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

It is not clear how, after a large perturbation, the brain explores the vast space of potential neuronal activity states to recover those compatible with consciousness. Here, we analyze recovery from pharmacologically induced coma to show that neuronal activity en route to consciousness is confined to a low-dimensional subspace. In this subspace, neuronal activity forms discrete metastable states persistent on the scale of minutes. The network of transitions that links these metastable states is structured such that some states form hubs that connect groups of otherwise disconnected states. Although many paths through the network are possible, to ultimately enter the activity state compatible with consciousness, the brain must first pass through these hubs in an orderly fashion. This organization of metastable states, along with dramatic dimensionality reduction, significantly simplifies the task of sampling the parameter space to recover the state consistent with wakefulness on a physiologically relevant timescale.

Sensory processing is distributed among many brain regions that interact via feedforward and feedback signaling. Neuronal oscillations have been shown to mediate intercortical feedforward and feedback interactions. Yet, the macroscopic structure of the multitude of such oscillations remains unclear. Here, we show that simple visual stimuli reliably evoke two traveling waves with spatial wavelengths that cover much of the cerebral hemisphere in awake mice. 30-50 Hz feedforward waves arise in primary visual cortex (V1) and propagate rostrally, while 3-6 Hz feedback waves originate in the association cortex and flow caudally. The phase of the feedback wave modulates the amplitude of the feedforward wave and synchronizes firing between V1 and parietal cortex. Altogether, these results provide direct experimental evidence that visual evoked traveling waves percolate through the cerebral cortex and coordinate neuronal activity across broadly distributed networks mediating visual processing. Processing sensory stimuli requires coordinated activation of neurons broadly distributed across many distant cortical sites, yet it is not clear how this coordination is accomplished in the brain. Here, the authors show that visual stimuli reliably evoke traveling waves of activity that percolate through the cortex and orchestrate neuronal firing across primary visual and association cortices.

Typically one expects that the intervals between consecutive occurrences of a particular behavior will have a characteristic time scale around which most observations are centered. Surprisingly, the timing of many diverse behaviors from human communication to animal foraging form complex self-similar temporal patterns reproduced on multiple time scales. We present a general framework for understanding how such scale invariance may arise in nonequilibrium systems, including those that regulate mammalian behaviors. We then demonstrate that the predictions of this framework are in agreement with detailed analysis of spontaneous mouse behavior observed in a simple unchanging environment. Neural systems operate on a broad range of time scales, from milliseconds to hours. We analytically show that such a separation between time scales could lead to scale-invariant dynamics without any fine tuning of parameters or other model-specific constraints. Our analyses reveal that the specifics of the distribution of resources or competition among several tasks are not essential for the expression of scale-free dynamics. Rather, we show that scale invariance observed in the dynamics of behavior can arise from the dynamics intrinsic to the brain.

In daily life, in the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness. A number of studies suggest that the awake, conscious state is not the default behavior of an assembly of neurons, but rather a very special state of activity that has to be actively maintained and curated to support its functional properties. Thus responsiveness is a feature that requires active maintenance, such as a homeostatic mechanism to balance excitation and inhibition. In this work we developed a method for monitoring such maintenance processes, focusing on a specific signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this effect to build a single-trial classifier which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a means for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice.

The level of activity of many animals including humans rises and falls with a period of ~ 24 hours. The intrinsic biological oscillator that gives rise to this circadian oscillation is driven by a molecular feedback loop with an approximately 24 hour cycle period and is influenced by the environment, most notably the light:dark cycle. In addition to the circadian oscillations, behavior of many animals is influenced by multiple oscillations occurring at faster-ultradian-time scales. These ultradian oscillations are also thought to be driven by feedback loops. While many studies have focused on identifying such ultradian oscillations, less is known about how the ultradian behavioral oscillations interact with each other and with the circadian oscillation. Decoding the coupling among the various physiological oscillators may be important for understanding how they conspire together to regulate the normal activity levels, as well in disease states in which such rhythmic fluctuations in behavior may be disrupted. Here, we use a wavelet-based cross-frequency analysis to show that different oscillations identified in spontaneous mouse behavior are coupled such that the amplitude of oscillations occurring at higher frequencies are modulated by the phase of the slower oscillations. The patterns of these interactions are different among different individuals. Yet this variability is not random. Differences in the pattern of interactions are confined to a low dimensional subspace where different patterns of interactions form clusters. These clusters expose the differences among individuals-males and females are preferentially segregated into different clusters. These sex-specific features of spontaneous behavior were not apparent in the spectra. Thus, our methodology reveals novel aspects of the structure of spontaneous animal behavior that are not observable using conventional methodology.

Mechanisms through which anesthetics disrupt neuronal activity are incompletely understood. In order to study anesthetic mechanisms in the intact brain, tight control over anesthetic pharmacology in a genetically and neurophysiologically accessible animal model is essential. Here, we developed a pharmacokinetic model that quantitatively describes propofol distribution into and elimination out of the brain. To develop the model, we used jugular venous catheters to infuse propofol in mice and measured propofol concentration in serial timed brain and blood samples using high performance liquid chromatography (HPLC). We then used adaptive fitting procedures to find parameters of a three compartment pharmacokinetic model such that all measurements collected in the blood and in the brain across different infusion schemes are fit by a single model. The purpose of the model was to develop target controlled infusion (TCI) capable of maintaining constant brain propofol concentration at the desired level. We validated the model for two different targeted concentrations in independent cohorts of experiments not used for model fitting. The predictions made by the model were unbiased, and the measured brain concentration was indistinguishable from the targeted concentration. We also verified that at the targeted concentration, state of anesthesia evidenced by slowing of the electroencephalogram and behavioral unresponsiveness was attained. Thus, we developed a useful tool for performing experiments necessitating use of anesthetics and for the investigation of mechanisms of action of propofol in mice.

Most cognitive functions require the brain to maintain immediately preceding stimuli in working memory. Here, using a human working memory task with multiple delays, we test the hypothesis that working memories are stored in a discrete set of stable neuronal activity configurations called attractors. We show that while discrete attractor dynamics can approximate working memory on a single time scale, they fail to generalize across multiple timescales. This failure occurs because at longer delay intervals the responses contain more information about the stimuli than can be stored in a discrete attractor model. We present a modeling approach that combines discrete attractor dynamics with activity-dependent plasticity. This model successfully generalizes across all timescales and correctly predicts intertrial interactions. Thus, our findings suggest that discrete attractor dynamics are insufficient to model working memory and that activity-dependent plasticity improves durability of information storage in attractor systems.Durability of information in human working memory across time intervals can be better explained by attractor dynamics that incorporate activity-dependent plasticity. Discrete attractor dynamics are less suited to model working memory when modeling the working memory time course.

The brain can become transiently disconnected from the environment while maintaining vivid, internally generated experiences. This so-called ‘dissociated state’ can occur in pathological conditions and under the influence of psychedelics or the anesthetic ketamine (KET). The cellular and circuit mechanisms producing the dissociative state remain poorly understood. We show in mice that KET causes spontaneously active neurons to become suppressed while previously silent neurons become spontaneously activated. This switch occurs in all cortical layers and different cortical regions, is induced by both systemic and cortical application of KET and is mediated by suppression of parvalbumin and somatostatin interneuron activity and inhibition of NMDA receptors and HCN channels. Combined, our results reveal two largely non-overlapping cortical neuronal populations—one engaged in wakefulness, the other contributing to the KET-induced brain state—and may lay the foundation for understanding how the brain might become disconnected from the surrounding environment while maintaining internal subjective experiences. Cichon et al. show that ketamine induces a switch in the active population of excitatory neurons across cortical layers and regions that contributes to impairments in sensory processing characteristic of a dissociative-like state.

Selection of behavioral responses to external stimuli is strongly influenced by internal states, such as intentions and expectations. These internal states are often attributed to higher-order brain functions. Yet here we show that even in the simple feeding network of Aplysia, external stimuli do not directly specify which motor output is expressed; instead, the motor output is specified by the state of the network at the moment of stimulation. The history-dependence of this network state manifests itself in the same way as do intentions and expectations in the behavior of higher animals. Remarkably, we find that activity-dependent plasticity of a synapse within the network itself, rather than some higher-order network, mediates one important aspect of the change in the network state. Through this mechanism, changes in the network state become an automatic consequence of the generation of behavior. Altogether, our findings suggest that intentions and expectations may emerge within behavior-generating networks themselves from the plasticity of the very processes that generate the behavior.