The Behavioral Relevance of Cortical Neural Ensemble Responses Emerges Suddenly

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.

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.

Neural Activity in the Anterior Insula at Drinking Onset and Licking Relates to Compulsion-Like Alcohol Consumption

Much remains unknown about the etiology of compulsion-like alcohol drinking, where consumption persists despite adverse consequences. The role of the anterior insula (AIC) in emotion, motivation, and interoception makes this brain region a likely candidate to drive challenge-resistant behavior, including compulsive drinking. Indeed, subcortical projections from the AIC promote compulsion-like intake in rats and are recruited in heavy-drinking humans during compulsion for alcohol, highlighting the importance of and need for more information about AIC activity patterns that support aversion-resistant responding. Single-unit activity was recorded in the AIC from 15 male rats during alcohol-only and compulsion-like consumption. We found three sustained firing phenotypes, sustained-increase, sustained-decrease, and drinking-onset cells, as well as several firing patterns synchronized with licking. While many AIC neurons had session-long activity changes, only neurons with firing increases at drinking onset had greater activity under compulsion-like conditions. Further, only cells with persistent firing increases maintained activity during pauses in licking, suggesting roles in maintaining drive for alcohol during breaks. AIC firing was not elevated during saccharin drinking, similar to lack of effect of AIC inhibition on sweet fluid intake in many studies. In addition, we observed subsecond changes in AIC neural activity tightly entrained to licking. One lick-synched firing pattern (determined for all licks in a session) predicted compulsion-like drinking, while a separate lick-associated pattern correlated with greater consumption across alcohol intake conditions. Collectively, these data provide a more integrated model for the role of AIC firing in compulsion-like drinking, with important relevance for how the AIC promotes sustained motivated responding more generally.

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.

Using lickometry to infer differential contributions of salience network regions during compulsion-like alcohol drinking

Alcohol use disorder extracts substantial personal, social and clinical costs, and continued intake despite negative consequences (compulsion-like consumption) can contribute strongly. Here we discuss lickometry, a simple method where lick times are determined across a session, while analysis across many aspects of licking can offer important insights into underlying psychological and action strategies, including their brain mechanisms. We first describe studies implicating anterior insula (AIC) and dorsal medial prefrontal cortex (dMPF) in compulsion-like responding for alcohol, then review work suggesting that AIC/ventral frontal cortex versus dMPF regulate different aspects of behavior (oral control and overall response strategy, versus moment-to-moment action organization). We then detail our lickometer work comparing alcohol-only drinking (AOD) and compulsion-like drinking under moderate- or higher-challenge (ModChD or HiChD, using quinine-alcohol). Many studies have suggested utilization of one of two main strategies, with higher motivation indicated by more bouts, and greater palatability suggested by longer, faster bouts. Instead, ModChD shows decreased variability in many lick measures, which is unexpected but consistent with the suggested importance of automaticity for addiction. Also surprising is that HiChD retains several behavior changes seen with ModChD, reduced tongue variability and earlier bout start, even though intake is otherwise disrupted. Since AIC-related measures are retained under both moderate- and higher-challenge, we propose a novel hypothesis that AIC sustains overall commitment regardless of challenge level, while disordered licking during HiChD mirrors the effects of dMPF inhibition. Thus, while AIC provides overall drive despite challenge, the ability to act is ultimately determined within the dMPF.

Switching state-space modeling of neural signal dynamics

Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting rapid changes in dynamics, with transient activity that is often the key feature of interest in the data. Stationary methods can be adapted to time-varying scenarios by employing fixed-duration windows under an assumption of quasi-stationarity. But time-varying dynamics can be explicitly modeled by switching state-space models, i.e., by using a pool of state-space models with different dynamics selected by a probabilistic switching process. Unfortunately, exact solutions for state inference and parameter learning with switching state-space models are intractable. Here we revisit a switching state-space model inference approach first proposed by Ghahramani and Hinton. We provide explicit derivations for solving the inference problem iteratively after applying a variational approximation on the joint posterior of the hidden states and the switching process. We introduce a novel initialization procedure using an efficient leave-one-out strategy to compare among candidate models, which significantly improves performance compared to the existing method that relies on deterministic annealing. We then utilize this state inference solution within a generalized expectation-maximization algorithm to estimate model parameters of the switching process and the linear state-space models with dynamics potentially shared among candidate models. We perform extensive simulations under different settings to benchmark performance against existing switching inference methods and further validate the robustness of our switching inference solution outside the generative switching model class. Finally, we demonstrate the utility of our method for sleep spindle detection in real recordings, showing how switching state-space models can be used to detect and extract transient spindles from human sleep electroencephalograms in an unsupervised manner.

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analysing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity but also because of changes in the signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analysing such data in terms of discrete latent states, but previous approaches have not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modelled condition-specific differences. We present a multilevel Bayesian HMM addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping and placing task. We show that, in line with previous work, the model identifies latent neural population states which are tightly linked to behavioural events, despite the model being trained without any information about event timing. The association between these states and corresponding behaviour is consistent across multiple days of recording. Notably, this consistency is not observed in the case of a single-level HMM, which fails to generalise across distinct recording sessions. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

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.

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.

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.

Eating driven by the gustatory insula: contrasting regulation by infralimbic vs. prelimbic cortices

Subregions within insular cortex and medial prefrontal cortex (mPFC) have been implicated in eating disorders; however, the way these brain regions interact to produce dysfunctional eating is poorly understood. The present study explored how two mPFC subregions, the infralimbic (IL) and prelimbic (PRL) cortices, regulate sucrose hyperphagia elicited specifically by a neurochemical manipulation of the agranular/dysgranular region of gustatory insula (AI/DI). Using intra-AI/DI infusion of the mu-opioid receptor (µ-OR) agonist, DAMGO (1 µg), sucrose hyperphagia was generated in ad-libitum-maintained rats, while in the same rat, either the IL or prelimbic (PRL) subregion of mPFC was inactivated bilaterally with muscimol (30 ng). Intra-IL muscimol markedly potentiated AI/DI DAMGO-induced sucrose hyperphagia by increasing eating bout duration and food consumption per bout. In contrast, PRL attenuated intra-AI/DI DAMGO-driven sucrose intake and feeding duration and eliminated the small DAMGO-induced increase in feeding bout initiation. Intra-IL or -PRL muscimol alone (i.e., without intra-AI/DI DAMGO) did not alter feeding behavior, but slightly reduced exploratory-like rearing in both mPFC subregions. These results reveal anatomical heterogeneity in mPFC regulation of the intense feeding-motivational state engendered by µ-OR signaling in the gustatory insula: IL significantly curtails consummatory activity, while PRL modestly contributes to feeding initiation. Results are discussed with regard to potential circuit-based mechanisms that may underlie the observed results.

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.

A series of unforced events

In this issue of Neuron, Recanatesi et al. (2022) show the need for, then find evidence of, directed noise fluctuations within cortical spike trains. Such fluctuations can reproduce the observed variability in timing of transitions between discrete activity patterns while maintaining their reliable sequential order as rats engage in self-initiated actions.

Encoding Taste: From Receptors to Perception

Taste information is encoded in the gustatory nervous system much as in other sensory systems, with notable exceptions. The concept of adequate stimulus is common to all sensory modalities, from somatosensory to auditory, visual, and so forth. That is, sensory cells normally respond only to one particular form of stimulation, the adequate stimulus, such as photons (photoreceptors in the visual system), odors (olfactory sensory neurons in the olfactory system), noxious heat (nociceptors in the somatosensory system), etc. Peripheral sensory receptors transduce the stimulus into membrane potential changes transmitted to the brain in the form of trains of action potentials. How information concerning different aspects of the stimulus such as quality, intensity, and duration are encoded in the trains of action potentials is hotly debated in the field of taste. At one extreme is the notion of labeled line/spatial coding - information for each different taste quality (sweet, salty, sour, etc.) is transmitted along a parallel but separate series of neurons (a "line") that project to focal clusters ("spaces") of neurons in the gustatory cortex. These clusters are distinct for each taste quality. Opposing this are concepts of population/combinatorial coding and temporal coding, where taste information is encrypted by groups of neurons (circuits) and patterns of impulses within these neuronal circuits. Key to population/combinatorial and temporal coding is that impulse activity in an individual neuron does not provide unambiguous information about the taste stimulus. Only populations of neurons and their impulse firing pattern yield that information.

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.

Dissociable control of μ-opioid-driven hyperphagia vs. food impulsivity across subregions of medial prefrontal, orbitofrontal, and insular cortex

This study explored potentially dissociable functions of mu-opioid receptor (µ-OR) signaling across different cortical territories in the control of anticipatory activity directed toward palatable food, consumption, and impulsive food-seeking behavior in male rats. The µ-OR agonist, DAMGO ([D-Ala², N-Me-Phe⁴, Gly⁵-ol]-enkephalin), was infused into infralimbic cortex (ILC), prelimbic cortex (PrL), medial and lateral ventral orbitofrontal cortices (VMO, VLO), and agranular/dysgranular insular (AI/DI) cortex of rats. Intra-ILC DAMGO markedly enhanced contact with a see-through screen behind which sucrose pellets were sequestered; in addition, rats having received intra-ILC and intra-VMO DAMGO exhibited locomotor hyperactivity while the screen was in place. Upon screen removal, intra-ILC and -VMO-treated rats emitted numerous, brief sucrose-intake bouts (yielding increased overall intake) interspersed with significant hyperactivity. In contrast, intra-AI/DI-treated rats consumed large amounts of sucrose in long, uninterrupted bouts with no anticipatory hyperactivity pre-screen removal. Intra-PrL and intra-VLO DAMGO altered neither pre-screen behavior nor sucrose intake. Finally, all rats were tested in a sucrose-reinforced differential reinforcement of low rates (DRL) task, which assesses the ability to advantageously withhold premature responses. DAMGO affected (impaired) DRL performance when infused into ILC only. These site-based dissociations reveal differential control of µ-OR-modulated appetitive/approach vs. consummatory behaviors by ventromedial/orbitofrontal and insular networks, respectively, and suggest a unique role of ILC µ-ORs in modulating inhibitory control.

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.

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.

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.

Against gustotopic representation in the human brain: There is no Cartesian Restaurant

The insular cortex is still one of the least understood cortical regions in the human brain. This review will highlight research on taste quality representation within the human insular cortex. Much of the controversy surrounding this topic is based in the ongoing debate over different theories of peripheral taste coding. When translated to the study of gustatory cortex, this has generated a distinct set of theoretical models, namely the topographic (or 'gustotopic') and population coding models of taste organization. Recent investigations into this topic have employed high-resolution functional neuroimaging methods and multivariate analytic approaches to examine taste quality coding in the human brain. Collectively, these recent studies do not support the topographic model of taste quality representation, but rather one where taste quality is represented by distributed patterns of activation within gustatory regions of the insula.

Multiple time-scales of decision-making in the hippocampus and prefrontal cortex

The prefrontal cortex and hippocampus are crucial for memory-guided decision-making. Neural activity in the hippocampus exhibits place-cell sequences at multiple timescales, including slow behavioral sequences (~seconds) and fast theta sequences (~100-200 ms) within theta oscillation cycles. How prefrontal ensembles interact with hippocampal sequences to support decision-making is unclear. Here, we examined simultaneous hippocampal and prefrontal ensemble activity in rats during learning of a spatial working-memory decision task. We found clear theta sequences in prefrontal cortex, nested within its behavioral sequences. In both regions, behavioral sequences maintained representations of current choices during navigation. In contrast, hippocampal theta sequences encoded alternatives for deliberation and were coordinated with prefrontal theta sequences that predicted upcoming choices. During error trials, these representations were preserved to guide ongoing behavior, whereas replay sequences during inter-trial periods were impaired prior to navigation. These results establish cooperative interaction between hippocampal and prefrontal sequences at multiple timescales for memory-guided decision-making.

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.

Spatial encoding in dorsomedial prefrontal cortex and hippocampus is related during deliberation

Deliberation is thought to involve the internal simulation of the outcomes of candidate actions, the valuation of those outcomes, and the selection of the actions with the highest expected value. While it is known that deliberation involves prefrontal cortical areas, specifically the dorsomedial prefrontal cortex (dmPFC), as well as the hippocampus (HPC) and other brain regions, how these areas process prospective information and select actions is not well understood. We recorded simultaneously from ensembles in dmPFC and CA1 of dorsal HPC in rats during performance of a spatial contingency switching task, and examined the relationships between spatial and reward encoding in these two areas during deliberation at the choice point. We found that CA1 and dmPFC represented either goal locations or the current position simultaneously, but that when goal locations were encoded, HPC and dmPFC did not always represent the same goal location. Ensemble activity in dmPFC predicted when HPC would represent goal locations, but on a broad timescale on the order of seconds. Also, reward encoding in dmPFC increased during hippocampal theta cycles where CA1 ensembles represented the goal location. These results suggest that dmPFC and HPC share prospective information during deliberation, that dmPFC may influence whether HPC represents prospective information, and that information recalled about goal locations by HPC may be integrated into dmPFC reward representations on fast timescales.

Covert sleep-related biological processes are revealed by probabilistic analysis in Drosophila

Reduced sleep duration and disrupted sleep quality are correlated with adverse mental and physical health outcomes. Better tools for measuring the internal drives for sleep and wake in model organisms would facilitate understanding the role of sleep quality in health. We defined two conditional probabilities, P(Wake) and P(Doze), that can be calculated from recordings of Drosophila activity without disturbing the animal. We demonstrated that P(Wake) is a measure of sleep depth and that P(Doze) is a measure of sleep pressure. In parallel, we developed an automatic classifier for state-based analysis of Drosophila behavior. These analysis tools will improve our understanding of the pharmacology and neuronal regulation of behavioral drives in the Drosophila brain.

Sleep pressure and sleep depth are key regulators of wake and sleep. Current methods of measuring these parameters in Drosophila melanogaster have low temporal resolution and/or require disrupting sleep. Here we report analysis tools for high-resolution, noninvasive measurement of sleep pressure and depth from movement data. Probability of initiating activity, P(Wake), measures sleep depth while probability of ceasing activity, P(Doze), measures sleep pressure. In vivo and computational analyses show that P(Wake) and P(Doze) are largely independent and control the amount of total sleep. We also develop a Hidden Markov Model that allows visualization of distinct sleep/wake substates. These hidden states have a predictable relationship with P(Doze) and P(Wake), suggesting that the methods capture the same behaviors. Importantly, we demonstrate that both the Doze/Wake probabilities and the sleep/wake substates are tied to specific biological processes. These metrics provide greater mechanistic insight into behavior than measuring the amount of sleep alone.

Synaptic Integration of Thalamic and Limbic Inputs in Rodent Gustatory Cortex

Neurons in the gustatory cortex (GC) process multiple aspects of a tasting experience, encoding not only the physiochemical identity of tastes, but also their anticipation and hedonic value. Information pertaining to these stimulus features is relayed to GC via the gustatory thalamus (VPMpc) and basolateral amygdala (BLA). It is not known whether these inputs drive separate groups of neurons, thus activating separate channels of information, or are integrated by neurons that receive both afferents. Here, we used anterograde labeling and in vivo intracellular recordings in anesthetized rats to assess the potential convergence of BLA and VPMpc inputs in GC, and to investigate the dynamics of integration of these inputs. We report substantial anatomic overlap of BLA and VPMpc axonal fields across GC, and identify a population of GC neurons receiving converging BLA and VPMpc inputs. Our data show that BLA modulates the gain of VPMpc-evoked responses in a time-dependent fashion and that this modulation is dependent on the recruitment of synaptic inhibition by both BLA and VPMpc. Our results suggest that BLA shapes cortical processing of thalamic inputs by dynamically gating the excitatory/inhibitory balance of the GC circuit.

Single and population coding of taste in the gustatory cortex of awake mice

Electrophysiological analysis has revealed much about the broad coding and neural ensemble dynamics that characterize gustatory cortical (GC) taste processing in awake rats and about how these dynamics relate to behavior. With regard to mice, however, data concerning cortical taste coding have largely been restricted to imaging, a technique that reveals average levels of neural responsiveness but that (currently) lacks the temporal sensitivity necessary for evaluation of fast response dynamics; furthermore, the few extant studies have thus far failed to provide consensus on basic features of coding. We have recorded the spiking activity of ensembles of GC neurons while presenting representatives of the basic taste modalities (sweet, salty, sour, and bitter) to awake mice. Our first central result is the identification of similarities between rat and mouse taste processing: most mouse GC neurons (~66%) responded distinctly to multiple (3-4) tastes; temporal coding analyses further reveal, for the first time, that single mouse GC neurons sequentially code taste identity and palatability, the latter responses emerging ~0.5 s after the former, with whole GC ensembles transitioning suddenly and coherently from coding taste identity to coding taste palatability. The second finding is that spatial location plays very little role in any aspect of taste responses: neither between- (anterior-posterior) nor within-mouse (dorsal-ventral) mapping revealed anatomic regions with narrow or temporally simple taste responses. These data confirm recent results showing that mouse cortical taste responses are not "gustotopic" but also go beyond these imaging results to show that mice process tastes through time.NEW & NOTEWORTHY Here, we analyzed taste-related spiking activity in awake mouse gustatory cortical (GC) neural ensembles, revealing deep similarities between mouse cortical taste processing and that repeatedly demonstrated in rat: mouse GC ensembles code multiple aspects of taste in a coarse-coded, time-varying manner that is essentially invariant across the spatial extent of GC. These data demonstrate that, contrary to some reports, cortical network processing is distributed, rather than being separated out into spatial subregion.

Cortical computations via metastable activity

Metastable brain dynamics are characterized by abrupt, jump-like modulations so that the neural activity in single trials appears to unfold as a sequence of discrete, quasi-stationary 'states'. Evidence that cortical neural activity unfolds as a sequence of metastable states is accumulating at fast pace. Metastable activity occurs both in response to an external stimulus and during ongoing, self-generated activity. These spontaneous metastable states are increasingly found to subserve internal representations that are not locked to external triggers, including states of deliberations, attention and expectation. Moreover, decoding stimuli or decisions via metastable states can be carried out trial-by-trial. Focusing on metastability will allow us to shift our perspective on neural coding from traditional concepts based on trial-averaging to models based on dynamic ensemble representations. Recent theoretical work has started to characterize the mechanistic origin and potential roles of metastable representations. In this article we review recent findings on metastable activity, how it may arise in biologically realistic models, and its potential role for representing internal states as well as relevant task variables.

Impact of precisely-timed inhibition of gustatory cortex on taste behavior depends on single-trial ensemble dynamics

Sensation and action are necessarily coupled during stimulus perception - while tasting, for instance, perception happens while an animal decides to expel or swallow the substance in the mouth (the former via a behavior known as 'gaping'). Taste responses in the rodent gustatory cortex (GC) span this sensorimotor divide, progressing through firing-rate epochs that culminate in the emergence of action-related firing. Population analyses reveal this emergence to be a sudden, coherent and variably-timed ensemble transition that reliably precedes gaping onset by 0.2-0.3s. Here, we tested whether this transition drives gaping, by delivering 0.5s GC perturbations in tasting trials. Perturbations significantly delayed gaping, but only when they preceded the action-related transition - thus, the same perturbation impacted behavior or not, depending on the transition latency in that particular trial. Our results suggest a distributed attractor network model of taste processing, and a dynamical role for cortex in driving motor behavior.

Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity

We demonstrate that a randomly connected attractor network with dynamic synapses can discriminate between similar sequences containing multiple stimuli suggesting such networks provide a general basis for neural computations in the brain. The network contains units representing assemblies of pools of neurons, with preferentially strong recurrent excitatory connections rendering each unit bi-stable. Weak interactions between units leads to a multiplicity of attractor states, within which information can persist beyond stimulus offset. When a new stimulus arrives, the prior state of the network impacts the encoding of the incoming information, with short-term synaptic depression ensuring an itinerancy between sets of active units. We assess the ability of such a network to encode the identity of sequences of stimuli, so as to provide a template for sequence recall, or decisions based on accumulation of evidence. Across a range of parameters, such networks produce the primacy (better final encoding of the earliest stimuli) and recency (better final encoding of the latest stimuli) observed in human recall data and can retain the information needed to make a binary choice based on total number of presentations of a specific stimulus. Similarities and differences in the final states of the network produced by different sequences lead to predictions of specific errors that could arise when an animal or human subject generalizes from training data, when the training data comprises a subset of the entire stimulus repertoire. We suggest that such networks can provide the general purpose computational engines needed for us to solve many cognitive tasks.

Recognizing Taste: Coding Patterns Along the Neural Axis in Mammals

The gustatory system encodes information about chemical identity, nutritional value, and concentration of sensory stimuli before transmitting the signal from taste buds to central neurons that process and transform the signal. Deciphering the coding logic for taste quality requires examining responses at each level along the neural axis-from peripheral sensory organs to gustatory cortex. From the earliest single-fiber recordings, it was clear that some afferent neurons respond uniquely and others to stimuli of multiple qualities. There is frequently a "best stimulus" for a given neuron, leading to the suggestion that taste exhibits "labeled line coding." In the extreme, a strict "labeled line" requires neurons and pathways dedicated to single qualities (e.g., sweet, bitter, etc.). At the other end of the spectrum, "across-fiber," "combinatorial," or "ensemble" coding requires minimal specific information to be imparted by a single neuron. Instead, taste quality information is encoded by simultaneous activity in ensembles of afferent fibers. Further, "temporal coding" models have proposed that certain features of taste quality may be embedded in the cadence of impulse activity. Taste receptor proteins are often expressed in nonoverlapping sets of cells in taste buds apparently supporting "labeled lines." Yet, taste buds include both narrowly and broadly tuned cells. As gustatory signals proceed to the hindbrain and on to higher centers, coding becomes more distributed and temporal patterns of activity become important. Here, we present the conundrum of taste coding in the light of current electrophysiological and imaging techniques at several levels of the gustatory processing pathway.

Expectation-induced modulation of metastable activity underlies faster coding of sensory stimuli

Sensory stimuli can be recognized more rapidly when they are expected. This phenomenon depends on expectation affecting the cortical processing of sensory information. However, the mechanisms responsible for the effects of expectation on sensory circuits remain elusive. Here, we report a novel computational mechanism underlying the expectation-dependent acceleration of coding observed in the gustatory cortex of alert rats. We use a recurrent spiking network model with a clustered architecture capturing essential features of cortical activity, such as its intrinsically generated metastable dynamics. Relying on network theory and computer simulations, we propose that expectation exerts its function by modulating the intrinsically generated dynamics preceding taste delivery. Our model’s predictions were confirmed in the experimental data, demonstrating how the modulation of ongoing activity can shape sensory coding. Altogether, these results provide a biologically plausible theory of expectation and ascribe a new functional role to intrinsically generated, metastable activity.

Orexin 1 receptors in the anterior cingulate and orbitofrontal cortex regulate cost and benefit decision-making

Orexin neurons are discretely localized within the lateral hypothalamus and have widespread projections into all areas of the brain. In addition, several lines of evidence specify that orexins may also participate in the regulation of a variety of affective and cognitive processes. The Orexin-1 receptor (OX1r) is distributed extensively throughout the prefrontal cortex (PFC). Delay-based decision- making is mediated largely by the orbitofrontal cortex (OFC) while effort- based decision-making is controlled by the anterior cingulated cortex (ACC). Hence, in the present study, a series of experiments were conducted to clarify the role of OX1r in the mPFC (ACC and/or OFC) in cost and benefit decision-making. The rats were trained in a delay and/or effort-based form of cost-benefit T-maze decision-making task. Two goal arms were different in the amount of accessible reward and cost. Before surgery, all animals were selecting the high reward arm and pay the cost on almost every trial. During the test days, the rats received local injections of either DMSO 20% /0.5 μl, as a vehicle, or SB334867 (3, 30 and 300 nM/0.5 μl), as a selective OX1r antagonist, within the ACC and/or OFC. The results of this study showed that the bilateral microinjection of SB334867 into ACC and/or OFC changed the preference to a low reward arm with no cost, indicating the role of OX1 receptors in cost and benefit decision- making. From these results, it can be implied that OX1 receptors in the mPFC play a crucial role for allowing the animal to evaluate and pay the cost to acquire greater rewards.

Interaction of Taste and Place Coding in the Hippocampus

An animal's survival depends on finding food and the memory of food and contexts are often linked. Given that the hippocampus is required for spatial and contextual memory, it is reasonable to expect related coding of space and food stimuli in hippocampal neurons. However, relatively little is known about how the hippocampus responds to tastes, the most central sensory property of food. In this study, we examined the taste-evoked responses and spatial firing properties of single units in the dorsal CA1 hippocampal region as male rats received a battery of taste stimuli differing in both chemical composition and palatability within a specific spatial context. We identified a subset of hippocampal neurons that responded to tastes, some of which were place cells. These taste and place responses had a distinct interaction: taste-responsive cells tended to have less spatially specific firing fields and place cells only responded to tastes delivered inside their place field. Like neurons in the amygdala and lateral hypothalamus, hippocampal neurons discriminated between tastes predominantly on the basis of palatability, with taste selectivity emerging concurrently with palatability-relatedness; these responses did not reflect movement or arousal. However, hippocampal taste responses emerged several hundred milliseconds later than responses in other parts of the taste system, suggesting that the hippocampus does not influence real-time taste decisions, instead associating the hedonic value of tastes with a particular context. This incorporation of taste responses into existing hippocampal maps could be one way that animals use past experience to locate food sources.SIGNIFICANCE STATEMENT Finding food is essential for animals' survival and taste and context memory are often linked. Although hippocampal responses to space and contexts have been well characterized, little is known about how the hippocampus responds to tastes. Here, we identified a subset of hippocampal neurons that discriminated between tastes based on palatability. Cells with stronger taste responses typically had weaker spatial responses and taste responses were confined to place cells' firing fields. Hippocampal taste responses emerged later than in other parts of the taste system, suggesting that the hippocampus does not influence taste decisions, but rather associates the hedonic value of tastes consumed within a particular context. This could be one way that animals use past experience to locate food sources.

The role of the gustatory cortex in incidental experience-evoked enhancement of later taste learning

The strength of learned associations between pairs of stimuli is affected by multiple factors, the most extensively studied of which is prior experience with the stimuli themselves. In contrast, little data is available regarding how experience with "incidental" stimuli (independent of any conditioning situation) impacts later learning. This lack of research is striking given the importance of incidental experience to survival. We have recently begun to fill this void using conditioned taste aversion (CTA), wherein an animal learns to avoid a taste that has been associated with malaise. We previously demonstrated that incidental exposure to salty and sour tastes (taste preexposure-TPE) enhances aversions learned later to sucrose. Here, we investigate the neurobiology underlying this phenomenon. First, we use immediate early gene (c-Fos) expression to identify gustatory cortex (GC) as a site at which TPE specifically increases the neural activation caused by taste-malaise pairing (i.e., TPE did not change c-Fos induced by either stimulus in isolation). Next, we use site-specific infection with the optical silencer Archaerhodopsin-T to show that GC inactivation during TPE inhibits the expected enhancements of both learning and CTA-related c-Fos expression, a full day later. Thus, we conclude that GC is almost certainly a vital part of the circuit that integrates incidental experience into later associative learning.

Visual Stimulus Detection Correlates with the Consistency of Temporal Sequences within Stereotyped Events of V1 Neuronal Population Activity

Sensory information about the world is translated into rate codes, such that modulations in mean spiking activity of neurons relate to differences in stimulus features. More recently, it has been proposed that also temporal properties of activity, such as assembly formation and sequential population activation, are important for understanding the relation between neuronal activity and behavioral output. These phenomena appear to be robust properties of neural circuits, but their relevance for perceptual judgments, such as the behavioral detection of stimuli, remains to be tested. Studying neuronal activity with two-photon calcium imaging in primary visual cortex of mice performing a go/no-go visual detection task, we found that assemblies (i.e., configurations of neuronal group activity) reliably recur, as defined using Ward-method clustering. However, population activation events with a recurring configuration of core neurons did not appear to serve a particular function in the coding of orientation or the detection of stimuli. Instead, we found that, regardless of whether the population event showed a recurring or nonrecurring configuration of neurons, the sequence of cluster activation was correlated with the detection of stimuli. Moreover, each neuron showed a preferred temporal position of activation within population events, which was robust despite varying neuronal participation. Furthermore, the timing of neuronal activity within such a sequence was more consistent when a stimulus was detected (hits) than when it remained unreported (misses). Our data indicate that neural processing of information related to visual detection behavior depends on the temporal positioning of individual and group-wise cell activity.

SIGNIFICANCE STATEMENT

Temporally coactive neurons have been hypothesized to form functional assemblies that might subserve different functions in the brain, but many of these proposed functions have not yet been experimentally tested. We used two-photon calcium imaging in V1 of mice performing a stimulus detection task to study the relation of assembly activity to the behavioral detection of visual stimuli. We found that the presence of recurring assemblies per se was not correlated with behavior, and these assemblies did not appear to serve a function in the coding of stimulus orientation. Instead, we found that activity in V1 is characterized by population events of varying membership, within which the consistency of the temporal sequence of neuronal activation is correlated with stimulus detection.

Single-neuron responses to intraoral delivery of odor solutions in primary olfactory and gustatory cortex

Smell plays a major role in our perception of food. Odorants released inside the mouth during consumption are combined with taste and texture qualities of a food to guide flavor preference learning and food choice behavior. Here, we built on recent physiological findings that implicated primary sensory cortex in multisensory flavor processing. Specifically, we used extracellular recordings in awake rats to characterize responses of single neurons in primary olfactory (OC) and gustatory cortex (GC) to intraoral delivery of odor solutions and compare odor responses to taste and plain water responses. The data reveal responses to olfactory, oral somatosensory, and gustatory qualities of intraoral stimuli in both OC and GC. Moreover, modality-specific responses overlap in time, indicating temporal convergence of multisensory, flavor-related inputs. The results extend previous work suggesting a role for primary OC in mediating influences of taste on smell that characterize flavor perception and point to an integral role for GC in olfactory processing.NEW & NOTEWORTHY Food perception is inherently multisensory, taking into account taste, smell, and texture qualities. However, the neural mechanisms underlying flavor perception remain unknown. Recording neural activity directly from the rat brain while animals consume multisensory flavor stimuli, we demonstrate that information about odor, taste, and mouthfeel of food converges on primary taste and smell cortex. The results suggest that processing of naturalistic, multisensory information involves an interacting network of primary sensory areas.

A gustocentric perspective to understanding primary sensory cortices

Most of the general principles used to explain sensory cortical function have been inferred from experiments performed on neocortical, primary sensory areas. Attempts to apply a neocortical view to the study of the gustatory cortex (GC) have provided only a limited understanding of this area. Failures to conform GC to classical neocortical principles have been implicitly interpreted as a demonstration of GC's uniqueness. Here we propose to take the opposite perspective, dismissing GC's uniqueness and using principles extracted from its study as a lens for looking at neocortical sensory function. In this review, we describe three significant findings related to gustatory cortical function and advocate their relevance for understanding neocortical sensory areas.

Itinerancy between attractor states in neural systems

Converging evidence from neural, perceptual and simulated data suggests that discrete attractor states form within neural circuits through learning and development. External stimuli may bias neural activity to one attractor state or cause activity to transition between several discrete states. Evidence for such transitions, whose timing can vary across trials, is best accrued through analyses that avoid any trial-averaging of data. One such method, hidden Markov modeling, has been effective in this context, revealing state transitions in many neural circuits during many tasks. Concurrently, modeling efforts have revealed computational benefits of stimulus processing via transitions between attractor states. This review describes the current state of the field, with comments on how its perceived limitations have been addressed.

Dynamical systems, attractors, and neural circuits

Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic—they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.