Prof. Qi Wang (Biomedical Engineering, Columbia): July 24th

Title: Reading and Writing the Neural Code: Initial Steps toward Engineered Sensory Percepts
 
Abstract: The transformation of sensory signals into spatiotemporal patterns of neural activity in the brain is critical in forming our perception of the external world. Physical signals, such as light, sound, and force, are transduced to neural electrical impulses, or spikes, at the periphery, and these spikes are subsequently transmitted to the brain through various stages of the sensory pathways, ultimately forming the representation of the sensory world. Deciphering the information conveyed in the spike trains is often referred to as “reading the neural code”. On the other hand, prosthetic devices designed to restore lost sensory function, such as cochlear implants, rely primarily on the principle of artificially activating neural circuits to induce a desired perception, which we might refer to as “writing the neural code”. This requires not only significant challenges in biomaterials and interfaces, but also in knowing precisely what to tell the brain to do.

My talk will focus on three topics. First, I will talk about the control of peripheral tactile sensations. Specifically, I will discuss the synthesis of virtual tactile sensations using a custom-built, high spatiotemporal resolution tactile display, a device we designed to create high fidelity, computer-controlled tactile sensations on the fingertip similar to those arising naturally. Second, I will utilize a decoding paradigm to discuss the neural representations of tactile sensations and how they are encoded and transformed across early stages of processing in the somatosensory pathway. Finally, I will discuss the design of sub-cortical microstimulation to control cortical activation, using downstream cortical measurements as a benchmark of the fidelity of the surrogate signaling. Taken together, an understanding of how to read and write the neural code is essential not only for the development of technologies for translating thoughts into actions (motor prostheses), but also for the development of technologies for creating artificial sensory percepts (sensory prostheses).

Carl Smith: July 9th

Title: Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data

Abstract: We develop new methods of Bayesian inference, largely in the context of analysis of neuroscience data. The work is broken into several parts. In the first part, we introduce a novel class of joint probability distributions in which exact inference is tractable. Previously it has been difficult to find general constructions for models in which efficient exact inference is possible, outside of certain classical cases. We identify a class of such models that are tractable owing to a certain “low-rank” structure in the potentials that couple neighboring variables. In the second part we develop methods to quantify and measure information loss in analysis of neuronal spike train data due to two types of noise, making use of the ideas developed in the first part. Information about neuronal identity or temporal resolution may be lost during spike detection and sorting, or precision of spike times may be corrupted by various effects. We quantify the information lost due to these effects for the relatively simple but sufficiently broad class of Markovian model neurons. We find that decoders that model the probability distribution of spike-neuron assignments significantly outperform decoders that use only the most likely spike assignments. We also apply the ideas of the low-rank models from the first section to defining a class of prior distributions over the space of stimuli (or other covariate) which, by conjugacy, preserve the tractability of inference. In the third part, we treat Bayesian methods for the estimation of sparse signals, with application to the locating of synapses in a dendritic tree. We develop a compartmentalized model of the dendritic tree. Building on previous work that applied and generalized ideas of least angle regression to obtain a fast Bayesian solution to the resulting estimation problem, we describe two other approaches to the same problem, one employing a horseshoe prior and the other using various spike-and-slab priors. In the last part, we revisit the low-rank models of the first section and apply them to the problem of inferring orientation selectivity maps from noisy observations of orientation preference. The relevant low-rank model exploits the self-conjugacy of the von Mises distribution on the circle. Because the orientation map model is loopy, we cannot do exact inference on the low-rank model by the forward back- ward algorithm, but block-wise Gibbs sampling by the forward backward algorithm speeds mixing. We explore another von Mises coupling potential Gibbs sampler that proves to effectively smooth noisily observed orientation maps.