Spiking Neurons

Understanding the Dynamics and Function of Networks of Spiking Neurons

Peter Stratton

Project 1: In Vivo Spike Shape Analysis – collaboration with Francois Windels.

Spikes have been recorded from single neurons in awake, behaving animals. Some of the neurons were also subjected to pharmacological manipulation to, for example, increase excitability of the neural membrane or block inhibition. This is a unique dataset that enables characterisation of spiking dynamics in awake, behaving animals rather than in anesthetised animals or in slice preparations. Analysis of this dataset has shown strong dependence of spike amplitude on spiking rate for the majority of cells (see Figure 1) as well as spike shape changes for some cells.

 

Figure 1: Spike amplitude is inversely correlated with firing rate. Top-left: Firing rate (green) and spike amplitude (red) shown over 130 s of recording. Bottom-left: Spike amplitude plotted against firing rate shows a clear inverse correlation (line of best fit – green; mean and std dev. – red). Top-right: Spike rise time (blue), fall time (green) and half width (red) in milliseconds, plotted against firing rate, show no significant change. Bottom-right: Average spike shapes plotted for low firing rates to high firing rates in five steps (blue, green, red, cyan and magenta) shows falling spike amplitude for higher firing rates.

The dataset is also unique because each recording clearly identifies a single recorded cell, allowing comparisons to be drawn between spike shapes of different neurons. The similarity of the spike shapes recorded from different neurons and the high level of noise that is typically present in neural recordings mean that spikes from different cells are likely to be misclassified (i.e. attributed to incorrect neurons) when doing normal multi-neuron recordings using single electrodes. We have calculated, for any given signal-to-noise ratio (SNR) and any given neural density, how likely misclassification is to occur. We have shown that neural recordings using single electrodes are highly likely to give erroneous results, with misclassification rates above 99% for many brain regions.

Project 2: Spike Timing Dependent Plasticity and Oscillations in Networks of Spiking Neurons

Complex activity in the brain is hypothesised to underlie its flexibility and sophisticated processing capability (i.e. higher cognitive function). This is particularly evident during periods of quiet relaxation when 10 Hz alpha waves are seen in many brain regions. This activity is associated with memory retrieval, planning, problem solving and day dreaming. Until now, it has not been possible to reproduce this sort of activity in a model of the brain; this is what we have been able to do. We are now investigating how modification of the synapses connecting the neurons (using Spike Timing Dependent Plasticity (STDP)) changes the network dynamics and how learning of spatiotemporal patterns of network activity can be achieved. Understanding how patterns can be stored in a network of spiking neurons is critical to understanding learning and memory in the brain.

Figure 2: Patterns of activity can be learned in a network of spiking neurons. The network contains 1250 neurons (Y axis) and was simulated for 30 seconds (X axis). For the first 5 seconds of simulation the network operated in a random activity regime with no external input, after which a fixed activity pattern was held for 10 seconds through input provided by external connections. When the external input was released at 15 seconds time, the observed pattern is partially reproduced in the ongoing network activity from 15 to 30 seconds.

Project 3: Calibration of Head Direction Networks on Robots

Neurons have been discovered in the brains of mammals that are active only when the animal is in a certain place in its environment, and others when the animal is facing a certain direction, effectively providing a brain-based map and compass. Understanding how such specific functions arise and are controlled in the brain can assist in revealing how the brain functions in general and how these functions can go awry in cases of brain injury and disease. We have modelled the brain network that contains the neurons that represent head direction in the mammalian brain (see Figure 3), and have shown how this network can be calibrated on a mobile robot, through feedback from the world, when the robot follows specific movements that many infant mammals perform. This work advances our understanding of the neural systems involved in motion tracking and the representation of space, links these systems to specific developmental behaviours and motor deficits, and further demonstrates how biological processes can afford practical solutions to engineering problems.

 

 

Figure 3: Head direction network. Head direction (HD) cells excite their close neighbours strongly and more distant neighbours less strongly, and this self-excitation creates the HD activity. These excitatory connections are calibrated to allow the HD system to accurately represent head direction as the animal or robot moves. The HD cells are also inhibited by the asymmetric Angular Head Velocity (AHV); the overall efficacy of this inhibition is also calibrated. DTN: Dorsal tegmental nucleus, LMN: Lateral mammillary nucleus.

Ongoing Collaborative Research

Collaborated with Michael Milford and Gordon Wyeth: Calibrating spiking head direction networks on robots with long-term deployments, for example in factory and warehouse delivery tasks, where on-going calibration is required due to mechanical wear and damage accrued over long timeframes.

Collaborated with Francois Windels and Allen Cheung: Continued analysis of the unique neural recording datasets from awake, behaving animals.

Collaborated with David Ball and Chris Nolan: Controlling Braitenberg vehicles with spiking neural networks – how do the temporal dynamics of spiking networks assist in the temporal organisation of embodied behaviour?