Navigating the Neural Space in Search of the Neural Code

Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks

Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. To partition an environment into discrete states, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields specified ad hoc by the researcher. This is problematic as a model for how an organism can learn appropriate behavioral sequences in unknown environments, as it fails to account for the unsupervised and self-organized nature of the required representations. Additionally, this approach presupposes knowledge on the part of the researcher on how the environment should be partitioned and represented and scales poorly with the size or complexity of the environment. To address these issues and gain insights into how the brain generates its own task-relevant mappings, we propose a learning architecture that combines unsupervised learning on the input projections with biologically motivated clustered connectivity within the representation layer. This combination allows input features to be mapped to clusters; thus the network self-organizes to produce clearly distinguishable activity patterns that can serve as the basis for reinforcement learning on the output projections. On the basis of the MNIST and Mountain Car tasks, we show that our proposed model performs better than either a comparable unclustered network or a clustered network with static input projections. We conclude that the combination of unsupervised learning and clustered connectivity provides a generic representational substrate suitable for further computation.

Behavior needs neural variability

Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.

The social dilemma: prefrontal control of mammalian sociability

Mammalian social interactions are orchestrated by a wide array of neural circuits. While some aspects of social behaviors are driven by subcortical circuits, and are considered to be highly conserved and hard-wired, others require dynamic and context-dependent modulation that integrates current state, past experience and goal-driven action selection. These cognitive social processes are known to be dependent on the integrity of the prefrontal cortex. However, the circuit mechanisms through which the prefrontal cortex supports complex social functions are still largely unknown, and it is unclear if and how they diverge from prefrontal control of behavior outside of the social domain. Here we review recent studies exploring the role of prefrontal circuits in mammalian social functions, and attempt to synthesize these findings to a holistic view of prefrontal control of sociability.

Measurement, manipulation and modeling of brain-wide neural population dynamics

Neural recording technologies increasingly enable simultaneous measurement of neural activity from multiple brain areas. To gain insight into distributed neural computations, a commensurate advance in experimental and analytical methods is necessary. We discuss two opportunities towards this end: the manipulation and modeling of neural population dynamics.

Multiregional communication and the channel modulation hypothesis

Multiregional communication is important to understanding the brain mechanisms supporting complex behaviors. Work in animals and human subjects shows that multiregional communication plays significant roles in cognitive function and is associated with neurological and neuropsychiatric disorders of brain function. Recent experimental advances enable empirical tests of the mechanisms of multiregional communication. Recent mechanistic insights into brain network function also suggest new therapies to treat disordered brain networks. Here, we discuss how to use the concept of communication channel modulation can help define and constrain what we mean by multiregional communication. We discuss behavioral and neurophysiological evidence for multiregional channels modulation. We then consider the role of causal manipulations and their implications for developing novel therapies based on multiregional communication.

Real-time, low-latency closed-loop feedback using markerless posture tracking

The ability to control a behavioral task or stimulate neural activity based on animal behavior in real-time is an important tool for experimental neuroscientists. Ideally, such tools are noninvasive, low-latency, and provide interfaces to trigger external hardware based on posture. Recent advances in pose estimation with deep learning allows researchers to train deep neural networks to accurately quantify a wide variety of animal behaviors. Here, we provide a new DeepLabCut-Live! package that achieves low-latency real-time pose estimation (within 15 ms, >100 FPS), with an additional forward-prediction module that achieves zero-latency feedback, and a dynamic-cropping mode that allows for higher inference speeds. We also provide three options for using this tool with ease: (1) a stand-alone GUI (called DLC-Live! GUI), and integration into (2) Bonsai, and (3) AutoPilot. Lastly, we benchmarked performance on a wide range of systems so that experimentalists can easily decide what hardware is required for their needs.

How many neurons are sufficient for perception of cortical activity?

Many theories of brain function propose that activity in sparse subsets of neurons underlies perception and action. To place a lower bound on the amount of neural activity that can be perceived, we used an all-optical approach to drive behaviour with targeted two-photon optogenetic activation of small ensembles of L2/3 pyramidal neurons in mouse barrel cortex while simultaneously recording local network activity with two-photon calcium imaging. By precisely titrating the number of neurons stimulated, we demonstrate that the lower bound for perception of cortical activity is ~14 pyramidal neurons. We find a steep sigmoidal relationship between the number of activated neurons and behaviour, saturating at only ~37 neurons, and show this relationship can shift with learning. Furthermore, activation of ensembles is balanced by inhibition of neighbouring neurons. This surprising perceptual sensitivity in the face of potent network suppression supports the sparse coding hypothesis, and suggests that cortical perception balances a trade-off between minimizing the impact of noise while efficiently detecting relevant signals.

Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition

Distributed neural population spiking patterns in macaque inferior temporal (IT) cortex that support core object recognition require additional time to develop for specific, "late-solved" images. This suggests the necessity of recurrent processing in these computations. Which brain circuits are responsible for computing and transmitting these putative recurrent signals to IT? To test whether the ventrolateral prefrontal cortex (vlPFC) is a critical recurrent node in this system, here, we pharmacologically inactivated parts of vlPFC and simultaneously measured IT activity while monkeys performed object discrimination tasks. vlPFC inactivation deteriorated the quality of late-phase (>150 ms from image onset) IT population code and produced commensurate behavioral deficits for late-solved images. Finally, silencing vlPFC caused the monkeys' IT activity and behavior to become more like those produced by feedforward-only ventral stream models. Together with prior work, these results implicate fast recurrent processing through vlPFC as critical to producing behaviorally sufficient object representations in IT.

Behavioral state coding by molecularly defined paraventricular hypothalamic cell type ensembles

Brains encode behaviors using neurons amenable to systematic classification by gene expression. The contribution of molecular identity to neural coding is not understood because of the challenges involved with measuring neural dynamics and molecular information from the same cells. We developed CaRMA (calcium and RNA multiplexed activity) imaging based on recording in vivo single-neuron calcium dynamics followed by gene expression analysis. We simultaneously monitored activity in hundreds of neurons in mouse paraventricular hypothalamus (PVH). Combinations of cell-type marker genes had predictive power for neuronal responses across 11 behavioral states. The PVH uses combinatorial assemblies of molecularly defined neuron populations for grouped-ensemble coding of survival behaviors. The neuropeptide receptor neuropeptide Y receptor type 1 (Npy1r) amalgamated multiple cell types with similar responses. Our results show that molecularly defined neurons are important processing units for brain function.

Learning to control the brain through adaptive closed-loop patterned stimulation

OBJECTIVE

Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed a model-free, adaptive, closed-loop stimulation (ACLS) system that learns to use multi-site electrical stimulation to control the pattern of activity of a population of neurons.

APPROACH

The ACLS system combined multi-electrode electrophysiological recordings with multi-site electrical stimulation to simultaneously record the activity of a population of 5-15 multiunit neurons and deliver spatially-patterned electrical stimulation across 4-16 sites. Using a closed-loop learning system, ACLS iteratively updated the pattern of stimulation to reduce the difference between the observed neural response and a specific target pattern of firing rates in the recorded multiunits.

MAIN RESULTS

In silico and in vivo experiments showed ACLS learns to produce specific patterns of neural activity (in ∼15 min) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus.

SIGNIFICANCE

Our results show an ACLS system that can learn, in real-time, to generate specific patterns of neural activity. This work provides a framework for using model-free closed-loop learning to control neural activity.

Transient Disruption of the Inferior Parietal Lobule Impairs the Ability to Attribute Intention to Action

Although it is well established that fronto-parietal regions are active during action observation, whether they play a causal role in the ability to infer others' intentions from visual kinematics remains undetermined. In the experiments reported here, we combined offline continuous theta burst stimulation (cTBS) with computational modeling to reveal and causally probe single-trial computations in the inferior parietal lobule (IPL) and inferior frontal gyrus (IFG). Participants received cTBS over the left anterior IPL and the left IFG pars orbitalis in separate sessions before completing an intention discrimination task (discriminate intention of observed reach-to-grasp acts) or a kinematic discrimination task unrelated to intention (discriminate peak wrist height of the same acts). We targeted intention-sensitive regions whose fMRI activity, recorded when observing the same reach-to-grasp acts, could accurately discriminate intention. We found that transient disruption of activity of the left IPL, but not the IFG, impaired the observer's ability to attribute intention to action. Kinematic discrimination unrelated to intention, in contrast, was largely unaffected. Computational analyses of how encoding (mapping of intention to movement kinematics) and readout (mapping of kinematics to intention choices) intersect at the single-trial level revealed that IPL cTBS did not diminish the overall sensitivity of intention readout to movement kinematics. Rather, it selectively misaligned intention readout with respect to encoding, deteriorating mapping from informative kinematic features to intention choices. These results provide causal evidence of how the left anterior IPL computes mapping from kinematics to intentions.

Single-trial cross-area neural population dynamics during long-term skill learning

Mammalian cortex has both local and cross-area connections, suggesting vital roles for both local and cross-area neural population dynamics in cortically-dependent tasks, like movement learning. Prior studies of movement learning have focused on how single-area population dynamics change during short-term adaptation. It is unclear how cross-area dynamics contribute to movement learning, particularly long-term learning and skill acquisition. Using simultaneous recordings of rodent motor (M1) and premotor (M2) cortex and computational methods, we show how cross-area activity patterns evolve during reach-to-grasp learning in rats. The emergence of reach-related modulation in cross-area activity correlates with skill acquisition, and single-trial modulation in cross-area activity predicts reaction time and reach duration. Local M2 neural activity precedes local M1 activity, supporting top-down hierarchy between the regions. M2 inactivation preferentially affects cross-area dynamics and behavior, with minimal disruption of local M1 dynamics. Together, these results indicate that cross-area population dynamics are necessary for learned motor skills.

Engineering recurrent neural networks from task-relevant manifolds and dynamics

Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons.

Birhythmic Analog Circuit Maze: A Nonlinear Neurostimulation Testbed

Brain dynamics can exhibit narrow-band nonlinear oscillations and multistability. For a subset of disorders of consciousness and motor control, we hypothesized that some symptoms originate from the inability to spontaneously transition from one attractor to another. Using external perturbations, such as electrical pulses delivered by deep brain stimulation devices, it may be possible to induce such transition out of the pathological attractors. However, the induction of transition may be non-trivial, rendering the current open-loop stimulation strategies insufficient. In order to develop next-generation neural stimulators that can intelligently learn to induce attractor transitions, we require a platform to test the efficacy of such systems. To this end, we designed an analog circuit as a model for the multistable brain dynamics. The circuit spontaneously oscillates stably on two periods as an instantiation of a 3-dimensional continuous-time gated recurrent neural network. To discourage simple perturbation strategies, such as constant or random stimulation patterns from easily inducing transition between the stable limit cycles, we designed a state-dependent nonlinear circuit interface for external perturbation. We demonstrate the existence of nontrivial solutions to the transition problem in our circuit implementation.

Cre-assisted fine-mapping of neural circuits using orthogonal split inteins

Existing genetic methods of neuronal targeting do not routinely achieve the resolution required for mapping brain circuits. New approaches are thus necessary. Here, we introduce a method for refined neuronal targeting that can be applied iteratively. Restriction achieved at the first step can be further refined in a second step, if necessary. The method relies on first isolating neurons within a targeted group (i.e. Gal4 pattern) according to their developmental lineages, and then intersectionally limiting the number of lineages by selecting only those in which two distinct neuroblast enhancers are active. The neuroblast enhancers drive expression of split Cre recombinase fragments. These are fused to non-interacting pairs of split inteins, which ensure reconstitution of active Cre when all fragments are expressed in the same neuroblast. Active Cre renders all neuroblast-derived cells in a lineage permissive for Gal4 activity. We demonstrate how this system can facilitate neural circuit-mapping in Drosophila.

Turning Touch into Perception

Many brain areas modulate their activity during vibrotactile tasks. The activity from these areas may code the stimulus parameters, stimulus perception, or perceptual reports. Here, we discuss findings obtained in behaving monkeys aimed to understand these processes. In brief, neurons from the somatosensory thalamus and primary somatosensory cortex (S1) only code the stimulus parameters during the stimulation periods. In contrast, areas downstream of S1 code the stimulus parameters during not only the task components but also perception. Surprisingly, the midbrain dopamine system is an actor not considered before in perception. We discuss the evidence that it codes the subjective magnitude of a sensory percept. The findings reviewed here may help us to understand where and how sensation transforms into perception in the brain.

Granger causality analysis of rat cortical functional connectivity in pain

OBJECTIVE

The primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) are two of the most important cortical brain regions encoding the sensory-discriminative and affective-emotional aspects of pain, respectively. However, the functional connectivity of these two areas during pain processing remains unclear. Developing methods to dissect the functional connectivity and directed information flow between cortical pain circuits can reveal insight into neural mechanisms of pain perception.

APPROACH

We recorded multichannel local field potentials (LFPs) from the S1 and ACC in freely behaving rats under various conditions of pain stimulus (thermal versus mechanical) and pain state (naive versus chronic pain). We applied Granger causality (GC) analysis to the LFP recordings and inferred frequency-dependent GC statistics between the S1 and ACC.

MAIN RESULTS

We found an increased information flow during noxious pain stimulus presentation in both S1[Formula: see text]ACC and ACC[Formula: see text]S1 directions, especially at theta and gamma frequency bands. Similar results were found for thermal and mechanical pain stimuli. The chronic pain state shares common observations, except for further elevated GC measures especially in the gamma band. Furthermore, time-varying GC analysis revealed a negative correlation between the direction-specific and frequency-dependent GC and animal's paw withdrawal latency. In addition, we used computer simulations to investigate the impact of model mismatch, noise, missing variables, and common input on the conditional GC estimate. We also compared the GC results with the transfer entropy (TE) estimates.

SIGNIFICANCE

Our results reveal functional connectivity and directed information flow between the S1 and ACC during various pain conditions. The dynamic GC analysis support the hypothesis of cortico-cortical information loop in pain perception, consistent with the computational predictive coding paradigm.

Does Closing the Eyes Enhance Auditory Attention? Eye Closure Increases Attentional Alpha-Power Modulation but Not Listening Performance

In challenging listening conditions, closing the eyes is a strategy with intuitive appeal to improve auditory attention and perception. On the neural level, closing the eyes increases the power of alpha oscillations (∼10 Hz), which are a prime signature of auditory attention. Here, we test whether eye closure benefits neural and behavioral signatures of auditory attention and perception. Participants (n = 22) attended to one of two alternating streams of spoken numbers with open or closed eyes in a darkened chamber. After each trial, participants indicated whether probes had been among the to-be-attended or to-be-ignored numbers. In the EEG, states of relative high versus low alpha power accompanied the presentation of attended versus ignored numbers. Importantly, eye closure did not only increase the overall level of absolute alpha power but also the attentional modulation thereof. Behaviorally, however, neither perceptual sensitivity nor response criterion was affected by eye closure. To further examine whether this behavioral null result would conceptually replicate in a simple auditory detection task, a follow-up experiment was conducted that required participants (n = 19) to detect a near-threshold target tone in noise. As in the main experiment, our results provide evidence for the absence of any difference in perceptual sensitivity and criterion for open versus closed eyes. In summary, we demonstrate here that the modulation of the human alpha rhythm by auditory attention is increased when participants close their eyes. However, our results speak against the widely held belief that eye closure per se improves listening behavior.

Local cortical desynchronization and pupil-linked arousal differentially shape brain states for optimal sensory performance

Instantaneous brain states have consequences for our sensation, perception, and behaviour. Fluctuations in arousal and neural desynchronization likely pose perceptually relevant states. However, their relationship and their relative impact on perception is unclear. We here show that, at the single-trial level in humans, local desynchronization in sensory cortex (expressed as time-series entropy) versus pupil-linked arousal differentially impact perceptual processing. While we recorded electroencephalography (EEG) and pupillometry data, stimuli of a demanding auditory discrimination task were presented into states of high or low desynchronization of auditory cortex via a real-time closed-loop setup. Desynchronization and arousal distinctly influenced stimulus-evoked activity and shaped behaviour displaying an inverted u-shaped relationship: States of intermediate desynchronization elicited minimal response bias and fastest responses, while states of intermediate arousal gave rise to highest response sensitivity. Our results speak to a model in which independent states of local desynchronization and global arousal jointly optimise sensory processing and performance.

From mental representations to neural codes: A multilevel approach

Representation and computation are the best tools we have for explaining intelligent behavior. In our program, we explore the space of representations present in the mind by constraining them to explain data at multiple levels of analysis, from behavioral patterns to neural activity. We argue that this integrated program assuages Brette's worries about the study of the neural code.

Beyond metaphors and semantics: A framework for causal inference in neuroscience

The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

The structure of non-human cognitive neuroscience: an epistemological critique

Every scientific practice rests on implicit unrevised theoretical assumptions. Neuroscience, in particular, focuses on a very controversial object of study-the brain and is therefore prone to tacitly embrace philosophical positions in its everyday workings. It is thus, of utmost importance, to develop a critique of the structure of neuroscientific investigation so as to understand what the uncovered pillars of the field are, what pitfalls they may implicate and how we can correct them. In this paper, I gather the first critiques in animal cognitive neuroscience and hope to establish the first step in a continuous process of revision. By applying a conceptual division of neuroscience into cognitive, behavioral and neurobiological theories, I point out the main problems in articulating the three, based on actual scientific practice rather than purely theoretical reasoning. I conclude by proposing developments on behavioral theory and set an initial critique on assumptions on both cognitive and neurobiological theories.

Contemporary strategies for dissecting the neuronal basis of neurodevelopmental disorders

Great efforts in clinical and basic research have shown progress in understanding the neurobiological mechanisms of neurodevelopmental disorders, such as autism, schizophrenia, and attention-deficit hyperactive disorders. Literature on this field have suggested that these disorders are affected by the complex interaction of genetic, biological, psychosocial and environmental risk factors. However, this complexity of interplaying risk factors during neurodevelopment has prevented a complete understanding of the causes of those neuropsychiatric symptoms. Recently, with advances in modern high-resolution neuroscience methods, the neural circuitry analysis approach has provided new solutions for understanding the causal relationship between dysfunction of a neural circuit and behavioral alteration in neurodevelopmental disorders. In this review we will discuss recent progress in developing novel optogenetic and chemogenetic strategies to investigate neurodevelopmental disorders.

Computational Neuroethology: A Call to Action

The brain is worthy of study because it is in charge of behavior. A flurry of recent technical advances in measuring and quantifying naturalistic behaviors provide an important opportunity for advancing brain science. However, the problem of understanding unrestrained behavior in the context of neural recordings and manipulations remains unsolved, and developing approaches to addressing this challenge is critical. Here we discuss considerations in computational neuroethology-the science of quantifying naturalistic behaviors for understanding the brain-and propose strategies to evaluate progress. We point to open questions that require resolution and call upon the broader systems neuroscience community to further develop and leverage measures of naturalistic, unrestrained behavior, which will enable us to more effectively probe the richness and complexity of the brain.

Neural Entrainment and Attentional Selection in the Listening Brain

The streams of sounds we typically attend to abound in acoustic regularities. Neural entrainment is seen as an important mechanism that the listening brain exploits to attune to these regularities and to enhance the representation of attended sounds. We delineate the neurophysiology underlying this mechanism and review entrainment alongside its more pragmatic signature, often called 'speech tracking'. The latter has become a popular analytical approach to trace the reflection of acoustic and linguistic information at different levels of granularity, from neurophysiology to neuroimaging. As we discuss, the concept of entrainment offers both a putative neurophysiological mechanism for selective listening and a versatile window onto the neural basis of hearing and speech comprehension.

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

Choice (-history) correlations in sensory cortex: cause or consequence?

One challenge in neuroscience, as in other areas of science, is to make inferences about the underlying causal structure from correlational data. Here, we discuss this challenge in the context of choice correlations in sensory neurons, that is, trial-by-trial correlations, unexplained by the stimulus, between the activity of sensory neurons and an animal's perceptual choice. Do these choice-correlations reflect feedforward, feedback signalling, both, or neither? We highlight recent results of correlational and causal examinations of choice and choice-history signals in sensory, and in part sensorimotor, cortex and address formal statistical frameworks to infer causal interactions from data.

Decoding and encoding (de)mixed population responses

A central tenet of neuroscience is that the brain works through large populations of interacting neurons. With recent advances in recording techniques, the inner working of these populations has come into full view. Analyzing the resulting large-scale data sets is challenging because of the often complex and 'mixed' dependency of neural activities on experimental parameters, such as stimuli, decisions, or motor responses. Here we review recent insights gained from analyzing these data with dimensionality reduction methods that 'demix' these dependencies. We demonstrate that the mappings from (carefully chosen) experimental parameters to population activities appear to be typical and stable across tasks, brain areas, and animals, and are often identifiable by linear methods. By considering when and why dimensionality reduction and demixing work well, we argue for a view of population coding in which populations represent (demixed) latent signals, corresponding to stimuli, decisions, motor responses, and so on. These latent signals are encoded into neural population activity via non-linear mappings and decoded via linear readouts. We explain how such a scheme can facilitate the propagation of information across cortical areas, and we review neural network architectures that can reproduce the encoding and decoding of latent signals in population activities. These architectures promise a link from the biophysics of single neurons to the activities of neural populations.

Data-Driven Approaches to Understanding Visual Neuron Activity

With modern neurophysiological methods able to record neural activity throughout the visual pathway in the context of arbitrarily complex visual stimulation, our understanding of visual system function is becoming limited by the available models of visual neurons that can be directly related to such data. Different forms of statistical models are now being used to probe the cellular and circuit mechanisms shaping neural activity, understand how neural selectivity to complex visual features is computed, and derive the ways in which neurons contribute to systems-level visual processing. However, models that are able to more accurately reproduce observed neural activity often defy simple interpretations. As a result, rather than being used solely to connect with existing theories of visual processing, statistical modeling will increasingly drive the evolution of more sophisticated theories.

Closed-loop control of gamma oscillations in the amygdala demonstrates their role in spatial memory consolidation

Gamma is a ubiquitous brain rhythm hypothesized to support cognitive, perceptual, and mnemonic functions by coordinating neuronal interactions. While much correlational evidence supports this hypothesis, direct experimental tests have been lacking. Since gamma occurs as brief bursts of varying frequencies and durations, most existing approaches to manipulate gamma are either too slow, delivered irrespective of the rhythm's presence, not spectrally specific, or unsuitable for bidirectional modulation. Here, we overcome these limitations with an approach that accurately detects and modulates endogenous gamma oscillations, using closed-loop signal processing and optogenetic stimulation. We first show that the rat basolateral amygdala (BLA) exhibits prominent gamma oscillations during the consolidation of contextual memories. We then boost or diminish gamma during consolidation, in turn enhancing or impairing subsequent memory strength. Overall, our study establishes the role of gamma oscillations in memory consolidation and introduces a versatile method for studying fast network rhythms in vivo.

Cracking the Function of Layers in the Sensory Cortex

Understanding how cortical activity generates sensory perceptions requires a detailed dissection of the function of cortical layers. Despite our relatively extensive knowledge of their anatomy and wiring, we have a limited grasp of what each layer contributes to cortical computation. We need to develop a theory of cortical function that is rooted solidly in each layer's component cell types and fine circuit architecture and produces predictions that can be validated by specific perturbations. Here we briefly review the progress toward such a theory and suggest an experimental road map toward this goal. We discuss new methods for the all-optical interrogation of cortical layers, for correlating in vivo function with precise identification of transcriptional cell type, and for mapping local and long-range activity in vivo with synaptic resolution. The new technologies that can crack the function of cortical layers are finally on the immediate horizon.

Lesion Studies in Contemporary Neuroscience

Studies of humans with focal brain damage and non-human animals with experimentally induced brain lesions have provided pivotal insights into the neural basis of behavior. As the repertoire of neural manipulation and recording techniques expands, the utility of studying permanent brain lesions bears re-examination. Studies on the effects of permanent lesions provide vital data about brain function that are distinct from those of reversible manipulations. Focusing on work carried out in humans and nonhuman primates, we address the inferential strengths and limitations of lesion studies, recent methodological developments, the integration of this approach with other methods, and the clinical and ecological relevance of this research. We argue that lesion studies are essential to the rigorous assessment of neuroscience theories.

Embracing Complexity in Defensive Networks

The neural basis of defensive behaviors continues to attract much interest, not only because they are important for survival but also because their dysregulation may be at the origin of anxiety disorders. Recently, a dominant approach in the field has been the optogenetic manipulation of specific circuits or cell types within these circuits to dissect their role in different defensive behaviors. While the usefulness of optogenetics is unquestionable, we argue that this method, as currently applied, fosters an atomistic conceptualization of defensive behaviors, which hinders progress in understanding the integrated responses of nervous systems to threats. Instead, we advocate for a holistic approach to the problem, including observational study of natural behaviors and their neuronal correlates at multiple sites, coupled to the use of optogenetics, not to globally turn on or off neurons of interest, but to manipulate specific activity patterns hypothesized to regulate defensive behaviors.

Bayesian Computation through Cortical Latent Dynamics

Statistical regularities in the environment create prior beliefs that we rely on to optimize our behavior when sensory information is uncertain. Bayesian theory formalizes how prior beliefs can be leveraged and has had a major impact on models of perception, sensorimotor function, and cognition. However, it is not known how recurrent interactions among neurons mediate Bayesian integration. By using a time-interval reproduction task in monkeys, we found that prior statistics warp neural representations in the frontal cortex, allowing the mapping of sensory inputs to motor outputs to incorporate prior statistics in accordance with Bayesian inference. Analysis of recurrent neural network models performing the task revealed that this warping was enabled by a low-dimensional curved manifold and allowed us to further probe the potential causal underpinnings of this computational strategy. These results uncover a simple and general principle whereby prior beliefs exert their influence on behavior by sculpting cortical latent dynamics.

Genetic Dissection of Neural Circuits: A Decade of Progress

Tremendous progress has been made since Neuron published our Primer on genetic dissection of neural circuits 10 years ago. Since then, cell-type-specific anatomical, neurophysiological, and perturbation studies have been carried out in a multitude of invertebrate and vertebrate organisms, linking neurons and circuits to behavioral functions. New methods allow systematic classification of cell types and provide genetic access to diverse neuronal types for studies of connectivity and neural coding during behavior. Here we evaluate key advances over the past decade and discuss future directions.

Body Patches in Inferior Temporal Cortex Encode Categories with Different Temporal Dynamics

An unresolved question in cognitive neuroscience is how representations of object categories at different levels (basic and superordinate) develop during the course of the neural response within an area. To address this, we decoded categories of different levels from the spiking responses of populations of neurons recorded in two fMRI-defined body patches in the macaque STS. Recordings of the two patches were made in the same animals with the same stimuli. Support vector machine classifiers were trained at brief response epochs and tested at the same or different epochs, thus assessing whether category representations change during the course of the response. In agreement with hierarchical processing within the body patch network, the posterior body patch mid STS body (MSB) showed an earlier onset of categorization compared with the anterior body patch anterior STS body (ASB), irrespective of the categorization level. Decoding of the superordinate body versus nonbody categories was less dynamic in MSB than in ASB, with ASB showing a biphasic temporal pattern. Decoding of the ordinate-level category human versus monkey bodies showed similar temporal patterns in both patches. The decoding onset of superordinate categorizations involving bodies was as early as for basic-level categorization, suggesting that previously reported differences between the onset of basic and superordinate categorizations may depend on the area. The qualitative difference between areas in their dynamics of category representation may hinder the interpretation of decoding dynamics based on EEG or MEG, methods that may mix signals of different areas.

Zooming Out of Single Neurons Reveals Structure in Mnemonic Representations

In this issue of Neuron, Rossi-Pool et al. (2017) show that the complex and heterogeneous response profiles of individual neurons in the dorsal premotor cortex during comparison of tactile temporal patterns can be understood in terms of two robust activity patterns that emerge across the population.

Emerging ideas and tools to study the emergent properties of the cortical neural circuits for voluntary motor control in non-human primates

For years, neurophysiological studies of the cerebral cortical mechanisms of voluntary motor control were limited to single-electrode recordings of the activity of one or a few neurons at a time. This approach was supported by the widely accepted belief that single neurons were the fundamental computational units of the brain (the “neuron doctrine”). Experiments were guided by motor-control models that proposed that the motor system attempted to plan and control specific parameters of a desired action, such as the direction, speed or causal forces of a reaching movement in specific coordinate frameworks, and that assumed that the controlled parameters would be expressed in the task-related activity of single neurons. The advent of chronically implanted multi-electrode arrays about 20 years ago permitted the simultaneous recording of the activity of many neurons. This greatly enhanced the ability to study neural control mechanisms at the population level. It has also shifted the focus of the analysis of neural activity from quantifying single-neuron correlates with different movement parameters to probing the structure of multi-neuron activity patterns to identify the emergent computational properties of cortical neural circuits. In particular, recent advances in “dimension reduction” algorithms have attempted to identify specific covariance patterns in multi-neuron activity which are presumed to reflect the underlying computational processes by which neural circuits convert the intention to perform a particular movement into the required causal descending motor commands. These analyses have led to many new perspectives and insights on how cortical motor circuits covertly plan and prepare to initiate a movement without causing muscle contractions, transition from preparation to overt execution of the desired movement, generate muscle-centered motor output commands, and learn new motor skills. Progress is also being made to import optical-imaging and optogenetic toolboxes from rodents to non-human primates to overcome some technical limitations of multi-electrode recording technology.

Neural population control via deep image synthesis

Particular deep artificial neural networks (ANNs) are today’s most accurate models of the primate brain’s ventral visual stream. Using an ANN-driven image synthesis method, we found that luminous power patterns (i.e., images) can be applied to primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. This method, although not yet perfect, achieves unprecedented independent control of the activity state of entire populations of V4 neural sites, even those with overlapping receptive fields. These results show how the knowledge embedded in today’s ANN models might be used to noninvasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.

Reversible Inactivation of Different Millimeter-Scale Regions of Primate IT Results in Different Patterns of Core Object Recognition Deficits

Extensive research suggests that the inferior temporal (IT) population supports visual object recognition behavior. However, causal evidence for this hypothesis has been equivocal, particularly beyond the specific case of face-selective subregions of IT. Here, we directly tested this hypothesis by pharmacologically inactivating individual, millimeter-scale subregions of IT while monkeys performed several core object recognition subtasks, interleaved trial-by trial. First, we observed that IT inactivation resulted in reliable contralateral-biased subtask-selective behavioral deficits. Moreover, inactivating different IT subregions resulted in different patterns of subtask deficits, predicted by each subregion's neuronal object discriminability. Finally, the similarity between different inactivation effects was tightly related to the anatomical distance between corresponding inactivation sites. Taken together, these results provide direct evidence that the IT cortex causally supports general core object recognition and that the underlying IT coding dimensions are topographically organized.

Myopic control of neural dynamics

Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment “input” to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This “myopic” controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future (hence its myopic nature), which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this control’s utility, two examples with distinctly different applications in neuroscience are studied. First, we show the myopic control’s utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence. Second, an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders.

Stimulating a neural system and observing its effect through simultaneous observation offers the promise to better understand how neural systems perform computations, as well as for the treatment of neurological disorders. A powerful perspective for understanding a neural system’s behavior undergoing stimulation is to conceptualize them as dynamical systems, which considers the global effect that stimulation has on the brain, rather than only assessing what impact it has on the recorded signal from the brain. With this more comprehensive perspective comes a central challenge of determining what requirements need to be satisfied to harness neural observations and then stimulate to make one dynamical system function as another one entirely. This could lead to applications such as neural stimulators that make a diseased brain behave like its healthy counterpart, or to make a neural system previously capable of only hasty decision making to wait and accumulate more evidence for a more informed decision. In this work we explore the implications of this new perspective on neural stimulation and derive a simple prescription for using neural observations to inform stimulation protocol that makes one neural system behave like another one.

Medial Prefrontal Cortex Population Activity Is Plastic Irrespective of Learning

The prefrontal cortex (PFC) is thought to learn the relationships between actions and their outcomes. But little is known about what changes to population activity in PFC are specific to learning these relationships. Here we characterize the plasticity of population activity in the medial PFC (mPFC) of male rats learning rules on a Y-maze. First, we show that the population always changes its patterns of joint activity between the periods of sleep either side of a training session on the maze, regardless of successful rule learning during training. Next, by comparing the structure of population activity in sleep and training, we show that this population plasticity differs between learning and nonlearning sessions. In learning sessions, the changes in population activity in post-training sleep incorporate the changes to the population activity during training on the maze. In nonlearning sessions, the changes in sleep and training are unrelated. Finally, we show evidence that the nonlearning and learning forms of population plasticity are driven by different neuron-level changes, with the nonlearning form entirely accounted for by independent changes to the excitability of individual neurons, and the learning form also including changes to firing rate couplings between neurons. Collectively, our results suggest two different forms of population plasticity in mPFC during the learning of action-outcome relationships: one a persistent change in population activity structure decoupled from overt rule-learning, and the other a directional change driven by feedback during behavior.SIGNIFICANCE STATEMENT The PFC is thought to represent our knowledge about what action is worth doing in which context. But we do not know how the activity of neurons in PFC collectively changes when learning which actions are relevant. Here we show, in a trial-and-error task, that population activity in PFC is persistently changing, regardless of learning. Only during episodes of clear learning of relevant actions are the accompanying changes to population activity carried forward into sleep, suggesting a long-lasting form of neural plasticity. Our results suggest that representations of relevant actions in PFC are acquired by reward imposing a direction onto ongoing population plasticity.

Re-evaluating Circuit Mechanisms Underlying Pattern Separation

When animals interact with complex environments, their neural circuits must separate overlapping patterns of activity that represent sensory and motor information. Pattern separation is thought to be a key function of several brain regions, including the cerebellar cortex, insect mushroom body, and dentate gyrus. However, recent findings have questioned long-held ideas on how these circuits perform this fundamental computation. Here, we re-evaluate the functional and structural mechanisms underlying pattern separation. We argue that the dimensionality of the space available for population codes representing sensory and motor information provides a common framework for understanding pattern separation. We then discuss how these three circuits use different strategies to separate activity patterns and facilitate associative learning in the presence of trial-to-trial variability.

Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

A compressed sensing framework for efficient dissection of neural circuits

A fundamental question in neuroscience is how neural networks generate behavior. The lack of genetic tools and unique promoters to functionally manipulate specific neuronal subtypes makes it challenging to determine the roles of individual subtypes in behavior. We describe a compressed sensing-based framework in combination with non-specific genetic tools to infer candidate neurons controlling behaviors with fewer measurements than previously thought possible. We tested this framework by inferring interneuron subtypes regulating the speed of locomotion of the nematode Caenorhabditis elegans. We developed a real-time stabilization microscope for accurate long-term, high-magnification imaging and targeted perturbation of neural activity in freely moving animals to validate our inferences. We show that a circuit of three interconnected interneuron subtypes, RMG, AVB and SIA control different aspects of locomotion speed as the animal navigates its environment. Our work suggests that compressed sensing approaches can be used to identify key nodes in complex biological networks.

Perceptual Decision-Making: A Field in the Midst of a Transformation

Major changes are underway in the field of perceptual decision-making. Single-neuron studies have given way to population recordings with identified cell types, traditional analyses have been extended to accommodate these large and diverse collections of neurons, and novel methods of neural disruption have provided insights about causal circuits. Further, the field has expanded to include multiple new species: rodents and invertebrates, for example, have been instrumental in demonstrating the importance of internal state on neural responses. Finally, a renewed interest in ethological stimuli prompted development of new behaviors, frequently analyzed by new, automated movement tracking methods. Taken together, these advances constitute a seismic shift in both our approach and understanding of how incoming sensory signals are used to guide decisions.

A Dynamical Systems Perspective on Flexible Motor Timing

A hallmark of higher brain function is the ability to rapidly and flexibly adjust behavioral responses based on internal and external cues. Here, we examine the computational principles that allow decisions and actions to unfold flexibly in time. We adopt a dynamical systems perspective and outline how temporal flexibility in such a system can be achieved through manipulations of inputs and initial conditions. We then review evidence from experiments in nonhuman primates that support this interpretation. Finally, we explore the broader utility and limitations of the dynamical systems perspective as a general framework for addressing open questions related to the temporal control of movements, as well as in the domains of learning and sequence generation.

How the Hippocampus Represents Memories: Making Sense of Memory Allocation Studies

In recent years there has been a wealth of studies investigating how memories are allocated in the hippocampus. Some of those studies showed that it is possible to manipulate the identity of neurons recruited to represent a given memory without affecting the memory's behavioral expression. Those findings raised questions about how the hippocampus represents memories, with some researchers arguing that hippocampal neurons do not represent fixed stimuli. Herein, an alternative hypothesis is argued. Neurons in high-order brain regions can be tuned to multiple dimensions, forming complex, abstract representations. It is argued that such complex receptive fields allow those neurons to show some flexibility in their responses while still representing relatively fixed sets of stimuli. Moreover, it is pointed out that changes induced by artificial manipulation of cell assemblies are not completely redundant-the observed behavioral redundancy does not imply cognitive redundancy, as different, but similar, memories may induce the same behavior.