Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks

Wavenumber-dependent transmission of subthreshold waves on electrical synapses network model of Caenorhabditis elegans

Recent experimental studies showed that electrically coupled neural networks like in mammalian inferior olive nucleus generate synchronized rhythmic activity by the subthreshold sinusoidal-like oscillations of the membrane voltage. Understanding the basic mechanism and its implication of such phenomena in the nervous system bears fundamental importance and requires preemptively the connectome information of a given nervous system. Inspired by these necessities of developing a theoretical and computational model to this end and, however, in the absence of connectome information for the inferior olive nucleus, here we investigated interference phenomena of the subthreshold oscillations in the reference system Caenorhabditis elegans for which the structural anatomical connectome was completely known recently. We evaluated how strongly the sinusoidal wave was transmitted between arbitrary two cells in the model network. The region of cell-pairs that are good at transmitting waves changed according to the wavenumber of the wave, for which we named a wavenumber-dependent transmission map. Also, we unraveled that (1) the transmission of all cell-pairs disappeared beyond a threshold wavenumber, (2) long distance and regular patterned transmission existed in the body-wall muscles part of the model network, and (3) major hub cell-pairs of the transmission were identified for many wavenumber conditions. A theoretical and computational model presented in this study provided fundamental insight for understanding how the multi-path constructive/destructive interference of the subthreshold oscillations propagating on electrically coupled neural networks could generate wavenumber-dependent synchronized rhythmic activity.

Daily rhythms drive dynamism in sleep, oscillations and interneuron firing, while excitatory firing remains stable across 24 h

The adaptation to the daily 24-h light-dark cycle is ubiquitous across animal species and is crucial for maintaining fitness. This free-running cycle occurs innately within multiple bodily systems, such as endogenous circadian rhythms in clock-gene expression and synaptic plasticity. These phenomena are well studied; however, it is unknown if and how the 24-h clock affects electrophysiologic network function in vivo. The hippocampus is a region of interest for long timescale (>8 h) studies because it is critical for cognitive function and exhibits time-of-day effects in learning. We recorded single cell spiking activity and local field potentials (LFPs) in mouse hippocampus across the 24-h (12:12-h light/dark) cycle to quantify how electrophysiological network function is modulated across the 24-h day. We found that while inhibitory population firing rates and LFP oscillations exhibit modulation across the day, average excitatory population firing is static. This excitatory stability, despite inhibitory dynamism, may enable consistent around-the-clock function of neural circuits.

The motifs of radical embodied neuroscience

In this paper, I analyse how the emerging scientific framework of radical embodied neuroscience is different from contemporary mainstream cognitive neuroscience. To do so, I propose the notion of motif to enrich the philosophical toolkit of cognitive neuroscience. This notion can be used to characterize the guiding ideas of any given scientific framework in psychology and neuroscience. Motifs are highly unconstrained, open-ended concepts that support equally open-ended families of explanations. Different scientific frameworks-e.g., psychophysics or cognitive neuroscience-provide these motifs to answer the overarching themes of these disciplines, such as the relationship between stimuli and sensations or the proper methods of the sciences of the mind. Some motifs of mainstream cognitive neuroscience are the motif of encoding, the motif of input-output systems, and the motif of algorithms. The two first ones answer the question about the relationship between stimuli, sensations and experience (e.g., stimuli are input and are encoded by brain structures). The latter one answers the question regarding the mechanism of cognition and experience. The three of them are equally unconstrained and open-ended, and they serve as an umbrella for different kinds of explanation-i.e., different positions regarding what counts as a code or as an input. Along with the articulation of the notion of motif, the main aim of this article is to present three motifs for radical embodied neuroscience: the motif of complex stimulation, the motif of organic behaviour and the motif of resonance.

Neuronal functional connectivity is impaired in a layer dependent manner near chronically implanted intracortical microelectrodes in C57BL6 wildtype mice

Objective.This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near chronically implanted microelectrodes. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how the FBR affects the functional stability of neural circuits near implanted brain-computer interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.Approach.This study utilized single-shank, 16-channel,100µm site-spacing Michigan-style microelectrodes (3 mm length, 703µm2 site area) that span all cortical layers and the hippocampal CA1 region. Sex balanced C57BL6 wildtype mice (11-13 weeks old) received perpendicularly implanted microelectrode in left primary visual cortex. Electrophysiological recordings were performed during both spontaneous activity and visual sensory stimulation. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of local field potential (LFP) and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.Main results. The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.Significance. This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

Neuronal functional connectivity is impaired in a layer dependent manner near the chronically implanted microelectrodes

Objective

This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near the chronically implanted microelectrode. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how does the FBR impact affect the functional stability of neural circuits near implanted Brain-Computer Interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.

Approach

This study utilized multisite Michigan-style microelectrodes that span all cortical layers and the hippocampal CA1 region to collect spontaneous and visually-evoked electrophysiological activity. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of LFP and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.

Main Results

The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.

Significance

This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.

How to incorporate biological insights into network models and why it matters

Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.

Neural Aspects of Prospective Control through Resonating Taus in an Interceptive Timing Task

High-density electroencephalography from visual and motor cortices in addition to kinematic hand and target movement recordings were used to investigate τ-coupling between brain activity patterns and physical movements in an interceptive timing task. Twelve adult participants were presented with a target car moving towards a destination at three constant accelerations, and an effector dot was available to intercept the car at the destination with a swift movement of the finger. A τ-coupling analysis was used to investigate involvement of perception and action variables at both the ecological scale of behavior and neural scale. By introducing the concept of resonance, the underlying dynamics of interceptive actions were investigated. A variety of one- and two-scale τ-coupling analyses showed significant differences in distinguishing between slow, medium, and fast target speed when car motion and finger movement, VEP and MRP brain activity, VEP and car motion, and MRP and finger movement were involved. These results suggested that the temporal structure present at the ecological scale is reflected at the neural scale. The results further showed a strong effect of target speed, indicating that τ-coupling constants k and k_res increased with higher speeds of the moving target. It was concluded that τ-coupling can be considered a valuable tool when combining different types of variables at both the ecological and neural levels of analysis.

Heterogeneous mechanisms for synchronization of networks of resonant neurons under different E/I balance regimes

Rhythmic synchronization of neuronal firing patterns is a widely present phenomenon in the brain-one that seems to be essential for many cognitive processes. A variety of mechanisms contribute to generation and synchronization of network oscillations, ranging from intrinsic cellular excitability to network mediated effects. However, it is unclear how these mechanisms interact together. Here, using computational modeling of excitatory-inhibitory neural networks, we show that different synchronization mechanisms dominate network dynamics at different levels of excitation and inhibition (i.e. E/I levels) as synaptic strength is systematically varied. Our results show that with low synaptic strength networks are sensitive to external oscillatory drive as a synchronizing mechanism-a hallmark of resonance. In contrast, in a strongly-connected regime, synchronization is driven by network effects via the direct interaction between excitation and inhibition, and spontaneous oscillations and cross-frequency coupling emerge. Unexpectedly, we find that while excitation dominates network synchrony at low excitatory coupling strengths, inhibition dominates at high excitatory coupling strengths. Together, our results provide novel insights into the oscillatory modulation of firing patterns in different excitation/inhibition regimes.

Network resonance can be generated independently at distinct levels of neuronal organization

Resonance is defined as maximal response of a system to periodic inputs in a limited frequency band. Resonance may serve to optimize inter-neuronal communication, and has been observed at multiple levels of neuronal organization. However, it is unknown how neuronal resonance observed at the network level is generated and how network resonance depends on the properties of the network building blocks. Here, we first develop a metric for quantifying spike timing resonance in the presence of background noise, extending the notion of spiking resonance for in vivo experiments. Using conductance-based models, we find that network resonance can be inherited from resonances at other levels of organization, or be intrinsically generated by combining mechanisms across distinct levels. Resonance of membrane potential fluctuations, postsynaptic potentials, and single neuron spiking can each be generated independently of resonance at any other level and be propagated to the network level. At all levels of organization, interactions between processes that give rise to low- and high-pass filters generate the observed resonance. Intrinsic network resonance can be generated by the combination of filters belonging to different levels of organization. Inhibition-induced network resonance can emerge by inheritance from resonance of membrane potential fluctuations, and be sharpened by presynaptic high-pass filtering. Our results demonstrate a multiplicity of qualitatively different mechanisms that can generate resonance in neuronal systems, and provide analysis tools and a conceptual framework for the mechanistic investigation of network resonance in terms of circuit components, across levels of neuronal organization.

How one part of the brain responds to periodic input from another part depends on resonant circuit properties. Resonance is a basic property of physical systems, and has been experimentally observed at various levels of neuronal organization both in vitro and in vivo. Yet how resonance is generated in neuronal networks is largely unknown. In particular, whether resonance can be generated directly at the level of a network of spiking neurons remains to be determined. Using detailed biophysical modeling, we develop a conceptual framework according to which resonance at a given level of organization is generated by the interplay of low- and high-pass filters, implemented at either the same or across levels of neuronal organization. We tease apart representative, biophysically-plausible generative mechanisms of resonance at four different levels of organization: membrane potential fluctuations, single neuron spiking, synaptic transmission, and neuronal networks. We identify conditions under which resonance at one level can be inherited to another level of organization, provide conclusive evidence that resonance at each level can be generated without resonance at any other level, and describe a number of representative routes to network resonance. The proposed framework facilitates the investigation of resonance in neuronal systems.

Hippocampal neurons' cytosolic and membrane-bound ribosomal transcript profiles are differentially regulated by learning and subsequent sleep

Sleep loss disrupts consolidation of hippocampus-dependent memory. To understand the cellular basis for this effect, we quantified RNAs associated with translating ribosomes in cytosol and on cellular membranes of different hippocampal neuron populations. Our analysis suggests that while sleep loss (but not learning) alters numerous ribosomal transcripts in cytosol, learning has dramatic effects on transcript profiles for less–well-characterized membrane-bound ribosomes. We demonstrate that postlearning sleep deprivation occludes already minimal learning-driven changes on cytosolic ribosomes. It simultaneously alters transcripts associated with metabolic and biosynthetic processes in membrane-bound ribosomes in excitatory hippocampal neurons and highly active, putative “engram” neurons, respectively. Together, these findings provide insights into the cellular mechanisms altered by learning and their disruption by subsequent sleep loss.

The hippocampus is essential for consolidating transient experiences into long-lasting memories. Memory consolidation is facilitated by postlearning sleep, although the underlying cellular mechanisms are largely unknown. We took an unbiased approach to this question by using a mouse model of hippocampally mediated, sleep-dependent memory consolidation (contextual fear memory). Because synaptic plasticity is associated with changes to both neuronal cell membranes (e.g., receptors) and cytosol (e.g., cytoskeletal elements), we characterized how these cell compartments are affected by learning and subsequent sleep or sleep deprivation (SD). Translating ribosome affinity purification was used to profile ribosome-associated RNAs in different subcellular compartments (cytosol and membrane) and in different cell populations (whole hippocampus, Camk2a+ neurons, or highly active neurons with phosphorylated ribosomal subunit S6 [pS6+]). We examined how transcript profiles change as a function of sleep versus SD and prior learning (contextual fear conditioning; CFC). While sleep loss altered many cytosolic ribosomal transcripts, CFC altered almost none, and CFC-driven changes were occluded by subsequent SD. In striking contrast, SD altered few transcripts on membrane-bound (MB) ribosomes, while learning altered many more (including long non-coding RNAs [lncRNAs]). The cellular pathways most affected by CFC were involved in structural remodeling. Comparisons of post-CFC MB transcript profiles between sleeping and SD mice implicated changes in cellular metabolism in Camk2a+ neurons and protein synthesis in highly active pS6+ (putative “engram”) neurons as biological processes disrupted by SD. These findings provide insights into how learning affects hippocampal neurons and suggest that the effects of SD on memory consolidation are cell type and subcellular compartment specific.

Dynamical Mechanism Underlying Scale-Free Network Reorganization in Low Acetylcholine States Corresponding to Slow Wave Sleep

Sleep is indispensable for most animals' cognitive functions, and is hypothesized to be a major factor in memory consolidation. Although we do not fully understand the mechanisms of network reorganisation driving memory consolidation, available data suggests that sleep-associated neurochemical changes may be important for such processes. In particular, global acetylcholine levels change across the sleep/wake cycle, with high cholinergic tone during wake and REM sleep and low cholinergic tone during slow wave sleep. Furthermore, experimental perturbation of cholinergic tone has been shown to impact memory storage. Through in silico modeling of neuronal networks, we show how spiking dynamics change in highly heterogenous networks under varying levels of cholinergic tone, with neuronal networks under high cholinergic modulation firing asynchronously and at high frequencies, while those under low cholinergic modulation exhibit synchronous patterns of activity. We further examined the network's dynamics and its reorganization mediated via changing levels of acetylcholine within the context of different scale-free topologies, comparing network activity within the hub cells, a small group of neurons having high degree connectivity, and with the rest of the network. We show a dramatic, state-dependent change in information flow throughout the network, with highly active hub cells integrating information in a high-acetylcholine state, and transferring it to rest of the network in a low-acetylcholine state. This result is experimentally corroborated by frequency-dependent frequency changes observed in vivo experiments. Together, these findings provide insight into how new neurons are recruited into memory traces during sleep, a mechanism which may underlie system memory consolidation.

Implicit-explicit gradient of nondual awareness or consciousness as such

Consciousness is multi-dimensional but is most often portrayed with a two-dimensional (2D) map that has global levels or states on one axis and phenomenal contents on the other. On this map, awareness is conflated either with general alertness or with phenomenal content. This contributes to ongoing difficulties in the scientific understanding of consciousness. Previously, I have proposed that consciousness as such or nondual awareness-a basic non-conceptual, non-propositional awareness in itself free of subject-object fragmentation-is a unique kind that cannot be adequately specified by this 2D map of states and contents. Here, I propose an implicit-explicit gradient of nondual awareness to be added as the z-axis to the existing 2D map of consciousness. This gradient informs about the degree to which nondual awareness is manifest in any experience, independent of the specifics of global state or local content. Alternatively, within the multi-dimensional state space model of consciousness, nondual awareness can be specified by several vectors, each representing one of its properties. In the first part, I outline nondual awareness or consciousness as such in terms of its phenomenal description, its function and its neural correlates. In the second part, I explore the implicit-explicit gradient of nondual awareness and how including it as an additional axis clarifies certain features of everyday dualistic experiences and is especially relevant for understanding the unitary and nondual experiences accessed via different contemplative methods, mind-altering substances or spontaneously.

Acetylcholine-gated current translates wake neuronal firing rate information into a spike timing-based code in Non-REM sleep, stabilizing neural network dynamics during memory consolidation

Sleep is critical for memory consolidation, although the exact mechanisms mediating this process are unknown. Combining reduced network models and analysis of in vivo recordings, we tested the hypothesis that neuromodulatory changes in acetylcholine (ACh) levels during non-rapid eye movement (NREM) sleep mediate stabilization of network-wide firing patterns, with temporal order of neurons’ firing dependent on their mean firing rate during wake. In both reduced models and in vivo recordings from mouse hippocampus, we find that the relative order of firing among neurons during NREM sleep reflects their relative firing rates during prior wake. Our modeling results show that this remapping of wake-associated, firing frequency-based representations is based on NREM-associated changes in neuronal excitability mediated by ACh-gated potassium current. We also show that learning-dependent reordering of sequential firing during NREM sleep, together with spike timing-dependent plasticity (STDP), reconfigures neuronal firing rates across the network. This rescaling of firing rates has been reported in multiple brain circuits across periods of sleep. Our model and experimental data both suggest that this effect is amplified in neural circuits following learning. Together our data suggest that sleep may bias neural networks from firing rate-based towards phase-based information encoding to consolidate memories.

We show that neuromodulatory changes during non-rapid eye movement (NREM) sleep generate stable spike timing relationships between neurons, the ordering of which reflects the neurons’ relative firing rates during wake. Learning-dependent ordering of firing in the hippocampus during NREM, acting in tandem with spike timing-dependent plasticity, reconfigures neuronal firing rates across the hippocampal network. This “rescaling” of neuronal firing rates has recently been reported in multiple brain circuits across periods of sleep. Together, our results suggest that the brain is remapping frequency-biased representations of information formed during wake into timing biased-representations during NREM sleep.

The Engram's Dark Horse: How Interneurons Regulate State-Dependent Memory Processing and Plasticity

Brain states such as arousal and sleep play critical roles in memory encoding, storage, and recall. Recent studies have highlighted the role of engram neurons-populations of neurons activated during learning-in subsequent memory consolidation and recall. These engram populations are generally assumed to be glutamatergic, and the vast majority of data regarding the function of engram neurons have focused on glutamatergic pyramidal or granule cell populations in either the hippocampus, amygdala, or neocortex. Recent data suggest that sleep and wake states differentially regulate the activity and temporal dynamics of engram neurons. Two potential mechanisms for this regulation are either via direct regulation of glutamatergic engram neuron excitability and firing, or via state-dependent effects on interneuron populations-which in turn modulate the activity of glutamatergic engram neurons. Here, we will discuss recent findings related to the roles of interneurons in state-regulated memory processes and synaptic plasticity, and the potential therapeutic implications of understanding these mechanisms.

Normal and Abnormal Sharp Wave Ripples in the Hippocampal-Entorhinal Cortex System: Implications for Memory Consolidation, Alzheimer's Disease, and Temporal Lobe Epilepsy

The appearance of hippocampal sharp wave ripples (SWRs) is an electrophysiological biomarker for episodic memory encoding and behavioral planning. Disturbed SWRs are considered a sign of neural network dysfunction that may provide insights into the structural connectivity changes associated with cognitive impairment in early-stage Alzheimer's disease (AD) and temporal lobe epilepsy (TLE). SWRs originating from hippocampus have been extensively studied during spatial navigation in rodents, and more recent studies have investigated SWRs in the hippocampal-entorhinal cortex (HPC-EC) system during a variety of other memory-guided behaviors. Understanding how SWR disruption impairs memory function, especially episodic memory, could aid in the development of more efficacious therapeutics for AD and TLE. In this review, we first provide an overview of the reciprocal association between AD and TLE, and then focus on the functions of HPC-EC system SWRs in episodic memory consolidation. It is posited that these waveforms reflect rapid network interactions among excitatory projection neurons and local interneurons and that these waves may contribute to synaptic plasticity underlying memory consolidation. Further, SWRs appear altered or ectopic in AD and TLE. These waveforms may thus provide clues to understanding disease pathogenesis and may even serve as biomarkers for early-stage disease progression and treatment response.

Neuromodulation Effect of Very Low Intensity Transcranial Ultrasound Stimulation on Multiple Nuclei in Rat Brain

Objective

Low-intensity transcranial ultrasound stimulation (TUS) is a non-invasive neuromodulation technique with high spatial resolution and feasible penetration depth. To date, the mechanisms of TUS modulated neural oscillations are not fully understood. This study designed a very low acoustic intensity (AI) TUS system that produces considerably reduced AI Ultrasound pulses (I_SPTA < 0.5 W/cm2) when compared to previous methods used to measure regional neural oscillation patterns under different TUS parameters.

Methods

We recorded the local field potential (LFP) of five brain nuclei under TUS with three groups of simulating parameters. Spectrum estimation, time-frequency analysis (TFA), and relative power analysis methods have been applied to investigate neural oscillation patterns under different stimulation parameters.

Results

Under PRF, 500 Hz and 1 kHz TUS, high-amplitude LFP activity with the auto-rhythmic pattern appeared in selected nuclei when I_SPTA exceeded 12 mW/cm². With TFA, high-frequency energy (slow gamma and high gamma) was significantly increased during the auto-rhythmic patterns. We observed an initial plateau in nuclei response when I_SPTA reached 16.4 mW/cm² for RPF 500 Hz and 20.8 mW/cm² for RPF 1 kHz. The number of responding nuclei started decreasing while I_SPTA continued increasing. Under 1.5 kHz TUS, no auto-rhythmic patterns have been observed, but slow frequency power was increased during TUS. TUS inhibited most of the frequency band and generated obvious slow waves (theta and delta band) when stimulated at RPF = 1.5 kHz, I_SPTA = 8.8 mW/cm².

Conclusion

These results demonstrate that very low intensity Transcranial Ultrasound Stimulation (VLTUS) exerts significant neuromodulator effects under specific parameters in rat models and may be a valid tool to study neuronal physiology.

Adaptive behaviour and learning in slime moulds: the role of oscillations

The slime mould Physarum polycephalum, an aneural organism, uses information from previous experiences to adjust its behaviour, but the mechanisms by which this is accomplished remain unknown. This article examines the possible role of oscillations in learning and memory in slime moulds. Slime moulds share surprising similarities with the network of synaptic connections in animal brains. First, their topology derives from a network of interconnected, vein-like tubes in which signalling molecules are transported. Second, network motility, which generates slime mould behaviour, is driven by distinct oscillations that organize into spatio-temporal wave patterns. Likewise, neural activity in the brain is organized in a variety of oscillations characterized by different frequencies. Interestingly, the oscillating networks of slime moulds are not precursors of nervous systems but, rather, an alternative architecture. Here, we argue that comparable information-processing operations can be realized on different architectures sharing similar oscillatory properties. After describing learning abilities and oscillatory activities of P. polycephalum, we explore the relation between network oscillations and learning, and evaluate the organism's global architecture with respect to information-processing potential. We hypothesize that, as in the brain, modulation of spontaneous oscillations may sustain learning in slime mould. This article is part of the theme issue 'Basal cognition: conceptual tools and the view from the single cell'.

Noisy Synaptic Conductance: Bug or a Feature?

More often than not, action potentials fail to trigger neurotransmitter release. And even when neurotransmitter is released, the resulting change in synaptic conductance is highly variable. Given the energetic cost of generating and propagating action potentials, and the importance of information transmission across synapses, this seems both wasteful and inefficient. However, synaptic noise arising from variable transmission can improve, in certain restricted conditions, information transmission. Under broader conditions, it can improve information transmission per release, a quantity that is relevant given the energetic constraints on computing in the brain. Here we discuss the role, both positive and negative, synaptic noise plays in information transmission and computation in the brain.

Near field non-invasive electrophysiology of retrotrapezoid nucleus using amperometric cation sensor

Central chemoreception is the process whereby the brainstem senses blood gas levels and adjusts homeostatic functions such as breathing and cardiovascular tone accordingly. Rodent evidence suggests that the retrotrapezoid nucleus (RTN) is a master regulator of central chemoreception, in particular, through direct sensation of acidosis induced by CO₂ levels. The oscillatory dynamics caused by pH changes as sensed by the RTN surface and its relationship to the fluctuations in cation flux is not clearly understood due to the current limitations of electrophysiology tools and this article presents our investigations to address this need. A cation selective sensor fabricated from polypyrrole doped with dodecyl benzenesulfonate (PPy (DBS)) is placed over RTN in an ex-vivo en bloc brain and changes in cation concentration in the diffusion limited region above the RTN is measured due to changes in externally imposed basal pH. The novelty of this technique lies in its feasibility to detect cation fluxes from the cells in the RTN region without having to access either sides of the cell membrane. Owing to the placement of the sensor in close proximity to the tissue, we refer to this technique as near-field electrophysiology. It is observed that lowering the pH in the physiological range (7.4-7.2) results in a significant increase in cation concentration in the vicinity of RTN with a median value of ~5 μM. The utilization of such quantifiable measurement techniques to detect sub-threshold brain activity may help provide a platform for future neural network architectures. Findings from this paper present a quantifiable, sensitive, and robust electrophysiology technique with minimal damage to the underlying tissue.

Acetylcholine Mediates Dynamic Switching Between Information Coding Schemes in Neuronal Networks

Rate coding and phase coding are the two major coding modes seen in the brain. For these two modes, network dynamics must either have a wide distribution of frequencies for rate coding, or a narrow one to achieve stability in phase dynamics for phase coding. Acetylcholine (ACh) is a potent regulator of neural excitability. Acting through the muscarinic receptor, ACh reduces the magnitude of the potassium M-current, a hyperpolarizing current that builds up as neurons fire. The M-current contributes to several excitability features of neurons, becoming a major player in facilitating the transition between Type 1 (integrator) and Type 2 (resonator) excitability. In this paper we argue that this transition enables a dynamic switch between rate coding and phase coding as levels of ACh release change. When a network is in a high ACh state variations in synaptic inputs will lead to a wider distribution of firing rates across the network and this distribution will reflect the network structure or pattern of external input to the network. When ACh is low, network frequencies become narrowly distributed and the structure of a network or pattern of external inputs will be represented through phase relationships between firing neurons. This work provides insights into how modulation of neuronal features influences network dynamics and information processing across brain states.

Hippocampal Network Oscillations Rescue Memory Consolidation Deficits Caused by Sleep Loss

Oscillations in the hippocampal network during sleep are proposed to play a role in memory storage by patterning neuronal ensemble activity. Here we show that following single-trial fear learning, sleep deprivation (which impairs memory consolidation) disrupts coherent firing rhythms in hippocampal area CA1. State-targeted optogenetic inhibition of CA1 parvalbumin-expressing (PV+) interneurons during postlearning NREM sleep, but not REM sleep or wake, disrupts contextual fear memory (CFM) consolidation in a manner similar to sleep deprivation. NREM-targeted inhibition disrupts CA1 network oscillations which predict successful memory storage. Rhythmic optogenetic activation of PV+ interneurons following learning generates CA1 oscillations with coherent principal neuron firing. This patterning of CA1 activity rescues CFM consolidation in sleep-deprived mice. Critically, behavioral and optogenetic manipulations that disrupt CFM also disrupt learning-induced stabilization of CA1 ensembles’ communication patterns in the hours following learning. Conversely, manipulations that promote CFM also promote long-term stability of CA1 communication patterns. We conclude that sleep promotes memory consolidation by generating coherent rhythms of CA1 network activity, which provide consistent communication patterns within neuronal ensembles. Most importantly, we show that this rhythmic patterning of activity is sufficient to promote long-term memory storage in the absence of sleep.

Asymmetrical voltage response in resonant neurons shaped by nonlinearities

The conventional impedance profile of a neuron can identify the presence of resonance and other properties of the neuronal response to oscillatory inputs, such as nonlinear response amplifications, but it cannot distinguish other nonlinear properties such as asymmetries in the shape of the voltage response envelope. Experimental observations have shown that the response of neurons to oscillatory inputs preferentially enhances either the upper or lower part of the voltage envelope in different frequency bands. These asymmetric voltage responses arise in a neuron model when it is submitted to high enough amplitude oscillatory currents of variable frequencies. We show how the nonlinearities associated to different ionic currents or present in the model as captured by its voltage equation lead to asymmetrical response and how high amplitude oscillatory currents emphasize this response. We propose a geometrical explanation for the phenomenon where asymmetries result not only from nonlinearities in their activation curves but also from nonlinearites captured by the nullclines in the phase-plane diagram and from the system's time-scale separation. In addition, we identify an unexpected frequency-dependent pattern which develops in the gating variables of these currents and is a product of strong nonlinearities in the system as we show by controlling such behavior by manipulating the activation curve parameters. The results reported in this paper shed light on the ionic mechanisms by which brain embedded neurons process oscillatory information.

How rhythms of the sleeping brain tune memory and synaptic plasticity

Decades of neurobehavioral research has linked sleep-associated rhythms in various brain areas to improvements in cognitive performance. However, it remains unclear what synaptic changes might underlie sleep-dependent declarative memory consolidation and procedural task improvement, and why these same changes appear not to occur across a similar interval of wake. Here we describe recent research on how one specific feature of sleep-network rhythms characteristic of rapid eye movement and non-rapid eye movement-could drive synaptic strengthening or weakening in specific brain circuits. We provide an overview of how these rhythms could affect synaptic plasticity individually and in concert. We also present an overarching hypothesis for how all network rhythms occurring across the sleeping brain could aid in encoding new information in neural circuits.

How Sleep Shapes Thalamocortical Circuit Function in the Visual System

Recent data have shown that sleep plays a beneficial role for cognitive functions such as declarative memory consolidation and perceptual learning. In this article, we review recent findings on the role of sleep in promoting adaptive visual response changes in the lateral geniculate nucleus and primary visual cortex following novel visual experiences. We discuss these findings in the context of what is currently known about how sleep affects the activity and function of thalamocortical circuits and current hypotheses regarding how sleep facilitates synaptic plasticity.

A diversity of interneurons and Hebbian plasticity facilitate rapid compressible learning in the hippocampus

The hippocampus is able to rapidly learn incoming information, even if that information is only observed once. Furthermore, this information can be replayed in a compressed format in either forward or reverse modes during sharp wave-ripples (SPW-Rs). We leveraged state-of-the-art techniques in training recurrent spiking networks to demonstrate how primarily interneuron networks can achieve the following: (1) generate internal theta sequences to bind externally elicited spikes in the presence of inhibition from the medial septum; (2) compress learned spike sequences in the form of a SPW-R when septal inhibition is removed; (3) generate and refine high-frequency assemblies during SPW-R-mediated compression; and (4) regulate the inter-SPW interval timing between SPW-Rs in ripple clusters. From the fast timescale of neurons to the slow timescale of behaviors, interneuron networks serve as the scaffolding for one-shot learning by replaying, reversing, refining, and regulating spike sequences.

Sleep Promotes, and Sleep Loss Inhibits, Selective Changes in Firing Rate, Response Properties and Functional Connectivity of Primary Visual Cortex Neurons

Recent studies suggest that sleep differentially alters the activity of cortical neurons based on firing rates during preceding wake—increasing the firing rates of sparsely firing neurons and decreasing those of faster firing neurons. Because sparsely firing cortical neurons may play a specialized role in sensory processing, sleep could facilitate sensory function via selective actions on sparsely firing neurons. To test this hypothesis, we analyzed longitudinal electrophysiological recordings of primary visual cortex (V1) neurons across a novel visual experience which induces V1 plasticity (or a control experience which does not), and a period of subsequent ad lib sleep or partial sleep deprivation. We find that across a day of ad lib sleep, spontaneous and visually-evoked firing rates are selectively augmented in sparsely firing V1 neurons. These sparsely firing neurons are more highly visually responsive, and show greater orientation selectivity than their high firing rate neighbors. They also tend to be “soloists” instead of “choristers”—showing relatively weak coupling of firing to V1 population activity. These population-specific changes in firing rate are blocked by sleep disruption either early or late in the day, and appear to be brought about by increases in neuronal firing rates across bouts of rapid eye movement (REM) sleep. Following a patterned visual experience that induces orientation-selective response potentiation (OSRP) in V1, sparsely firing and weakly population-coupled neurons show the highest level of sleep-dependent response plasticity. Across a day of ad lib sleep, population coupling strength increases selectively for sparsely firing neurons—this effect is also disrupted by sleep deprivation. Together, these data suggest that sleep may optimize sensory function by augmenting the functional connectivity and firing rate of highly responsive and stimulus-selective cortical neurons, while simultaneously reducing noise in the network by decreasing the activity of less selective, faster-firing neurons.