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Chris Eliasmith

Publications and source records attributed to Chris Eliasmith.

6 recordsLinked to original sources

Neural populations can induce reliable postsynaptic currents without observable spike rate changes or precise spike timing.

Fine temporal patterns of firing in much of the brain are highly irregular. In some circuits, the precise pattern of irregularity contains information beyond that contained in mean firing rates. However, the capacity of neural circuits to use this additional information for computational purposes is not well understood. Here we employ computational methods to show that an ensemble of neurons firing at a constant mean rate can induce arbitrarily chosen temporal current patterns in postsynaptic cells. If the presynaptic neurons fire with nearly uniform interspike intervals, then current patterns are sensitive to variations in spike timing. But irregular, Poisson-like firing can drive current patterns robustly, even if spike timing varies by tens of milliseconds from trial to trial. Notably, irregular firing patterns can drive useful patterns of current even if they are so variable that several hundred repeated experimental trials would be needed to distinguish them from random firing. Together, these results describe an unrestrictive set of conditions in which postsynaptic cells might exploit virtually any information contained in spike timing. We speculate as to how this capability may underlie an extension of population coding to the temporal domain.

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Higher-dimensional neurons explain the tuning and dynamics of working memory cells.

Measurements of neural activity in working memory during a somatosensory discrimination task show that the content of working memory is not only stimulus dependent but also strongly time varying. We present a biologically plausible neural model that reproduces the wide variety of characteristic responses observed in those experiments. Central to our model is a heterogeneous ensemble of two-dimensional neurons that are hypothesized to simultaneously encode two distinct stimuli dimensions. We demonstrate that the spiking activity of each neuron in the population can be understood as the result of a two-dimensional state space trajectory projected onto the tuning curve of the neuron. The wide variety of observed responses is thus a natural consequence of a population of neurons with a diverse set of preferred stimulus vectors and response functions in this two-dimensional space. In addition, we propose a taxonomy of network topologies that will generate the two-dimensional trajectory necessary to exploit this population. We conclude by proposing some experimental indicators to help distinguish among these possibilities.

Action Potentials↗

Integrating behavioral and neural data in a model of zebrafish network interaction.

The spinal neural networks of larval zebrafish (Danio rerio) generate a variety of movements such as escape, struggling, and swimming. Various mechanisms at the neural and network levels have been proposed to account for switches between these behaviors. However, there are currently no detailed demonstrations of such mechanisms. This makes determining which mechanisms are plausible extremely difficult. In this paper, we propose a detailed biologically plausible model of the interactions between the swimming and escape networks in the larval zebrafish, while taking into account anatomical and physiological evidence. We show that the results of our neural model generate the expected behavior when used to control a hydrodynamic model of carangiform locomotion. As a result, the model presented here is a clear demonstration of a plausible mechanism by which these distinct behaviors can be controlled. Interestingly, the networks are anatomically overlapping, despite clear differences in behavioral function and physiology.

Animals↗

A unified approach to building and controlling spiking attractor networks.

Extending work in Eliasmith and Anderson (2003), we employ a general framework to construct biologically plausible simulations of the three classes of attractor networks relevant for biological systems: static (point, line, ring, and plane) attractors, cyclic attractors, and chaotic attractors. We discuss these attractors in the context of the neural systems that they have been posited to help explain: eye control, working memory, and head direction; locomotion (specifically swimming); and olfaction, respectively. We then demonstrate how to introduce control into these models. The addition of control shows how attractor networks can be used as subsystems in larger neural systems, demonstrates how a much larger class of networks can be related to attractor networks, and makes it clear how attractor networks can be exploited for various information processing tasks in neurobiological systems.

Action Potentials↗

A general framework for neurobiological modeling: an application to the vestibular system.

The otolith organs in the vestibular system are excellent detectors of linear accelerations. However, any measurement of linear acceleration is ambiguous between a tilt in a gravitational field and an inertial acceleration. Angelaki et al. have put forward a general hypothesis about how inertial accelerations can be computed based on vestibular signals (J. Neurosci. 19 (1999) 316). We have constructed a realistic, detailed model of the relevant systems to test this hypothesis. The model produces useful predictions about what kinds of neurons should be found in the vestibular nucleus if such a computation is actually performed in the vestibular system. The model is constructed using general principles of neurobiological simulation (J. Neurophys. 84 (2000) 2113).

Acceleration↗

A controlled attractor network model of path integration in the rat.

Cells in several areas of the hippocampal formation show place specific firing patterns, and are thought to form a distributed representation of an animal's current location in an environment. Experimental results suggest that this representation is continually updated even in complete darkness, indicating the presence of a path integration mechanism in the rat. Adopting the Neural Engineering Framework (NEF) presented by Eliasmith and Anderson (2003) we derive a novel attractor network model of path integration, using heterogeneous spiking neurons. The network we derive incorporates representation and updating of position into a single layer of neurons, eliminating the need for a large external control population, and without making use of multiplicative synapses. An efficient and biologically plausible control mechanism results directly from applying the principles of the NEF. We simulate the network for a variety of inputs, analyze its performance, and give three testable predictions of our model.

Action Potentials↗