computational models

Model-based recommendations for optimal surgical placement of epiretinal implants

We systematically explored the space of possible implant configurations to make recommendations for optimal intraocular positioning of Argus II.

What would the world look like with a bionic eye?

Clinical studies have demonstrated that the vision provided by current SR devices differs substantially from normal sight.

Data-driven models in human neuroscience and neuroengineering

In this review, we provide an accessible primer to modern modeling approaches and highlight recent data-driven discoveries in the domains of neuroimaging, single-neuron and neuronal population responses, and device neuroengineering.

Neural correlates of sparse coding and dimensionality reduction

Brains face the fundamental challenge of extracting relevant information from high-dimensional external stimuli in order to form the neural basis that can guide an organism's behavior and its interaction with the world. One potential approach to addressing this challenge is to reduce the number of variables required to represent a particular input space (i.e., dimensionality reduction). We review compelling evidence that a range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC)—a form of efficient population coding due to dimensionality reduction and sparsity constraints.

A model of ganglion axon pathways accounts for percepts elicited by retinal implants

We show that the perceptual experience of retinal implant users can be accurately predicted using a computational model that simulates each individual patient’s retinal ganglion axon pathways.

Biophysical model of axonal stimulation in epiretinal visual prostheses

To investigate the effect of axonal stimulation on the retinal response, we developed a computational model of a small population of morphologically and biophysically detailed retinal ganglion cells, and simulated their response to epiretinal electrical stimulation. We found that activation thresholds of ganglion cell somas and axons varied systematically with both stimulus pulse duration and electrode-retina distance. These findings have important implications for the improvement of stimulus encoding methods for epiretinal prostheses.

CARLsim 4: An open source library for large scale, biologically detailed spiking neural network simulation using heterogeneous clusters

We have developed CARLsim 4, a user-friendly SNN library written in C++ that can simulate large biologically detailed neural networks. Improving on the efficiency and scalability of earlier releases, the present release allows for the simulation using multiple GPUs and multiple CPU cores concurrently in a heterogeneous computing cluster. Benchmarking results demonstrate simulation of 8.6 million neurons and 0.48 billion synapses using 4 GPUs and up to 60x speedup for multi-GPU implementations over a single-threaded CPU implementation, making CARLsim 4 well-suited for large-scale SNN models in the presence of real-time constraints.

pulse2percept: A Python-based simulation framework for bionic vision

*pulse2percept* is an open-source Python simulation framework used to predict the perceptual experience of retinal prosthesis patients across a wide range of implant configurations.

3D visual response properties of MSTd emerge from an efficient, sparse population code

Using a dimensionality reduction technique known as non-negative matrix factorization, we found that a variety of medial superior temporal (MSTd) neural response properties could be derived from MT-like input features. The responses that emerge from this technique, such as 3D translation and rotation selectivity, spiral tuning, and heading selectivity, can account for a number of empirical results. These findings (1) provide a further step toward a scientific understanding of the often nonintuitive response properties of MSTd neurons; (2) suggest that response properties, such as complex motion tuning and heading selectivity, might simply be a byproduct of MSTd neurons performing dimensionality reduction on their inputs; and (3) imply that motion perception in the cortex is consistent with ideas from the efficient-coding and free-energy principles.

A GPU-accelerated cortical neural network model for visually guided robot navigation

We present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the position of objects based on motion discontinuities, and combines these signals with the representation of a goal location to produce motor commands that successfully steer the robot around obstacles toward the goal. This study demonstrates how neural signals in a model of cortical area MT might provide sufficient motion information to steer a physical robot on human-like paths around obstacles in a real-world environment.