A spiking neural model of decision making and the speed-accuracy trade-off.

The speed-accuracy trade-off (SAT) is the tendency for fast decisions to come at the expense of accurate performance. Evidence accumulation models such as the drift diffusion model can reproduce a variety of behavioral data related to the SAT, and their parameters have been linked to neural activities in the brain. However, our understanding of how biological neural networks realize the associated cognitive operations remains incomplete, limiting our ability to unify neurological and computational accounts of the SAT.

A scalable spiking amygdala model that explains fear conditioning, extinction, renewal and generalization.

The amygdala (AMY) is widely implicated in fear learning and fear behaviour, but it remains unclear how the many biological components present within AMY interact to achieve these abilities. Building on previous work, we hypothesize that individual AMY nuclei represent different quantities and that fear conditioning arises from error-driven learning on the synapses between AMY nuclei. We present a computational model of AMY that (a) recreates the divisions and connections between AMY nuclei and their constituent pyramidal and inhibitory neurons; (b) accommodates scalable high-dimensional representations of external stimuli; (c) learns to associate complex stimuli with the presence (or absence) of an aversive stimulus; (d) preserves feature information when mapping inputs to salience estimates, such that these estimates generalize to similar stimuli; and (e) induces a diverse profile of neural responses within each nucleus.

Constructing functional models from biophysically-detailed neurons.

Improving biological plausibility and functional capacity are two important goals for brain models that connect low-level neural details to high-level behavioral phenomena. We develop a method called "oracle-supervised Neural Engineering Framework" (osNEF) to train biologically-detailed spiking neural networks that realize a variety of cognitively-relevant dynamical systems. Specifically, we train networks to perform computations that are commonly found in cognitive systems (communication, multiplication, harmonic oscillation, and gated working memory) using four distinct neuron models (leaky-integrate-and-fire neurons, Izhikevich neurons, 4-dimensional nonlinear point neurons, and 4-compartment, 6-ion-channel layer-V pyramidal cell reconstructions) connected with various synaptic models (current-based synapses, conductance-based synapses, and voltage-gated synapses).

Chaos and dynamical complexity in the quantum to classical transition.

We study the largest Lyapunov exponents λ and dynamical complexity for an open quantum driven double-well oscillator, mapping its dependence on coupling to the environment Γ as well as effective Planck's constant β. We show that in general λ increases with effective Hilbert space size (as β decreases, or the system becomes larger and closer to the classical limit). However, if the classical limit is regular, there is always a quantum system with λ greater than the classical λ, with several examples where the quantum system is chaotic even though the classical system is regular.

The Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory.

We use a spiking neural network model of working memory (WM) capable of performing the spatial delayed response task (DRT) to investigate two drugs that affect WM: guanfacine (GFC) and phenylephrine (PHE). In this model, the loss of information over time results from changes in the spiking neural activity through recurrent connections. We reproduce the standard forgetting curve and then show that this curve changes in the presence of GFC and PHE, whose application is simulated by manipulating functional, neural, and biophysical properties of the model.