Peter Stratton

Imagined gait modulates neuronal network dynamics in the human pedunculopontine nucleus

The pedunculopontine nucleus (PPN) is a part of the mesencephalic locomotor region and is thought to be important for the initiation and maintenance of gait. Lesions of the PPN induce gait deficits, and the PPN has therefore emerged as a target for deep brain stimulation for the control of gait and postural disability. However, the role of the PPN in gait control is not understood. Using extracellular single-unit recordings in awake patients, we found that neurons in the PPN discharged as synchronous functional networks whose activity was phase locked to alpha oscillations. Neurons in the PPN responded to limb movement and imagined gait by dynamically changing network activity and decreasing alpha phase locking. Our results indicate that different synchronous networks are activated during initial motor planning and actual motion, and suggest that changes in gait initiation in Parkinsons disease may result from disrupted network activity in the PPN.

Action Potential Waveform Variability Limits Multi-Unit Separation in Freely Behaving Rats

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or mulitwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥4) and low neuronal density (≈20,000/ mm3). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution.

Self-sustained non-periodic activity in networks of spiking neurons: The contribution of local and long-range connections and dynamic synapses

Cortical dynamics show self-sustained activity which is complex and non-periodic. Assemblies of neurons show transient coupling exhibiting both integration and segregation without entering a seizure state. Models to date have demonstrated these properties but have required external input to maintain activity. Here we propose a spiking network model that incorporates a novel combination of both local and long-range connectivity and dynamic synapses (which we call the LLDS network) and we present explorations of the networks micro and macro behaviour. At the micro level, the LLDS network exhibits self-sustained activity which is complex and non-periodic and shows transient coupling between assemblies in different network regions. At the macro level, the power spectrum of the derived EEG, calculated from the summed membrane potentials, shows a power-law-like distribution similar to that recorded from human EEG. We systematically explored parameter combinations to map the variety of behavioural regimes and found that network connectivity and synaptic mechanisms significantly impact the dynamics. The complex sustained behaviour occupies a transition region in parameter space between two types of non-complex activity state, a synchronised high firing rate regime, resembling seizure, for low connectivity, and repetitive activation of a single network assembly for high connectivity. Networks without synaptic dynamics show only transient complex behaviour. We conclude that local and long-range connectivity and short-term synaptic dynamics are together sufficient to support complex persistent activity. The ability to craft such persistent dynamics in a spiking network model creates new opportunities to study neural processing, learning, injury and disease in nervous systems.

Auditory Tones and Foot-Shock Recapitulate Spontaneous Sub-Threshold Activity in Basolateral Amygdala Principal Neurons and Interneurons

In quiescent states such as anesthesia and slow wave sleep, cortical networks show slow rhythmic synchronized activity. In sensory cortices this rhythmic activity shows a stereotypical pattern that is recapitulated by stimulation of the appropriate sensory modality. The amygdala receives sensory input from a variety of sources, and in anesthetized animals, neurons in the basolateral amygdala (BLA) show slow rhythmic synchronized activity. Extracellular field potential recordings show that these oscillations are synchronized with sensory cortex and the thalamus, with both the thalamus and cortex leading the BLA. Using whole-cell recording in vivo we show that the membrane potential of principal neurons spontaneously oscillates between up- and down-states. Footshock and auditory stimulation delivered during down-states evokes an up-state that fully recapitulates those occurring spontaneously. These results suggest that neurons in the BLA receive convergent input from networks of cortical neurons with slow oscillatory activity and that somatosensory and auditory stimulation can trigger activity in these same networks.

Using strategic movement to calibrate a neural compass: a spiking network for tracking head direction in rats and robots.

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that ‘grounding’ of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex.

Calibration of the head direction network: a role for symmetric angular head velocity cells.

Continuous attractor networks require calibration. Computational models of the head direction (HD) system of the rat usually assume that the connections that maintain HD neuron activity are pre-wired and static. Ongoing activity in these models relies on precise continuous attractor dynamics. It is currently unknown how such connections could be so precisely wired, and how accurate calibration is maintained in the face of ongoing noise and perturbation. Our adaptive attractor model of the HD system that uses symmetric angular head velocity (AHV) cells as a training signal shows that the HD system can learn to support stable firing patterns from poorly-performing, unstable starting conditions. The proposed calibration mechanism suggests a requirement for symmetric AHV cells, the existence of which has previously been unexplained, and predicts that symmetric and asymmetric AHV cells should be distinctly different (in morphology, synaptic targets and/or methods of action on postsynaptic HD cells) due to their distinctly different functions.

Rodent Scope: A User-Configurable Digital Wireless Telemetry System for Freely Behaving Animals

This paper describes the design and implementation of a wireless neural telemetry system that enables new experimental paradigms, such as neural recordings during rodent navigation in large outdoor environments. RoSco, short for Rodent Scope, is a small lightweight user-configurable module suitable for digital wireless recording from freely behaving small animals. Due to the digital transmission technology, RoSco has advantages over most other wireless modules of noise immunity and online user-configurable settings. RoSco digitally transmits entire neural waveforms for 14 of 16 channels at 20 kHz with 8-bit encoding which are streamed to the PC as standard USB audio packets. Up to 31 RoSco wireless modules can coexist in the same environment on non-overlapping independent channels. The design has spatial diversity reception via two antennas, which makes wireless communication resilient to fading and obstacles. In comparison with most existing wireless systems, this system has online user-selectable independent gain control of each channel in 8 factors from 500 to 32,000 times, two selectable ground references from a subset of channels, selectable channel grounding to disable noisy electrodes, and selectable bandwidth suitable for action potentials (300 Hz–3 kHz) and low frequency field potentials (4 Hz–3 kHz). Indoor and outdoor recordings taken from freely behaving rodents are shown to be comparable to a commercial wired system in sorting for neural populations. The module has low input referred noise, battery life of 1.5 hours and transmission losses of 0.1% up to a range of 10 m.

Global segregation of cortical activity and metastable dynamics

Cortical activity exhibits persistent metastable dynamics. Assemblies of neurons transiently couple (integrate) and decouple (segregate) at multiple spatiotemporal scales; both integration and segregation are required to support metastability. Integration of distant brain regions can be achieved through long range excitatory projections, but the mechanism supporting long range segregation is not clear. We argue that the thalamocortical matrix connections, which project diffusely from the thalamus to the cortex and have long been thought to support cortical gain control, play an equally-important role in cortical segregation. We present a computational model of the diffuse thalamocortical loop, called the competitive cross-coupling (CXC) spiking network. Simulations of the model show how different levels of tonic input from the brainstem to the thalamus could control dynamical complexity in the cortex, directing transitions between sleep, wakefulness and high attention or vigilance. The model also explains how mutually-exclusive activity could arise across large portions of the cortex, such as between the default-mode and task-positive networks. It is robust to noise but does not require noise to autonomously generate metastability. We conclude that the long range segregation observed in brain activity and required for global metastable dynamics could be provided by the thalamocortical matrix, and is strongly modulated by brainstem input to the thalamus.

Calcium signalling in medial intercalated cell dendrites and spines

Dendritic and spine calcium imaging in combination with electrophysiology in acute slices revealed that in medial intercalated cells of the amygdala: Action potentials back‐propagate into the dendritic tree, but due to the presence of voltage‐dependent potassium channels, probably Kv4.2 channels, attenuate over distance. A mixed population of AMPA receptors with rectifying and linear I–V relations are present at individual spines of a single neuron. Decay kinetics and pharmacology suggest tri‐heteromeric NMDA receptors at basolateral–intercalated cell synapses. NMDA receptors are the main contributors to spine calcium entry in response to synaptic stimulation. Calcium signals in response to low‐ and high‐frequency stimulation, and in combination with spontaneous action potentials are locally restricted to the vicinity of active spines. Together, these data show that calcium signalling in these GABAergic neurons is tightly controlled and acts as a local signal.

Unlocking neural complexity with a robotic key

Complex brains evolved in order to comprehend and interact with complex environments in the real world. Despite significant progress in our understanding of perceptual representations in the brain, our understanding of how the brain carries out higher level processing remains largely superficial. This disconnect is understandable, since the direct mapping of sensory inputs to perceptual states is readily observed, while mappings between (unknown) stages of processing and intermediate neural states is not. We argue that testing theories of higher level neural processing on robots in the real world offers a clear path forward, since (1) the complexity of the neural robotic controllers can be staged as necessary, avoiding the almost intractable complexity apparent in even the simplest current living nervous systems; (2) robotic controller states are fully observable, avoiding the enormous technical challenge of recording from complete intact brains; and (3) unlike computational modelling, the real world can stand for itself when using robots, avoiding the computational intractability of simulating the world at an arbitrary level of detail. We suggest that embracing the complex and often unpredictable closed‐loop interactions between robotic neuro‐controllers and the physical world will bring about deeper understanding of the role of complex brain function in the high‐level processing of information and the control of behaviour.