Alex Proekt

Feeding Neural Networks in the Mollusc Aplysia

Aplysia feeding is striking in that it is executed with a great deal of plasticity. At least in part, this flexibility is a result of the organization of the feeding neural network. To illustrate this, we primarily discuss motor programs triggered via stimulation of the command-like cerebral-buccal interneuron 2 (CBI-2). CBI-2 is interesting in that it can generate motor programs that serve opposing functions, i.e., programs can be ingestive or egestive. When programs are egestive, radula-closing motor neurons are activated during the protraction phase of the motor program. When programs are ingestive, radula-closing motor neurons are activated during retraction. When motor programs change in nature, activity in the radula-closing circuitry is altered. Thus, CBI-2 stimulation stereotypically activates the protraction and retraction circuitry, with protraction being generated first, and retraction immediately thereafter. In contrast, radula-closing motor neurons can be activated during either protraction or retraction. Which will occur is determined by whether other cerebral and buccal neurons are recruited, e.g. radula-closing motor neurons tend to be activated during retraction if a second CBI, CBI-3, is recruited. Fundamentally different motor programs are, therefore, generated because CBI-2 activates some interneurons in a stereotypic manner and other interneurons in a variable manner.

Recovery of consciousness is mediated by a network of discrete metastable activity states

Significance How does the brain recover consciousness after significant perturbations such as anesthesia? The simplest answer is that as the anesthetic washes out, the brain follows a steady and monotonic path toward consciousness. We show that this simple intuition is incorrect. We varied the anesthetic concentration to parametrically control the magnitude of perturbation to brain dynamics while analyzing the characteristics of neuronal activity during recovery of consciousness. We find that, en route to consciousness, the brain passes through several discrete activity states. Although transitions between certain of these activity states occur spontaneously, transitions between others are not observed. Thus, the network formed by these state transitions gives rise to an ordered sequence of states that mediates recovery of consciousness. 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.

Scale invariance in the dynamics of spontaneous behavior

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.

Loss of Consciousness Is Associated with Stabilization of Cortical Activity

What aspects of neuronal activity distinguish the conscious from the unconscious brain? This has been a subject of intense interest and debate since the early days of neurophysiology. However, as any practicing anesthesiologist can attest, it is currently not possible to reliably distinguish a conscious state from an unconscious one on the basis of brain activity. Here we approach this problem from the perspective of dynamical systems theory. We argue that the brain, as a dynamical system, is self-regulated at the boundary between stable and unstable regimes, allowing it in particular to maintain high susceptibility to stimuli. To test this hypothesis, we performed stability analysis of high-density electrocorticography recordings covering an entire cerebral hemisphere in monkeys during reversible loss of consciousness. We show that, during loss of consciousness, the number of eigenmodes at the edge of instability decreases smoothly, independently of the type of anesthetic and specific features of brain activity. The eigenmodes drift back toward the unstable line during recovery of consciousness. Furthermore, we show that stability is an emergent phenomenon dependent on the correlations among activity in different cortical regions rather than signals taken in isolation. These findings support the conclusion that dynamics at the edge of instability are essential for maintaining consciousness and provide a novel and principled measure that distinguishes between the conscious and the unconscious brain. SIGNIFICANCE STATEMENT What distinguishes brain activity during consciousness from that observed during unconsciousness? Answering this question has proven difficult because neither consciousness nor lack thereof have universal signatures in terms of most specific features of brain activity. For instance, different anesthetics induce different patterns of brain activity. We demonstrate that loss of consciousness is universally and reliably associated with stabilization of cortical dynamics regardless of the specific activity characteristics. To give an analogy, our analysis suggests that loss of consciousness is akin to depressing the damper pedal on the piano, which makes the sounds dissipate quicker regardless of the specific melody being played. This approach may prove useful in detecting consciousness on the basis of brain activity under anesthesia and other settings.

Changes of Internal State Are Expressed in Coherent Shifts of Neuromuscular Activity in Aplysia Feeding Behavior

The multitasking central pattern generator (CPG) that drives consummatory feeding behaviors of Aplysia can produce ingestive, egestive, and intermediate motor programs. External stimuli trigger the programs but, remarkably, do not directly specify which type of program is produced. Rather, recent work has proposed, the type of program is determined by the internal network state of the CPG that has developed in response to the previous history of the stimulation. Here we have tested a key prediction of this network-state hypothesis. If the network state has a real existence and governs real functional behavior, changes in the state should be seen as coherent, coordinated changes along many dimensions of interneuron and motor neuron activity, muscle contraction, and ultimately movement, that underlie functional behavior. In reduced neuromuscular preparations, we elicited repetitive motor programs by continued stimulation of the esophageal nerve while recording the firing of motor neurons B8, B15, B16, B4/5, and B48, and contractions of the accessory radula closer and I7-I10 muscles that respectively close and open the animals food-grasping organ, the radula. Using sonomicrometric techniques, we similarly recorded the movement of the radula in the complete buccal mass. Successive esophageal nerve programs indeed exhibited clear progressive changes in motor neuron firing, muscle contractions, and the phasing of radula movements within each cycle, from an initially intermediate or even ingestive character to a strongly egestive character. We conclude that the Aplysia feeding CPG really has a coherent internal network state whose dynamics are likely to be reflected in the real behavior of the animal.

Identification of a new neuropeptide precursor reveals a novel source of extrinsic modulation in the feeding system of aplysia

The Aplysia feeding system is advantageous for investigating the role of neuropeptides in behavioral plasticity. One family of Aplysia neuropeptides is the myomodulins (MMs), originally purified from one of the feeding muscles, the accessory radula closer (ARC). However, two MMs, MMc and MMe, are not encoded on the only known MM gene. Here, we identify MM gene 2 (MMG2), which encodes MMc and MMe and four new neuropeptides. We use matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to verify that these novel MMG2-derived peptides (MMG2-DPs), as well as MMc and MMe, are synthesized from the precursor. Using antibodies against the MMG2-DPs, we demonstrate that neuronal processes that stain for MMG2-DPs are found in the buccal ganglion, which contains the feeding network, and in the buccal musculature including the ARC muscle. Surprisingly, however, no immunostaining is observed in buccal neurons including the ARC motoneurons. In situ hybridization reveals only few MMG2-expressing neurons that are mostly located in the pedal ganglion. Using immunohistochemical and electrophysiological techniques, we demonstrate that some of these pedal neurons project to the buccal ganglion and are the likely source of the MMG2-DP innervation of the feeding network and musculature. We show that the MMG2-DPs are bioactive both centrally and peripherally: they bias egestive feeding programs toward ingestive ones, and they modulate ARC muscle contractions. The multiple actions of the MMG2-DPs suggest that these peptides play a broad role in behavioral plasticity and that the pedal-buccal projection neurons that express them are a novel source of extrinsic modulation of the feeding system of Aplysia.

Dynamical criticality during induction of anesthesia in human ECoG recordings

In this work we analyze electro-corticography (ECoG) recordings in human subjects during induction of anesthesia with propofol. We hypothesize that the decrease in responsiveness that defines the anesthetized state is concomitant with the stabilization of neuronal dynamics. To test this hypothesis, we performed a moving vector autoregressive analysis and quantified stability of neuronal dynamics using eigenmode decomposition of the autoregressive matrices, independently fitted to short sliding temporal windows. Consistent with the hypothesis we show that while the subject is awake, many modes of neuronal activity oscillations are found at the edge of instability. As the subject becomes anesthetized, we observe statistically significant increase in the stability of neuronal dynamics, most prominently observed for high frequency oscillations. Stabilization was not observed in phase randomized surrogates constructed to preserve the spectral signatures of each channel of neuronal activity. Thus, stability analysis offers a novel way of quantifying changes in neuronal activity that characterize loss of consciousness induced by general anesthetics.

State Dependence of Spike Timing and Neuronal Function in a Motor Pattern Generating Network

When sustained firing of a neuron is similar in different types of motor programs, its role in the generation of these programs is often similar. We investigated whether this is also the case for neurons involved in phase transition. In the Aplysia feeding central pattern generator (CPG), identified interneuron B64 starts firing at the transition between the protraction and the retraction phases of all types of motor programs, and its firing is sustained during the retraction phase. It was thought that B64 functions as a protraction terminator as it provides strong inhibitory input to protraction interneurons and motoneurons. Furthermore, premature activation of B64 can lead to premature termination of the protraction phase. Indeed, as we show here, B64 can terminate the protraction phase regardless of the type of motor program. However, B64 actually only functions as a protraction terminator in ingestive-like but not in egestive-like programs. This differential role of B64 results from a differential timing of the initiation of B64 spiking in the two types of programs. In turn, this differential timing of the initiation of B64 firing is determined by the internal state of the CPG. Thus, this study indicates the importance of the timing of initiation of firing in determining the functional role of a neuron and demonstrates that this role depends on the activity-dependent state of the network.

Predicting Adaptive Behavior in the Environment from Central Nervous System Dynamics

To generate adaptive behavior, the nervous system is coupled to the environment. The coupling constrains the dynamical properties that the nervous system and the environment must have relative to each other if adaptive behavior is to be produced. In previous computational studies, such constraints have been used to evolve controllers or artificial agents to perform a behavioral task in a given environment. Often, however, we already know the controller, the real nervous system, and its dynamics. Here we propose that the constraints can also be used to solve the inverse problem—to predict from the dynamics of the nervous system the environment to which they are adapted, and so reconstruct the production of the adaptive behavior by the entire coupled system. We illustrate how this can be done in the feeding system of the sea slug Aplysia. At the core of this system is a central pattern generator (CPG) that, with dynamics on both fast and slow time scales, integrates incoming sensory stimuli to produce ingestive and egestive motor programs. We run models embodying these CPG dynamics—in effect, autonomous Aplysia agents—in various feeding environments and analyze the performance of the entire system in a realistic feeding task. We find that the dynamics of the system are tuned for optimal performance in a narrow range of environments that correspond well to those that Aplysia encounter in the wild. In these environments, the slow CPG dynamics implement efficient ingestion of edible seaweed strips with minimal sensory information about them. The fast dynamics then implement a switch to a different behavioral mode in which the system ignores the sensory information completely and follows an internal “goal,” emergent from the dynamics, to egest again a strip that proves to be inedible. Key predictions of this reconstruction are confirmed in real feeding animals.

Cycle-to-cycle variability as an optimal behavioral strategy

Aplysia feeding behavior is highly variable from cycle to cycle. In some cycles, when the variability causes a mismatch between the animals movements and the requirements of the feeding task, the variability makes the behavior unsuccessful. We propose that the behavior is variable nevertheless because the variability serves a higher-order functional purpose. When the animal is faced with a new and only imperfectly known feeding task in each cycle, the variability implements a trial-and-error search through the space of possible feeding movements. Over many cycles, this may be the animals optimal strategy in an uncertain and changing feeding environment.