and GC) extracellular taste responses in female rats, probing the multiregional dynamics of activity to directly test whether BLA and GC responses contain coupled dynamics. We show that BLA and GC response magnitudes covary across trials and within single responses, and that changes in BLA-GC local field potential phase coherence are epoch specific. Such classic coherence analyses, however, obscure the most salient facet of BLA-GC coupling: sudden transitions in and out of the epoch known to be involved in driving gaping behavior happen near simultaneously in the two regions, despite huge trial-to-trial variability in transition latencies. This novel form of inter-regional coupling, which we show is easily replicated in model networks, suggests collective processing in a distributed neural network.SIGNIFICANCE STATEMENT There has been little investigation into real-time communication between brain regions during taste processing, a fact reflecting the dominant belief that taste circuitry is largely feedforward. Here, we perform an in-depth analysis of real-time interactions between GC and BLA in response to passive taste deliveries, using both conventional coherence metrics and a novel methodology that explicitly considers trial-to-trial variability and fast single-trial dynamics in evoked responses. Our results demonstrate that BLA-GC coherence changes as the taste response unfolds, and that BLA and GC specifically couple for the sudden transition into (and out of) the behaviorally relevant neural response epoch, suggesting (although not proving) that: (1) recurrent interactions subserve the function of the dyad as (2) a putative attractor network.

One dimensional approximations of neuronal dynamics reveal computational strategy

The relationship between neuronal activity and computations embodied by it remains an open question. We develop a novel methodology that condenses observed neuronal activity into a quantitatively accurate, simple, and interpretable model and validate it on diverse systems and scales from single neurons in C. elegans to fMRI in humans. The model treats neuronal activity as collections of interlocking 1-dimensional trajectories. Despite their simplicity, these models accurately predict future neuronal activity and future decisions made by human participants. Moreover, the structure formed by interconnected trajectories-a scaffold-is closely related to the computational strategy of the system. We use these scaffolds to compare the computational strategy of primates and artificial systems trained on the same task to identify specific conditions under which the artificial agent learns the same strategy as the primate. The computational strategy extracted using our methodology predicts specific errors on novel stimuli. These results show that our methodology is a powerful tool for studying the relationship between computation and neuronal activity across diverse systems.

Neural Representation of Intraoral Olfactory and Gustatory Signals by the Mediodorsal Thalamus in Alert Rats

The mediodorsal thalamus is a multimodal region involved in a variety of cognitive behaviors, including olfactory attention, odor discrimination, and the hedonic perception of flavors. Although the mediodorsal thalamus forms connections with principal regions of the olfactory and gustatory networks, its role in processing olfactory and gustatory signals originating from the mouth remains unclear. Here, we recorded single-unit activity in the mediodorsal thalamus of behaving female rats during the intraoral delivery of individual odors, individual tastes, and odor-taste mixtures. Our results are the first to demonstrate that neurons in the mediodorsal thalamus dynamically encode chemosensory signals originating from the mouth. This chemoselective population is broadly tuned, exhibits excited and suppressed responses, and responds to odor-taste mixtures differently than an odor or taste alone. Furthermore, a subset of chemoselective neurons encodes the palatability-related features of tastes and may represent associations between previously experienced odor-taste pairs. Our results further demonstrate the multidimensionality of the mediodorsal thalamus and provide additional evidence of its involvement in processing chemosensory information important for ingestive behaviors.SIGNIFICANCE STATEMENT The perception of food relies on the concurrent processing of olfactory and gustatory signals originating from the mouth. The mediodorsal thalamus is a higher-order thalamic nucleus involved in a variety of chemosensory-dependent behaviors and connects the olfactory and gustatory cortices with the prefrontal cortex. However, it is unknown how neurons in the mediodorsal thalamus process intraoral chemosensory signals. Using tetrode recordings in alert rats, our results are the first to show that neurons in the mediodorsal thalamus dynamically represent olfactory and gustatory signals from the mouth. Our findings are consistent with the mediodorsal thalamus being a key node between sensory and prefrontal cortical areas for processing chemosensory information underlying ingestive behavior.

Neural mechanisms underlying the temporal organization of naturalistic animal behavior

Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.

LiCl-induced sickness modulates rat gustatory cortical responses

Gustatory cortex (GC), a structure deeply involved in the making of consumption decisions, presumably performs this function by integrating information about taste, experiences, and internal states related to the animal's health, such as illness. Here, we investigated this assertion, examining whether illness is represented in GC activity, and how this representation impacts taste responses and behavior. We recorded GC single-neuron activity and local field potentials (LFPs) from healthy rats and rats made ill (via LiCl injection). We show (consistent with the extant literature) that the onset of illness-related behaviors arises contemporaneously with alterations in 7 to 12 Hz LFP power at approximately 12 min following injection. This process was accompanied by reductions in single-neuron taste response magnitudes and discriminability, and with enhancements in palatability-relatedness-a result reflecting the collapse of responses toward a simple "good-bad" code visible in the entire sample, but focused on a specific subset of GC neurons. Overall, our data show that a state (illness) that profoundly reduces consumption changes basic properties of the sensory cortical response to tastes, in a manner that can easily explain illness' impact on consumption.

Metastable dynamics of neural circuits and networks

Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.

Network Analysis of Time Series: Novel Approaches to Network Neuroscience

In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

Neural Circuits for Dynamics-Based Segmentation of Time Series

The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on data sets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series.

Metastable attractors explain the variable timing of stable behavioral action sequences

The timing of self-initiated actions shows large variability even when they are executed in stable, well-learned sequences. Could this mix of reliability and stochasticity arise within the same neural circuit? We trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), which is known to reflect trial-by-trial action-timing fluctuations. Using hidden Markov models, we established a dictionary between activity patterns and actions. We then showed that metastable attractors, representing activity patterns with a reliable sequential structure and large transition timing variability, could be produced by reciprocally coupling a high-dimensional recurrent network and a low-dimensional feedforward one. Transitions between attractors relied on correlated variability in this mesoscale feedback loop, predicting a specific structure of low-dimensional correlations that were empirically verified in M2 recordings. Our results suggest a novel mesoscale network motif based on correlated variability supporting naturalistic animal behavior.

Asymmetric and transient properties of reciprocal activity of antagonists during the paw-shake response in the cat

Mutually inhibitory populations of neurons, half-center oscillators (HCOs), are commonly involved in the dynamics of the central pattern generators (CPGs) driving various rhythmic movements. Previously, we developed a multifunctional, multistable symmetric HCO model which produced slow locomotor-like and fast paw-shake-like activity patterns. Here, we describe asymmetric features of paw-shake responses in a symmetric HCO model and test these predictions experimentally. We considered bursting properties of the two model half-centers during transient paw-shake-like responses to short perturbations during locomotor-like activity. We found that when a current pulse was applied during the spiking phase of one half-center, let’s call it #1, the consecutive burst durations (BDs) of that half-center increased throughout the paw-shake response, while BDs of the other half-center, let’s call it #2, only changed slightly. In contrast, the consecutive interburst intervals (IBIs) of half-center #1 changed little, while IBIs of half-center #2 increased. We demonstrated that this asymmetry between the half-centers depends on the phase of the locomotor-like rhythm at which the perturbation was applied. We suggest that the fast transient response reflects functional asymmetries of slow processes that underly the locomotor-like pattern; e.g., asymmetric levels of inactivation across the two half-centers for a slowly inactivating inward current. We compared model results with those of in-vivo paw-shake responses evoked in locomoting cats and found similar asymmetries. Electromyographic (EMG) BDs of anterior hindlimb muscles with flexor-related activity increased in consecutive paw-shake cycles, while BD of posterior muscles with extensor-related activity did not change, and vice versa for IBIs of anterior flexors and posterior extensors. We conclude that EMG activity patterns during paw-shaking are consistent with the proposed mechanism producing transient paw-shake-like bursting patterns found in our multistable HCO model. We suggest that the described asymmetry of paw-shaking responses could implicate a multifunctional CPG controlling both locomotion and paw-shaking.

The existence of multifunctional central pattern generators (CPGs), circuits which control more than one rhythmic motor behavior, is an intriguing hypothesis. We suggest that the cat paw-shaking response could be a transient response of the locomotor CPG. Our general prediction is that this CPG is multifunctional, and in addition to the locomotor rhythm, it can generate a transient, ten-times faster, paw-shake-like response to a stimulus. In our multistable half-center oscillator (HCO) CPG model, we applied perturbations to the locomotor pattern which resulted in a transient paw-shake-like pattern that eventually returned back to the locomotor pattern. We showed that the inactivation of the slow inward current that drives the locomotor rhythm produced asymmetry of the transient flexor and extensor activity in a symmetric HCO model. To test predictions from our model about the transient nature of the paw-shake response, we compared burst durations (BDs) and interburst intervals (IBIs) of the model half-centers in consecutive cycles of paw-shake-like responses with the BD and IBI of electromyographic (EMG) activity bursts of cat hindlimb flexors and extensors recorded during a paw-shake response. In both cases, we found similar asymmetric trends in the BD and IBI throughout a paw-shake response.

Multiple-Timescale Neural Networks: Generation of History-Dependent Sequences and Inference Through Autonomous Bifurcations

Sequential transitions between metastable states are ubiquitously observed in the neural system and underlying various cognitive functions such as perception and decision making. Although a number of studies with asymmetric Hebbian connectivity have investigated how such sequences are generated, the focused sequences are simple Markov ones. On the other hand, fine recurrent neural networks trained with supervised machine learning methods can generate complex non-Markov sequences, but these sequences are vulnerable against perturbations and such learning methods are biologically implausible. How stable and complex sequences are generated in the neural system still remains unclear. We have developed a neural network with fast and slow dynamics, which are inspired by the hierarchy of timescales on neural activities in the cortex. The slow dynamics store the history of inputs and outputs and affect the fast dynamics depending on the stored history. We show that the learning rule that requires only local information can form the network generating the complex and robust sequences in the fast dynamics. The slow dynamics work as bifurcation parameters for the fast one, wherein they stabilize the next pattern of the sequence before the current pattern is destabilized depending on the previous patterns. This co-existence period leads to the stable transition between the current and the next pattern in the non-Markov sequence. We further find that timescale balance is critical to the co-existence period. Our study provides a novel mechanism generating robust complex sequences with multiple timescales. Considering the multiple timescales are widely observed, the mechanism advances our understanding of temporal processing in the neural system.

Cortical Hub for Flavor Sensation in Rodents

The experience of eating is inherently multimodal, combining intraoral gustatory, olfactory, and somatosensory signals into a single percept called flavor. As foods and beverages enter the mouth, movements associated with chewing and swallowing activate somatosensory receptors in the oral cavity, dissolve tastants in the saliva to activate taste receptors, and release volatile odorant molecules to retronasally activate olfactory receptors in the nasal epithelium. Human studies indicate that sensory cortical areas are important for intraoral multimodal processing, yet their circuit-level mechanisms remain unclear. Animal models allow for detailed analyses of neural circuits due to the large number of molecular tools available for tracing and neuronal manipulations. In this review, we concentrate on the anatomical and neurophysiological evidence from rodent models toward a better understanding of the circuit-level mechanisms underlying the cortical processing of flavor. While more work is needed, the emerging view pertaining to the multimodal processing of food and beverages is that the piriform, gustatory, and somatosensory cortical regions do not function solely as independent areas. Rather they act as an intraoral cortical hub, simultaneously receiving and processing multimodal sensory information from the mouth to produce the rich and complex flavor experience that guides consummatory behavior.

Cellular activity in insular cortex across seconds to hours: Sensations and predictions of bodily states

Our wellness relies on continuous interactions between our brain and body: different organs relay their current state to the brain and are regulated, in turn, by descending visceromotor commands from our brain and by actions such as eating, drinking, thermotaxis, and predator escape. Human neuroimaging and theoretical studies suggest a key role for predictive processing by insular cortex in guiding these efforts to maintain bodily homeostasis. Here, we review recent studies recording and manipulating cellular activity in rodent insular cortex at timescales from seconds to hours. We argue that consideration of these findings in the context of predictive processing of future bodily states may reconcile several apparent discrepancies and offer a unifying, heuristic model for guiding future work.

The population doctrine in cognitive neuroscience

A major shift is happening within neurophysiology: a population doctrine is drawing level with the single-neuron doctrine that has long dominated the field. Population-level ideas have so far had their greatest impact in motor neuroscience, but they hold great promise for resolving open questions in cognition as well. Here, we codify the population doctrine and survey recent work that leverages this view to specifically probe cognition. Our discussion is organized around five core concepts that provide a foundation for population-level thinking: (1) state spaces, (2) manifolds, (3) coding dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the progress and promise that population-level thinking holds for cognitive neuroscience-for delivering new insight into attention, working memory, decision-making, executive function, learning, and reward processing.

A model of naturalistic decision making in preference tests

Decisions as to whether to continue with an ongoing activity or to switch to an alternative are a constant in an animal’s natural world, and in particular underlie foraging behavior and performance in food preference tests. Stimuli experienced by the animal both impact the choice and are themselves impacted by the choice, in a dynamic back and forth. Here, we present model neural circuits, based on spiking neurons, in which the choice to switch away from ongoing behavior instantiates this back and forth, arising as a state transition in neural activity. We analyze two classes of circuit, which differ in whether state transitions result from a loss of hedonic input from the stimulus (an “entice to stay” model) or from aversive stimulus-input (a “repel to leave” model). In both classes of model, we find that the mean time spent sampling a stimulus decreases with increasing value of the alternative stimulus, a fact that we linked to the inclusion of depressing synapses in our model. The competitive interaction is much greater in “entice to stay” model networks, which has qualitative features of the marginal value theorem, and thereby provides a framework for optimal foraging behavior. We offer suggestions as to how our models could be discriminatively tested through the analysis of electrophysiological and behavioral data.

Many decisions are of the ilk of whether to continue sampling a stimulus or to switch to an alternative, a key feature of foraging behavior. We produce two classes of model for such stay-switch decisions, which differ in how decisions to switch stimuli can arise. In an “entice-to-stay” model, a reduction in the necessary positive stimulus input causes switching decisions. In a “repel-to-leave” model, a rise in aversive stimulus input produces a switch decision. We find that in tasks where the sampling of one stimulus follows another, adaptive biological processes arising from a highly hedonic stimulus can reduce the time spent at the following stimulus, by up to ten-fold in the “entice-to-stay” models. Along with potentially observable behavioral differences that could distinguish the classes of networks, we also found signatures in neural activity, such as oscillation of neural firing rates and a rapid change in rates preceding the time of choice to leave a stimulus. In summary, our model findings lead to testable predictions and suggest a neural circuit-based framework for explaining foraging choices.

Perturbation of amygdala-cortical projections reduces ensemble coherence of palatability coding in gustatory cortex

Taste palatability is centrally involved in consumption decisions-we ingest foods that taste good and reject those that don't. Gustatory cortex (GC) and basolateral amygdala (BLA) almost certainly work together to mediate palatability-driven behavior, but the precise nature of their interplay during taste decision-making is still unknown. To probe this issue, we discretely perturbed (with optogenetics) activity in rats' BLA→GC axons during taste deliveries. This perturbation strongly altered GC taste responses, but while the perturbation itself was tonic (2.5 s), the alterations were not-changes preferentially aligned with the onset times of previously-described taste response epochs, and reduced evidence of palatability-related activity in the 'late-epoch' of the responses without reducing the amount of taste identity information available in the 'middle epoch.' Finally, BLA→GC perturbations changed behavior-linked taste response dynamics themselves, distinctively diminishing the abruptness of ensemble transitions into the late epoch. These results suggest that BLA 'organizes' behavior-related GC taste dynamics.

Neuronal Sequence Models for Bayesian Online Inference

Various imaging and electrophysiological studies in a number of different species and brain regions have revealed that neuronal dynamics associated with diverse behavioral patterns and cognitive tasks take on a sequence-like structure, even when encoding stationary concepts. These neuronal sequences are characterized by robust and reproducible spatiotemporal activation patterns. This suggests that the role of neuronal sequences may be much more fundamental for brain function than is commonly believed. Furthermore, the idea that the brain is not simply a passive observer but an active predictor of its sensory input, is supported by an enormous amount of evidence in fields as diverse as human ethology and physiology, besides neuroscience. Hence, a central aspect of this review is to illustrate how neuronal sequences can be understood as critical for probabilistic predictive information processing, and what dynamical principles can be used as generators of neuronal sequences. Moreover, since different lines of evidence from neuroscience and computational modeling suggest that the brain is organized in a functional hierarchy of time scales, we will also review how models based on sequence-generating principles can be embedded in such a hierarchy, to form a generative model for recognition and prediction of sensory input. We shortly introduce the Bayesian brain hypothesis as a prominent mathematical description of how online, i.e., fast, recognition, and predictions may be computed by the brain. Finally, we briefly discuss some recent advances in machine learning, where spatiotemporally structured methods (akin to neuronal sequences) and hierarchical networks have independently been developed for a wide range of tasks. We conclude that the investigation of specific dynamical and structural principles of sequential brain activity not only helps us understand how the brain processes information and generates predictions, but also informs us about neuroscientific principles potentially useful for designing more efficient artificial neuronal networks for machine learning tasks.

State-Dependent Regulation of Cortical Processing Speed via Gain Modulation

To thrive in dynamic environments, animals must be capable of rapidly and flexibly adapting behavioral responses to a changing context and internal state. Examples of behavioral flexibility include faster stimulus responses when attentive and slower responses when distracted. Contextual or state-dependent modulations may occur early in the cortical hierarchy and may be implemented via top-down projections from corticocortical or neuromodulatory pathways. However, the computational mechanisms mediating the effects of such projections are not known. Here, we introduce a theoretical framework to classify the effects of cell type-specific top-down perturbations on the information processing speed of cortical circuits. Our theory demonstrates that perturbation effects on stimulus processing can be predicted by intrinsic gain modulation, which controls the timescale of the circuit dynamics. Our theory leads to counterintuitive effects, such as improved performance with increased input variance. We tested the model predictions using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where we found that a decrease in single-cell intrinsic gain during locomotion led to an acceleration of visual processing. Our results establish a novel theory of cell type-specific perturbations, applicable to top-down modulation as well as optogenetic and pharmacological manipulations. Our theory links connectivity, dynamics, and information processing via gain modulation.SIGNIFICANCE STATEMENT To thrive in dynamic environments, animals adapt their behavior to changing circumstances and different internal states. Examples of behavioral flexibility include faster responses to sensory stimuli when attentive and slower responses when distracted. Previous work suggested that contextual modulations may be implemented via top-down inputs to sensory cortex coming from higher brain areas or neuromodulatory pathways. Here, we introduce a theory explaining how the speed at which sensory cortex processes incoming information is adjusted by changes in these top-down projections, which control the timescale of neural activity. We tested our model predictions in freely running mice, revealing that locomotion accelerates visual processing. Our theory is applicable to internal modulation as well as optogenetic and pharmacological manipulations and links circuit connectivity, dynamics, and information processing.

ApoE4 attenuates cortical neuronal activity in young behaving apoE4 rats

The E4 allele of apolipoprotein E (apoE4) is the strongest genetic risk factor for late-onset Alzheimer's disease (AD). However, apoE4 may cause innate brain abnormalities before the appearance of AD-related neuropathology. Understanding these primary dysfunctions is vital for the early detection of AD and the development of therapeutic strategies. Recently we reported impaired extra-hippocampal memory in young apoE4 mice, a deficit that was correlated with attenuated structural pre-synaptic plasticity in cortical and subcortical regions. Here we tested the hypothesis that these early structural deficits impact learning via changes in basal and stimuli evoked neuronal activity. We recorded extracellular neuronal activity from the gustatory cortex (GC) of three-month-old humanized apoE4 (hApoE4) and wildtype rats expressing rat apoE (rAE), before and after conditioned taste aversion (CTA) training. Despite normal sucrose drinking behavior before CTA, young hApoE4 rats showed impaired CTA learning, consistent with our previous results in target-replacement apoE4 mice. This behavioral deficit was correlated with decreased basal and taste-evoked firing rates in both putative excitatory and inhibitory GC neurons. Further taste coding analyses at the single neuron and ensemble levels revealed that GC neurons of the hApoE4 group correctly classified tastes, but were unable to undergo plasticity to support learning. These results suggest that apoE4 impacts brain excitability and plasticity early in life that may act as an initiator for later AD pathologies.

Slower prefrontal metastable dynamics during deliberation predicts error trials in a distance discrimination task

Cortical activity related to erroneous behavior in discrimination or decision-making tasks is rarely analyzed, yet it can help clarify which computations are essential during a specific task. Here, we use a hidden Markov model (HMM) to perform a trial-by-trial analysis of the ensemble activity of dorsolateral prefrontal cortex (PFdl) neurons of rhesus monkeys performing a distance discrimination task. By segmenting the neural activity into sequences of metastable states, HMM allows us to uncover modulations of the neural dynamics related to internal computations. We find that metastable dynamics slow down during error trials, while state transitions at a pivotal point during the trial take longer in difficult correct trials. Both these phenomena occur during the decision interval, with errors occurring in both easy and difficult trials. Our results provide further support for the emerging role of metastable cortical dynamics in mediating complex cognitive functions and behavior.

Neural Coding of Food Is a Multisensory, Sensorimotor Function

This review is a curated discussion of the relationship between the gustatory system and the perception of food beginning at the earliest stage of neural processing. A brief description of the idea of taste qualities and mammalian anatomy of the taste system is presented first, followed by an overview of theories of taste coding. The case is made that food is encoded by the several senses that it stimulates beginning in the brainstem and extending throughout the entire gustatory neuraxis. In addition, the feedback from food-related movements is seamlessly melded with sensory input to create the representation of food objects in the brain.

Nonlinear control of networked dynamical systems

We develop a principled mathematical framework for controlling nonlinear, networked dynamical systems. Our method integrates dimensionality reduction, bifurcation theory, and emerging model discovery tools to find low-dimensional subspaces where feed-forward control can be used to manipulate a system to a desired outcome. The method leverages the fact that many high-dimensional networked systems have many fixed points, allowing for the computation of control signals that will move the system between any pair of fixed points. The sparse identification of nonlinear dynamics (SINDy) algorithm is used to fit a nonlinear dynamical system to the evolution on the dominant, low-rank subspace. This then allows us to use bifurcation theory to find collections of constant control signals that will produce the desired objective path for a prescribed outcome. Specifically, we can destabilize a given fixed point while making the target fixed point an attractor. The discovered control signals can be easily projected back to the original high-dimensional state and control space. We illustrate our nonlinear control procedure on established bistable, low-dimensional biological systems, showing how control signals are found that generate switches between the fixed points. We then demonstrate our control procedure for high-dimensional systems on random high-dimensional networks and Hopfield memory networks.

Layer- and Cell Type-Specific Response Properties of Gustatory Cortex Neurons in Awake Mice

Studies in visual, auditory, and somatosensory cortices have revealed that different cell types as well as neurons located in different laminae display distinct stimulus response profiles. The extent to which these layer and cell type-specific distinctions generalize to gustatory cortex (GC) remains unknown. In this study, we performed extracellular recordings in adult female mice to monitor the activity of putative pyramidal and inhibitory neurons located in deep and superficial layers of GC. Awake, head-restrained mice were trained to lick different tastants (sucrose, salt, citric acid, quinine, and water) from a lick spout. We found that deep layer neurons show higher baseline firing rates (FRs) in GC with deep-layer inhibitory neurons displaying highest FRs at baseline and following the stimulus. GC's activity shows robust modulations before animals' contact with tastants, and this phenomenon is most prevalent in deep-layer inhibitory neurons. Furthermore, we show that licking activity strongly shapes the spiking pattern of GC pyramidal neurons, eliciting phase-locked spiking across trials and tastants. We demonstrate that there is a greater percentage of taste-coding neurons in deep versus superficial layers with chemosensitive neurons across all categories showing similar breadth of tuning, but different decoding performance. Lastly, we provide evidence for functional convergence in GC, with neurons that can show prestimulus activity, licking-related rhythmicity and taste responses. Overall, our results demonstrate that baseline and stimulus-evoked firing profiles of GC neurons and their processing schemes change as a function of cortical layer and cell type in awake mice.SIGNIFICANCE STATEMENT Sensory cortical areas show a laminar structure, with each layer composed of distinct cell types embedded in different circuits. While studies in other primary sensory areas have elucidated that pyramidal and inhibitory neurons belonging to distinct layers show distinct response properties, whether and how response properties of gustatory cortex (GC) neurons change as a function of their laminar position and cell type remains uninvestigated. Here, we show that there are several notable differences in baseline, prestimulus, and stimulus-evoked response profiles of pyramidal and inhibitory neurons belonging to deep and superficial layers of GC.

Attractor-state itinerancy in neural circuits with synaptic depression

Neural populations with strong excitatory recurrent connections can support bistable states in their mean firing rates. Multiple fixed points in a network of such bistable units can be used to model memory retrieval and pattern separation. The stability of fixed points may change on a slower timescale than that of the dynamics due to short-term synaptic depression, leading to transitions between quasi-stable point attractor states in a sequence that depends on the history of stimuli. To better understand these behaviors, we study a minimal model, which characterizes multiple fixed points and transitions between them in response to stimuli with diverse time- and amplitude-dependencies. The interplay between the fast dynamics of firing rate and synaptic responses and the slower timescale of synaptic depression makes the neural activity sensitive to the amplitude and duration of square-pulse stimuli in a nontrivial, history-dependent manner. Weak cross-couplings further deform the basins of attraction for different fixed points into intricate shapes. We find that while short-term synaptic depression can reduce the total number of stable fixed points in a network, it tends to strongly increase the number of fixed points visited upon repetitions of fixed stimuli. Our analysis provides a natural explanation for the system's rich responses to stimuli of different durations and amplitudes while demonstrating the encoding capability of bistable neural populations for dynamical features of incoming stimuli.

Taste coding strategies in insular cortex

While the cortical representation of sensory stimuli is well described for some sensory systems, a clear understanding of the cortical representation of taste stimuli remains elusive. Recent investigations have focused on both spatial and temporal organization of taste responses in the putative taste region of insular cortex. This review highlights recent literature focused on spatiotemporal coding strategies in insular cortex. These studies are examined in the context of the organization and function of the entire insular cortex, rather than a specific gustatory region of insular cortex. In regard to a taste quality-specific map, imaging studies have reported conflicting results, whereas electrophysiology studies have described a broad distribution of taste-responsive neurons found throughout insular cortex with no spatial organization. The current collection of evidence suggests that insular cortex may be organized into a hedonic or “viscerotopic” map, rather than one ordered according to taste quality. Further, it has been proposed that cortical taste responses can be separated into temporal “epochs” representing stimulus identity and palatability. This coding strategy presents a potential framework, whereby the coordinated activity of a population of neurons allows for the same neurons to respond to multiple taste stimuli or even other sensory modalities, a well-documented phenomenon in insular cortex neurons. However, these representations may not be static, as several studies have demonstrated that both spatial representation and temporal dynamics of taste coding change with experience. Collectively, these studies suggest that cortical taste representation is not organized in a spatially discrete map, but rather is plastic and spatially dispersed, using temporal information to encode multiple types of information about ingested stimuli.

Impact statement

The organization of taste coding in insular cortex is widely debated. While early work has focused on whether taste quality is encoded via labeled line or ensemble mechanisms, recent work has attempted to delineate the spatial organization and temporal components of taste processing in insular cortex. Recent imaging and electrophysiology studies have reported conflicting results in regard to the spatial organization of cortical taste responses, and many studies ignore potentially important temporal dynamics when investigating taste processing. This review highlights the latest research in these areas and examines them in the context of the anatomy and physiology of the insular cortex in general to provide a more comprehensive description of taste coding in insular cortex.

Dynamic effective connectivity

Metastability is a key source of itinerant dynamics in the brain; namely, spontaneous spatiotemporal reorganization of neuronal activity. This itinerancy has been the focus of numerous dynamic functional connectivity (DFC) analyses - developed to characterize the formation and dissolution of distributed functional patterns over time, using resting state fMRI. However, aside from technical and practical controversies, these approaches cannot recover the neuronal mechanisms that underwrite itinerant (e.g., metastable) dynamics-due to their descriptive, model-free nature. We argue that effective connectivity (EC) analyses are more apt for investigating the neuronal basis of metastability. To this end, we appeal to biologically-grounded models (i.e., dynamic causal modelling, DCM) and dynamical systems theory (i.e., heteroclinic sequential dynamics) to create a probabilistic, generative model of haemodynamic fluctuations. This model generates trajectories in the parametric space of EC modes (i.e., states of connectivity) that characterize functional brain architectures. In brief, it extends an established spectral DCM, to generate functional connectivity data features that change over time. This foundational paper tries to establish the model's face validity by simulating non-stationary fMRI time series and recovering key model parameters (i.e., transition probabilities among connectivity states and the parametric nature of these states) using variational Bayes. These data are further characterized using Bayesian model comparison (within and between subjects). Finally, we consider practical issues that attend applications and extensions of this scheme. Importantly, the scheme operates within a generic Bayesian framework - that can be adapted to study metastability and itinerant dynamics in any non-stationary time series.

Dopamine neuron ensembles signal the content of sensory prediction errors

Dopamine neurons respond to errors in predicting value-neutral sensory information. These data, combined with causal evidence that dopamine transients support sensory-based associative learning, suggest that the dopamine system signals a multidimensional prediction error. Yet such complexity is not evident in the activity of individual neurons or population averages. How then do downstream areas know what to learn in response to these signals? One possibility is that information about content is contained in the pattern of firing across many dopamine neurons. Consistent with this, here we show that the pattern of firing across a small group of dopamine neurons recorded in rats signals the identity of a mis-predicted sensory event. Further, this same information is reflected in the BOLD response elicited by sensory prediction errors in human midbrain. These data provide evidence that ensembles of dopamine neurons provide highly specific teaching signals, opening new possibilities for how this system might contribute to learning.