Heterogeneity in E/I neural network allows entrainment to a wide frequency range

Oscillations and rhythms are measured in the brain through large-scale measures like EEG (electroencephalogram) and LFP (Local Field Potential). Particularly, cortical gamma rhythms (30-90 Hz) found in different brain regions are correlated with different cognitive states. Despite vast differences in the range frequencies in gamma rhythms, the regions communicate to complete high-level tasks. One way in which this takes place is entrainment, where the postsynaptic group phase-lock to the rhythmic input from the presynaptic group (constant phase-shift). Mathematical models of the neurons and the neural networks are proposed to uncover the mechanisms behind experimentally observed phenomena. Most works have used homogeneous models of spiking networks. These simplified models provide a valuable understanding of neural dynamics. However, neural heterogeneity (variation in the neural or network parameters) has been experimentally observed and shown to have a non-trivial effect on many neural processes. Few studies have dealt with the role of different types of neural heterogeneity in the entrainment of a large network, and how it affects the frequency range the neural network entrains to. In this project, we aimed to show how different types of network heterogeneity affect the ability of the networks to entrain to gamma frequencies. We used the Pyramidal-Interneuronal Network Gamma (PING) model, a model consisting of excitatory pyramidal cells (E-cells) and inhibitory interneurons (I-cells) that are synaptically connected and generate gamma oscillations. We show that heterogeneity in the synaptic conductance from excitatory neurons to inhibitory neurons greatly increases the frequency range over which the network can entrain. The mechanism that allows this to happen requires the heterogeneity to 1. Create an I-cell excitability gradient; 2. Introduce input synchrony difference among the I-cells. The entrained I-cell subsets formed under these two conditions are necessary for well-enhanced entrainment as they support the entrainment of the whole network through feedback inhibition. This improvement is shown to be robust in large parameter space

Encoding of Temporal Sound Features in the Rodent Superior Paraolivary Nucleus

The superior paraolivary nucleus (SPON) is a prominent cell group in the mammalian brainstem. SPON neurons are part of a monaural circuit that encodes temporal sound features in the ascending auditory pathway. Such attributes of acoustic signals are critical for speech perception in humans and likely equally as important in animal communication. While basic properties of SPON neurons have been characterized in some detail, a comprehensive examination of mechanisms that underlie their ability to precisely represent temporal information is lacking. Furthermore, little is known of how the SPON impacts its primary target, the inferior colliculus. Combinations of electrophysiological, pharmacological and histological techniques were used to investigate SPON neuronal responses to stimuli whose temporal parameters were systematically varied. In addition, properties of neurons in the inferior colliculus were examined before and after reversible inactivation of the SPON in order to explore its functional role in hearing. An after-hyperpolarization rebound mechanism was shown to generate the hallmark offset response of SPON neurons in vitro. Single-cell labeling techniques provided a detailed morphological description of cell bodies and dendrites and revealed a homogeneous population of neurons. Moreover, subthreshold ionic currents and synaptic neurotransmitter receptor systems were shown to mediate the precision of responses to temporal features of sound in vivo. It was also demonstrated that input from the SPON shapes response properties of inferior colliculus neurons to both periodic and singular temporal stimulus features. Taken together, these results suggest the SPON likely has a substantial role in temporal processing that has not been taken into account in the current understanding of the central auditory system. Demonstrating a functional role for the SPON in hearing will expand our knowledge of neuronal circuits responsible for representing biologically important sounds in both normal hearing and hearing impaired states

How does the brain extract acoustic patterns? A behavioural and neural study

In complex auditory scenes the brain exploits statistical regularities to group sound elements into streams. Previous studies using tones that transition from being randomly drawn to regularly repeating, have highlighted a network of brain regions involved during this process of regularity detection, including auditory cortex (AC) and hippocampus (HPC; Barascud et al., 2016). In this thesis, I seek to understand how the neurons within AC and HPC detect and maintain a representation of deterministic acoustic regularity. I trained ferrets (n = 6) on a GO/NO-GO task to detect the transition from a random sequence of tones to a repeating pattern of tones, with increasing pattern lengths (3, 5 and 7). All animals performed significantly above chance, with longer reaction times and declining performance as the pattern length increased. During performance of the behavioural task, or passive listening, I recorded from primary and secondary fields of AC with multi-electrode arrays (behaving: n = 3), or AC and HPC using Neuropixels probes (behaving: n = 1; passive: n = 1). In the local field potential, I identified no differences in the evoked response between presentations of random or regular sequences. Instead, I observed significant increases in oscillatory power at the rate of the repeating pattern, and decreases at the tone presentation rate, during regularity. Neurons in AC, across the population, showed higher firing with more repetitions of the pattern and for shorter pattern lengths. Single-units within AC showed higher precision in their firing when responding to their best frequency during regularity. Neurons in AC and HPC both entrained to the pattern rate during presentation of the regular sequence when compared to the random sequence. Lastly, development of an optogenetic approach to inactivate AC in the ferret paves the way for future work to probe the causal involvement of these brain regions

Stochastic resonance and finite resolution in a network of leaky integrate-and-fire neurons.

This thesis is a study of stochastic resonance (SR) in a discrete implementation of a leaky integrate-and-fire (LIF) neuron network. The aim was to determine if SR can be realised in limited precision discrete systems implemented on digital hardware. How neuronal modelling connects with SR is discussed. Analysis techniques for noisy spike trains are described, ranging from rate coding, statistical measures, and signal processing measures like power spectrum and signal-to-noise ratio (SNR). The main problem in computing spike train power spectra is how to get equi-spaced sample amplitudes given the short duration of spikes relative to their frequency. Three different methods of computing the SNR of a spike train given its power spectrum are described. The main problem is how to separate the power at the frequencies of interest from the noise power as the spike train encodes both noise and the signal of interest. Two models of the LIF neuron were developed, one continuous and one discrete, and the results compared. The discrete model allowed variation of the precision of the simulation values allowing investigation of the effect of precision limitation on SR. The main difference between the two models lies in the evolution of the membrane potential. When both models are allowed to decay from a high start value in the absence of input, the discrete model does not completely discharge while the continuous model discharges to almost zero. The results of simulating the discrete model on an FPGA and the continuous model on a PC showed that SR can be realised in discrete low resolution digital systems. SR was found to be sensitive to the precision of the values in the simulations. For a single neuron, we find that SR increases between 10 bits and 12 bits resolution after which it saturates. For a feed-forward network with multiple input neurons and one output neuron, SR is stronger with more than 6 input neurons and it saturates at a higher resolution. We conclude that stochastic resonance can manifest in discrete systems though to a lesser extent compared to continuous systems

Dynamic synchronization of sympathetic oscillators

Synchronous activity of single postganglionic sympathetic neurones (PGNs) underlies rhythmical or semi-rhythmical burst discharges recorded from peripheral sympathetic nerves. It is still controversial whether this rhythmicity is generated by an autonomous sympathetic oscillator. Previous studies have demonstrated that activity of single PGNs innervating the caudal ventral artery (CVA) of the rat's tail has a dominant rhythm (T-rhythm). The frequency of T- rhythm is different from the cardiac frequency and can be different from those of ventilatory and respiratory rhythms, suggesting that T-rhythm is generated by an oscillator independent of periodic drives originating from the arterial baroreceptors, the ventilation afferents and the respiratory network. Using the rat's tail circulation as a model, the purpose of the present study is: 1) to determine whether activity from different single PGNs is generated by multiple oscillators. 2) to establish whether synchronization of single PGNs is an obligatory feature and if not, how it is regulated. 3) to determine whether periodically driven single PGN oscillators exhibit dynamics as predicted by the theory of nonlinear coupled oscillators. 4) to explain the discharge behaviour of whole nerve activity based on the findings at single PGN level. The experiments were conducted in anaesthetized Sprague-Dawley rats. Population PGN activity was recorded from the ventral collector nerve (VCN) of the tail. Single PGN activity was recorded focally from the surface of the CVA. The interaction between two single PGNs was studied by recording two units simultaneously. The discharge behaviours of PGNs in response to a periodic input were studied using the central respiratory drive (CRD) and lung-inflation cycle (LlC)-related activity as the driving forces. The findings from the present study suggest that: 1) Activity of CVA PGNs is generated by multiple oscillators independent of CRD, LIC-related activity and cardiac activity. 2) The multiple PGN oscillators are capable of dynamic synchronization. 3) When subjected to frequency changes of LICs, single PGNs exhibit dynamics, such as 1:1 entrainment, relative coordination, high order rational frequency-lock, asynchrony, characterising nonlinear coupled oscillators. 4) Population PGN activity should be considered as output activity from a pool of dynamically interactive multiple oscillators rather than that from a single oscillator

Oscillatory architecture of memory circuits

The coordinated activity between remote brain regions underlies cognition and memory function. Although neuronal oscillations have been proposed as a mechanistic substrate for the coordination of information transfer and memory consolidation during sleep, little is known about the mechanisms that support the widespread synchronization of brain regions and the relationship of neuronal dynamics with other bodily rhythms, such as breathing. During exploratory behavior, the hippocampus and the prefrontal cortex are organized by theta oscillations, known to support memory encoding and retrieval, while during sleep the same structures are dominated by slow oscillations that are believed to underlie the consolidation of recent experiences. The expression of conditioned fear and extinction memories relies on the coordinated activity between the mPFC and the basolateral amygdala (BLA), a neuronal structure encoding associative fear memories. However, to date, the mechanisms allowing this long-range network synchronization of neuronal activity between the mPFC and BLA during fear behavior remain virtually unknown. Using a combination of extracellular recordings and open- and closed-loop optogenetic manipulations, we investigated the oscillatory and coding mechanisms mediating the organization and coupling of the limbic circuit in the awake and asleep brain, as well as during memory encoding and retrieval. We found that freezing, a behavioral expression of fear, is tightly associated with an internally generated brain state that manifests in sustained 4Hz oscillatory dynamics in prefrontal-amygdala circuits. 4Hz oscillations accurately predict the onset and termination of the freezing state. These oscillations synchronize prefrontal-amygdala circuits and entrain neuronal activity to dynamically regulate the development of neuronal ensembles. This enables the precise timing of information transfer between the two structures and the expression of fear responses. Optogenetic induction of prefrontal 4Hz oscillations promotes freezing behavior and the formation of long-lasting fear memory, while closed-loop phase specific manipulations bidirectionally modulate fear expression. Our results unravel a physiological signature of fear memory and identify a novel internally generated brain state, characterized by 4Hz oscillations. This oscillation enables the temporal coordination and information transfer in the prefrontal-amygdala circuit via a phase-specific coding mechanism, facilitating the encoding and expression of fear memory. In the search for the origin of this oscillation, we focused our attention on breathing, the most fundamental and ubiquitous rhythmic activity in life. Using large-scale extracellular recordings from a number of structures, including the medial prefrontal cortex, hippocampus, thalamus, amygdala and nucleus accumbens in mice we identified and characterized the entrainment by breathing of a host of network dynamics across the limbic circuit. We established that fear-related 4Hz oscillations are a state-specific manifestation of this cortical entrainment by the respiratory rhythm. We characterized the translaminar and transregional profile of this entrainment and demonstrated a causal role of breathing in synchronizing neuronal activity and network dynamics between these structures in a variety of behavioral scenarios in the awake and sleep state. We further revealed a dual mechanism of respiratory entrainment, in the form of an intracerebral corollary discharge that acts jointly with an olfactory reafference to coordinate limbic network dynamics, such as hippocampal ripples and cortical UP and DOWN states, involved in memory consolidation. Respiration provides a perennial stream of rhythmic input to the brain. In addition to its role as the condicio sine qua non for life, here we provide evidence that breathing rhythm acts as a global pacemaker for the brain, providing a reference signal that enables the integration of exteroceptive and interoceptive inputs with the internally generated dynamics of the hippocampus and the neocortex. Our results highlight breathing, a perennial rhythmic input to the brain, as an oscillatory scaffold for the functional coordination of the limbic circuit, enabling the segregation and integration of information flow across neuronal networks

Translational pipelines for closed-loop neuromodulation

Closed-loop neuromodulation systems have shown significant potential for addressing unmet needs in the treatment of disorders of the central nervous system, yet progress towards clinical adoption has been slow. Advanced technological developments often stall in the preclinical stage by failing to account for the constraints of implantable medical devices, and due to the lack of research platforms with a translational focus. This thesis presents the development of three clinically relevant research systems focusing on refinements of deep brain stimulation therapies. First, we introduce a system for synchronising implanted and external stimulation devices, allowing for research into multi-site stimulation paradigms, cross-region neural plasticity, and questions of phase coupling. The proposed design aims to sidestep the limited communication capabilities of existing commercial implant systems in providing a stimulation state readout without reliance on telemetry, creating a cross-platform research tool. Next, we present work on the Picostim-DyNeuMo adaptive neuromodulation platform, focusing on expanding device capabilities from activity and circadian adaptation to bioelectric marker--based responsive stimulation. Here, we introduce a computationally optimised implementation of a popular band power--estimation algorithm suitable for deployment in the DyNeuMo system. The new algorithmic capability was externally validated to establish neural state classification performance in two widely-researched use cases: Parkinsonian beta bursts and seizures. For in vivo validation, a pilot experiment is presented demonstrating responsive neurostimulation to cortical alpha-band activity in a non-human primate model for the modulation of attention state. Finally, we turn our focus to the validation of a recently developed method to provide computationally efficient real-time phase estimation. Following theoretical analysis, the method is integrated into the commonly used Intan electrophysiological recording platform, creating a novel closed-loop optogenetics research platform. The performance of the research system is characterised through a pilot experiment, targeting the modulation of cortical theta-band activity in a transgenic mouse model

Encoding of Coordinating Information in a Network of Coupled Oscillators

Animal locomotion is driven by cyclic movements of the body or body appendages. These movements are under the control of neural networks that are driven by central pattern generators (CPG). In order to produce meaningful behavior, CPGs need to be coordinated. The crayfish swimmeret system is a model to investigate the coordination of distributed CPGs. Swimmerets are four pairs of limbs on the animal’s abdomen, which move in cycles of alternating power-strokes and return-strokes. The swimmeret pairs are coordinated in a metachronal wave from posterior to anterior with a phase lag of approximately 25% between segments. Each swimmeret is controlled by its own neural microcircuit, located in the body segment’s hemiganglion. Three neurons per hemiganglion are necessary and sufficient for the 25% phase lag. ASCE DSC encode information about their home ganglion’s activity state and send it to their anterior or posterior target ganglia, respectively. ComInt 1, which is electrically coupled to the CPG, receives the coordinating information. The isolated abdominal ganglia chain reliably produces fictive swimming. Motor burst strength is encoded by the number of spikes per ASCE and DSC burst. If motor burst strength varies spontaneously, the coordinating neurons track these changes linearly. The neurons are hypothesized to adapt their spiking range to the occurring motor burst strengths. One aim of this study was to investigate the putative adaptive encoding of the coordinating neurons in electrophysiological experiments. This revealed that the system’s excitation level influenced both the whole system and the individual coordinating neurons. These mechanisms allowed the coordinating neurons to adapt to the range of burst strengths at any given excitation level by encoding relative burst strengths. The second aim was to identify the transmitters of the coordinating neurons at the synapse to ComInt 1. Immunohistochemical experiments demonstrated that coordinating neurons were not co-localized with serotonin-immunoreactive positive neurons. MALDI-TOF mass spectrometry suggested acetylcholine as presumable transmitter