Emergent Dynamics and Spatio Temporal Patterns on Multiplex Neuronal Networks

We present a study on the emergence of a variety of spatio temporal patterns among neurons that are connected in a multiplex framework, with neurons on two layers with different functional couplings. With the Hindmarsh-Rose model for the dynamics of single neurons, we analyze the possible patterns of dynamics in each layer separately and report emergent patterns of activity like in-phase synchronized oscillations and amplitude death (AD) for excitatory coupling and anti-phase mixed-mode oscillations (MMO) in multi-clusters with phase regularities when the connections are inhibitory. When they are multiplexed, with neurons of one layer coupled with excitatory synaptic coupling and neurons of the other layer coupled with inhibitory synaptic coupling, we observe the transfer or selection of interesting patterns of collective behavior between the layers. While the revival of oscillations occurs in the layer with excitatory coupling, the transition from anti-phase to in-phase and vice versa is observed in the other layer with inhibitory synaptic coupling. We also discuss how the selection of these spatio temporal patterns can be controlled by tuning the intralayer or interlayer coupling strengths or increasing the range of non-local coupling. With one layer having electrical coupling while the other synaptic coupling of excitatory(inhibitory)type, we find in-phase(anti-phase) synchronized patterns of activity among neurons in both layers.

Hamiltonian energy and coexistence of hidden firing patterns from bidirectional coupling between two different neurons

In this paper, bidirectional-coupled neurons through an asymmetric electrical synapse are investigated. These coupled neurons involve 2D Hindmarsh–Rose (HR) and 2D FitzHugh–Nagumo (FN) neurons. The equilibria of the coupled neurons model are investigated, and their stabilities have revealed that, for some values of the electrical synaptic weight, the model under consideration can display either self-excited or hidden firing patterns. In addition, the hidden coexistence of chaotic bursting with periodic spiking, chaotic spiking with period spiking, chaotic bursting with a resting pattern, and the coexistence of chaotic spiking with a resting pattern are also found for some sets of electrical synaptic coupling. For all the investigated phenomena, the Hamiltonian energy of the model is computed. It enables the estimation of the amount of energy released during the transition between the various electrical activities. Pspice simulations are carried out based on the analog circuit of the coupled neurons to support our numerical results. Finally, an STM32F407ZE microcontroller development board is exploited for the digital implementation of the proposed coupled neurons model.

Synchronization through uncorrelated noise in excitatory-inhibitory networks

Gamma rhythms play a major role in many different processes in the brain, such as attention, working memory and sensory processing. While typically considered detrimental, counterintuitively noise can sometimes have beneficial effects on communication and information transfer. Recently, Meng and Riecke showed that synchronization of interacting networks of inhibitory neurons increases while synchronization within these networks decreases when neurons are subject to uncorrelated noise. However, experimental and modelling studies point towards an important role of the pyramidal-interneuronal network gamma (PING) mechanism in the cortex. Therefore, we investigated the effect of uncorrelated noise on the communication between excitatory-inhibitory networks producing gamma oscillations via a PING mechanism. Our results suggest that synaptic noise can have a supporting role in facilitating inter-regional communication and that noise-induced synchronization between networks is generated via a different mechanism than when synchronization is mediated by strong synaptic coupling. Noise-induced synchronization is achieved by lowering synchronization within networks which allows the respective other network to impose its own gamma rhythm resulting in synchronization between networks.

Dynamic Analysis and Synchronization with Unidirectional Coupling Involving Energy of a HR Neuron Under Electric Field and Memristive Autapse

Reliable neuron models play an important role in identifying the electrical activities, global bifurcation patterns, and dynamic mechanisms of neurons in complex electromagnetic environments. Considering the memristive autapse involving magnetic coupling has voltage-controlled, nonlinear, and memory, a 5-D HR neuron model containing magnetic field and electric field variables is established. Detailedly, the existence and stability conditions of the equilibrium point are determined by theoretical analysis, and the complex time-varying stability, saddle-node bifurcation, and Hopf bifurcation behaviors of the model are verified by numerical calculation. Interestingly, the system has a bistable structure consisting of quiescent state and period-1 and period-2 bursting modes near the subcritical Hopf bifurcation. It is noteworthy that the memristive autapse has a complex regulation mechanism for the bistable region so that three kinds of bistable coexisting structures and counterintuitive dynamic phenomena can be induced by appropriately adjusting the memristive autapse. Accordingly, the mechanism of positive feedback memristive autapse decreases its firing frequency, while negative feedback memristive autapse promotes its excitability was revealed by the fast-slow dynamic analysis. Extensive numerical results display that the system generally possesses period-adding bifurcation modes and comb-shaped chaotic structures. Furthermore, it is found that the firing modes and multistability regions of the system can be accurately predicted by analyzing the global dynamic behaviors of Hamilton energy. Importantly, it is verified that the unidirectional coupling controller involving energy is far more efficient and consumes less energy than electrical synaptic coupling in achieving complete synchronization with mismatched parameters.

Evolving interpretable plasticity for spiking networks

Continuous adaptation allows survival in an ever-changing world. Adjustments in the synaptic coupling strength between neurons are essential for this capability, setting us apart from simpler, hard-wired organisms. How these changes can be mathematically described at the phenomenological level, as so-called ‘plasticity rules’, is essential both for understanding biological information processing and for developing cognitively performant artificial systems. We suggest an automated approach for discovering biophysically plausible plasticity rules based on the definition of task families, associated performance measures and biophysical constraints. By evolving compact symbolic expressions, we ensure the discovered plasticity rules are amenable to intuitive understanding, fundamental for successful communication and human-guided generalization. We successfully apply our approach to typical learning scenarios and discover previously unknown mechanisms for learning efficiently from rewards, recover efficient gradient-descent methods for learning from target signals, and uncover various functionally equivalent STDP-like rules with tuned homeostatic mechanisms.

Recurrence-mediated suprathreshold stochastic resonance

It has previously been shown that the encoding of time-dependent signals by feedforward networks (FFNs) of processing units exhibits suprathreshold stochastic resonance (SSR), which is an optimal signal transmission for a finite level of independent, individual stochasticity in the single units. In this study, a recurrent spiking network is simulated to demonstrate that SSR can be also caused by network noise in place of intrinsic noise. The level of autonomously generated fluctuations in the network can be controlled by the strength of synapses, and hence the coding fraction (our measure of information transmission) exhibits a maximum as a function of the synaptic coupling strength. The presence of a coding peak at an optimal coupling strength is robust over a wide range of individual, network, and signal parameters, although the optimal strength and peak magnitude depend on the parameter being varied. We also perform control experiments with an FFN illustrating that the optimized coding fraction is due to the change in noise level and not from other effects entailed when changing the coupling strength. These results also indicate that the non-white (temporally correlated) network noise in general provides an extra boost to encoding performance compared to the FFN driven by intrinsic white noise fluctuations.

Adult mouse dorsal root ganglia neurons form aberrant glutamatergic connections in dissociated cultures

Cultured sensory neurons can exhibit complex activity patterns following stimulation in terms of increased excitability and interconnected responses of multiple neurons. Although these complex activity patterns suggest a network-like configuration, research so far had little interest in synaptic network formation ability of the sensory neurons. To identify interaction profiles of Dorsal Root Ganglia (DRG) neurons and explore their putative connectivity, we developed an in vitro experimental approach. A double transgenic mouse model, expressing genetically encoded calcium indicator (GECI) in their glutamatergic neurons, was produced. Dissociated DRG cultures from adult mice were prepared with a serum-free protocol and no additional growth factors or cytokines were utilized for neuronal sensitization. DRG neurons were grown on microelectrode arrays (MEA) to induce stimulus-evoked activity with a modality-free stimulation strategy. With an almost single-cell level electrical stimulation, spontaneous and evoked activity of GCaMP6s expressing neurons were detected under confocal microscope. Typical responses were analyzed, and correlated calcium events were detected across individual DRG neurons. Next, correlated responses were successfully blocked by glutamatergic receptor antagonists, which indicated functional synaptic coupling. Immunostaining confirmed the presence of synapses mainly in the axonal terminals, axon-soma junctions and axon-axon intersection sites. Concisely, the results presented here illustrate a new type of neuron-to-neuron interaction in cultured DRG neurons conducted through synapses. The developed assay can be a valuable tool to analyze individual and collective responses of the cultured sensory neurons.

Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

Natural Brains and Motivated, Emotional Mind

We know that the brain is the seat of the mind. Constructing the reductive model of the conscious mind requires an indication of the laws according to which the mind emerges from biophysical processes occurring in natural brains. Because in Part I, the authors presented the theoretical model referring to the ideal structures of the imagined neural network, we now have easier task, because we need to indicate in the brains of the living beings those processes that functionally correspond to our postulates. Such suitability is not guaranteed by known processes occurring in specialized parts of the brain. The role of the primary sensory areas is a detailed analysis of sensory stimuli with specific modality. They result in analysis of the meaning of all useful stimuli and their interpretation used in various parts of the cortex. The high specialization of individual cortex areas is striking and are the result of evolutionary development of the brain. New brain structures, such as the new cortex, were added on the outskirts of existing structures, improving their performance in the ever more demanding environments, where other intelligent beings ravened. But even as we know the brain organization, we struggle to understand how it works. How neurons that make the brain work together to create the conscious mind. To discover functionally effective processes in the brain, one need to reach for the biophysical properties of the astrocyt-neural network. In this chapter, the authors suggest that some concepts of neuro-electro-dynamics and the phenomena of neuro- and synapto-genesis as well as synaptic couplings may explain the processes of categorization, generalization and association leading to the formation of extensive, semihierarchical brain structures constituting neural representations of perceptions, objects and phenomena. Natural brains meet the embodiment condition. They are products of evolution, so they have intentionality, their own goals and needs. So they can naturally show emotions, drives and instincts that motivate to act. This determines the nature of constructed mental representations. They are the subject of psychological research, which shows the motivation of pain and pleasure in the field of intelligent activities, as well as the motivation of curiosity and the need for understanding in the domain of propositional and phenomenal consciousness. They describe the way pain is felt in organisms as basic quale. The role of other qualia for “how-it-is-like to feel something” and their subjective character was explained, as well as their interspecies specificity was characterized. In this chapter, the authors present an elementary biophysical phenomenon, that is a flash of consciousness. This phenomenon is synaptic coupling formed in the course of learning. They justify that the stream of such phenomena is the foundation of consciousness. They also point out that the astrocytic-neural network meets all the conditions required to generate conscious sensations.

Coordinated hippocampal-thalamic-cortical communication crucial for engram dynamics underneath systems consolidation

Systems consolidation refers to the reorganization of memory over time across brain regions. Despite recent advancements in unravelling engrams and circuits essential for this process, the exact mechanisms behind engram cell dynamics and the role of associated pathways remain poorly understood. Here, we propose a computational model to address this knowledge gap that consists of a multi-region spiking recurrent neural network subject to biologically-plausible synaptic plasticity mechanisms. By coordinating the timescales of synaptic plasticity throughout the network and incorporating a hippocampus-thalamus-cortex circuit, our model is able to couple engram reactivations across these brain regions and thereby reproduce key dynamics of cortical and hippocampal engram cells along with their interdependencies. Decoupling hippocampal-thalamic-cortical activity disrupts engram dynamics and systems consolidation. Our modeling work also yields several testable predictions: engram cells in mediodorsal thalamus are activated in response to partial cues in recent and remote recall and are crucial for systems consolidation; hippocampal and thalamic engram cells are essential for coupling engram reactivations between subcortical and cortical regions; inhibitory engram cells have region-specific dynamics with coupled reactivations; inhibitory input to mediodorsal thalamus is critical for systems consolidation; and thalamocortical synaptic coupling is predictive of cortical engram dynamics and the retrograde amnesia pattern induced by hippocampal damage. Overall, our results suggest that systems consolidation emerges from concerted interactions among engram cells in distributed brain regions enabled by coordinated synaptic plasticity timescales in multisynaptic subcortical-cortical circuits.