Adaptive robustness through incoherent signaling mechanisms in a regenerative brain.

Animal behavior emerges from collective dynamics of neurons, making it vulnerable to damage. Paradoxically, many organisms exhibit a remarkable ability to maintain significant behavior even after large-scale neural injury. Molecular underpinnings of this extreme robustness remain largely unknown. Here, we develop a quantitative pipeline to measure long-lasting latent states in planarian flatworm behaviors during whole-brain regeneration. By combining >20,000 animal trials with neural network modeling, we show that long-range volumetric peptidergic signals allow the planarian to rapidly restore coarse behavior output after large perturbations to the nervous system, while slow restoration of small-molecule neuromodulator functions refines precision. This relies on the different time and length scales of neuropeptide and small-molecule transmission to generate incoherent patterns of neural activity that competitively regulate behavior. Controlling behavior through opposing communication mechanisms creates a more robust system than either alone and may serve as a generalizable approach for constructing robust neural networks.

A hierarchy index for networks in the brain reveals a complex entangled organizational structure.

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general.

Information Geometry Theoretic Measures for Characterizing Neural Information Processing from Simulated EEG Signals.

In this work, we explore information geometry theoretic measures for characterizing neural information processing from EEG signals simulated by stochastic nonlinear coupled oscillator models for both healthy subjects and Alzheimer's disease (AD) patients with both eyes-closed and eyes-open conditions. In particular, we employ information rates to quantify the time evolution of probability density functions of simulated EEG signals, and employ causal information rates to quantify one signal's instantaneous influence on another signal's information rate. These two measures help us find significant and interesting distinctions between healthy subjects and AD patients when they open or close their eyes. These distinctions may be further related to differences in neural information processing activities of the corresponding brain regions, and to differences in connectivities among these brain regions. Our results show that information rate and causal information rate are superior to their more traditional or established information-theoretic counterparts, i.e., differential entropy and transfer entropy, respectively. Since these novel, information geometry theoretic measures can be applied to experimental EEG signals in a model-free manner, and they are capable of quantifying non-stationary time-varying effects, nonlinearity, and non-Gaussian stochasticity presented in real-world EEG signals, we believe that they can form an important and powerful tool-set for both understanding neural information processing in the brain and the diagnosis of neurological disorders, such as Alzheimer's disease as presented in this work.

Revisiting neural information, computing and linking capacity.

Neural information theory represents a fundamental method to model dynamic relations in biological systems. However, the notion of information, its representation, its content and how it is processed are the subject of fierce debates. Since the limiting capacity of neuronal links strongly depends on how neurons are hypothesized to work, their operating modes are revisited by analyzing the differences between the results of the communication models published during the past seven decades and those of the recently developed generalization of the classical information theory. It is pointed out that the operating mode of neurons is in resemblance with an appropriate combination of the formerly hypothesized analog and digital working modes; furthermore that not only the notion of neural information and its processing must be reinterpreted. Given that the transmission channel is passive in Shannon's model, the active role of the transfer channels (the axons) may introduce further transmission limits in addition to the limits concluded from the information theory. The time-aware operating model enables us to explain why (depending on the researcher's point of view) the operation can be considered either purely analog or purely digital.

Neural representations of the content and production of human vocalization.

Speech, as the spoken form of language, is fundamental for human communication. The phenomenon of covert inner speech implies functional independence of speech content and motor production. However, it remains unclear how a flexible mapping between speech content and production is achieved on the neural level. To address this, we recorded magnetoencephalography in humans performing a rule-based vocalization task. On each trial, vocalization content (one of two vowels) and production form (overt or covert) were instructed independently. Using multivariate pattern analysis, we found robust neural information about vocalization content and production, mostly originating from speech areas of the left hemisphere. Production signals dynamically transformed upon presentation of the content cue, whereas content signals remained largely stable throughout the trial. In sum, our results show dissociable neural representations of vocalization content and production in the human brain and provide insights into the neural dynamics underlying human vocalization.

Prenatal cyanuric acid exposure induced spatial learning impairments associated with alteration of acetylcholine-mediated neural information flow at the hippocampal CA3-CA1 synapses of male rats.

Cyanuric acid (CA) is reported to induce nephrotoxicity but its toxic effect is not fully known. Prenatal CA exposure causes neurodevelopmental deficits and abnormal behavior in spatial learning ability. Dysfunction of the acetyl-cholinergic system in neural information processing is correlated with spatial learning impairment and was found in the previous reports of CA structural analogue melamine. To further investigate the neurotoxic effects and the potential mechanism, the acetylcholine (ACh) level was detected in the rats which were exposed to CA during the whole of gestation. Local field potentials (LFPs) were recorded when rats infused with ACh or cholinergic receptor agonist into hippocampal CA3 or CA1 region were trained in the Y-maze task. We found the expression of ACh in the hippocampus was significantly reduced in dose-dependent manners. Intra-hippocampal infusion of ACh into the CA1 but not the CA3 region could effectively mitigate learning deficits induced by CA exposure. However, activation of cholinergic receptors did not rescue the learning impairments. In the LFP recording, we found that the hippocampal ACh infusions could enhance the values of phase synchronization between CA3 and CA1 regions in theta and alpha oscillations. Meanwhile, the reduction in the coupling directional index and the strength of CA3 driving CA1 in the CA-treated groups was also reversed by the ACh infusions. Our findings are consistent with the hypothesis and provide the first evidence that prenatal CA exposure induced spatial learning defect is attributed to the weakened ACh-mediated neuronal coupling and NIF in the CA3-CA1 pathway.

Maternal immune activation-induced proBDNF-mediated neural information processing dysfunction at hippocampal CA3-CA1 synapses associated with memory deficits in offspring.

Prenatal exposure to maternal infection increases the risk of offspring developing schizophrenia in adulthood. Current theories suggest that the consequences of MIA on mBDNF secretion may underlie the increased risk of cognitive disorder. There is little evidence for whether the expression of its precursor, proBDNF, is changed and how proBDNF-mediated signaling may involve in learning and memory. In this study, proBDNF levels were detected in the hippocampal CA1 and CA3 regions of male adult rats following MIA by prenatal polyI:C exposure. Behaviorally, learning and memory were assessed in contextual fear conditioning tasks. Local field potentials were recorded in the hippocampal CA3-CA1 pathway. The General Partial Directed Coherence approach was utilized to identify the directional alternation of neural information flow between CA3 and CA1 regions. EPSCs were recorded in CA1 pyramidal neurons to explore a possible mechanism involving the proBDNF-p75NTR signaling pathway. Results showed that the expression of proBDNF in the polyI:C-treated offspring was abnormally enhanced in both CA3 and CA1 regions. Meanwhile, the mBDNF expression was reduced in both hippocampal regions. Intra-hippocampal CA1 but not CA3 injection with anti-proBDNF antibody and p75NTR inhibitor TAT-Pep5 effectively mitigated the contextual memory deficits. Meanwhile, reductions in the phase synchronization between CA3 and CA1 and the coupling directional indexes from CA3 to CA1 were enhanced by the intra-CA1 infusions. Moreover, blocking proBDNF/p75NTR signaling could reverse the declined amplitude of EPSCs in CA1 pyramidal neurons, indicating the changes in postsynaptic information processing in the polyI:C-treated offspring. Therefore, the changes in hippocampal proBDNF activity in prenatal polyI:C exposure represent a potential mechanism involved in NIF disruption leading to contextual memory impairments.

Towards Generalizing the Information Theory for Neural Communication.

Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes' timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon's idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory.

The structural aspects of neural dynamics and information flow.

BACKGROUND: Neurons have specialized structures that facilitate information transfer using electrical and chemical signals. Within the perspective of neural computation, the neuronal structure is an important prerequisite for the versatile computational capabilities of neurons resulting from the integration of diverse synaptic input patterns, complex interactions among the passive and active dendritic local currents, and the interplay between dendrite and soma to generate action potential output. For this, characterization of the relationship between the structure and neuronal spike dynamics could provide essential information about the cellular-level mechanism supporting neural computations. RESULTS: This work describes simulations and an information-theoretic analysis to investigate how specific neuronal structure affects neural dynamics and information processing. Correlation analysis on the Allen Cell Types Database reveals biologically relevant structural features that determine neural dynamics-eight highly correlated structural features are selected as the primary set for characterizing neuronal structures. These features are used to characterize biophysically realistic multi-compartment mathematical models for primary neurons in the direct and indirect hippocampal pathways consisting of the pyramidal cells of Cornu Ammonis 1 (CA1) and CA3 and the granule cell in the dentate gyrus (DG). Simulations reveal that the dynamics of these neurons vary depending on their specialized structures and are highly sensitive to structural modifications. Information-theoretic analysis confirms that structural factors are critical for versatile neural information processing at a single-cell and a neural circuit level; not only basic AND/OR but also linearly non-separable XOR functions can be explained within the information-theoretic framework. CONCLUSIONS: Providing quantitative information on the relationship between the structure and the dynamics/information flow of neurons, this work would help us understand the design and coding principles of biological neurons and may be beneficial for designing biologically plausible neuron models for artificial intelligence (AI) systems.

Multiscale 'whole-cell' models to study neural information processing - New insights from fly photoreceptor studies.

Understanding a neuron's input-output relationship is a longstanding challenge. Arguably, these signalling dynamics can be better understood if studied at three levels of analysis: computational, algorithmic and implementational (Marr, 1982). But it is difficult to integrate such analyses into a single platform that can realistically simulate neural information processing. Multiscale dynamical "whole-cell" modelling, a recent systems biology approach, makes this possible. Dynamical "whole-cell" models are computational models that aim to account for the integrated function of numerous genes or molecules to behave like virtual cells in silico. However, because constructing such models is laborious, only a couple of examples have emerged since the first one, built for Mycoplasma genitalium bacterium, was reported in 2012. Here, we review dynamic "whole-cell" neuron models for fly photoreceptors and how these have been used to study neural information processing. Specifically, we review how the models have helped uncover the mechanisms and evolutionary rules of quantal light information sampling and integration, which underlie light adaptation and further improve our understanding of insect vision.