A wearable mobility device for the blind using retina-inspired dynamic vision sensors.

Proposed is a prototype of a wearable mobility device which aims to assist the blind with navigation and object avoidance via auditory-vision-substitution. The described system uses two dynamic vision sensors and event-based information processing techniques to extract depth information. The 3D visual input is then processed using three different strategies, and converted to a 3D output sound using an individualized head-related transfer function. The performance of the device with different processing strategies is evaluated via initial tests with ten subjects. The outcome of these tests demonstrate promising performance of the system after only very short training times of a few minutes due to the minimal encoding of outputs from the vision sensors which are translated into simple sound patterns easily interpretable for the user. The envisioned system will allow for efficient real-time algorithms on a hands-free and lightweight device with exceptional battery life-time.

A programmable analog subthreshold biomimetic model for bi-directional communication with the brain.

In this paper, we present a hardware implementation of a second order Laguerre Expansion of Volterra Kernel (LEV) model with four basis functions. The model is versatile enough to be applied at different abstraction levels (synapse, neuron, or network of neurons) and is implemented with analog building blocks in a modular manner. These analog blocks, realized using low power subthreshold CMOS transistors, can serve as a basis for large-scale hardware systems that emulate multi-input multi-output (MIMO) spike transformations in populations of neurons. The normalized mean square error between the signals produced by the circuit LEV implementation and the ideal LEV model is 8.15%. The total power consumption of the analog circuitry is less than 33nW.

A computational model to investigate astrocytic glutamate uptake influence on synaptic transmission and neuronal spiking.

Over the past decades, our view of astrocytes has switched from passive support cells to active processing elements in the brain. The current view is that astrocytes shape neuronal communication and also play an important role in many neurodegenerative diseases. Despite the growing awareness of the importance of astrocytes, the exact mechanisms underlying neuron-astrocyte communication and the physiological consequences of astrocytic-neuronal interactions remain largely unclear. In this work, we define a modeling framework that will permit to address unanswered questions regarding the role of astrocytes. Our computational model of a detailed glutamatergic synapse facilitates the analysis of neural system responses to various stimuli and conditions that are otherwise difficult to obtain experimentally, in particular the readouts at the sub-cellular level. In this paper, we extend a detailed glutamatergic synaptic model, to include astrocytic glutamate transporters. We demonstrate how these glial transporters, responsible for the majority of glutamate uptake, modulate synaptic transmission mediated by ionotropic AMPA and NMDA receptors at glutamatergic synapses. Furthermore, we investigate how these local signaling effects at the synaptic level are translated into varying spatio-temporal patterns of neuron firing. Paired pulse stimulation results reveal that the effect of astrocytic glutamate uptake is more apparent when the input inter-spike interval is sufficiently long to allow the receptors to recover from desensitization. These results suggest an important functional role of astrocytes in spike timing dependent processes and demand further investigation of the molecular basis of certain neurological diseases specifically related to alterations in astrocytic glutamate uptake, such as epilepsy.

Analog low-power hardware implementation of a Laguerre-Volterra model of intracellular subthreshold neuronal activity.

The right level of abstraction for a model mimicking a neural function is often difficult to determine. There are trade-offs between capturing biological complexities on one hand and the scalability and efficiency of the model on the other. In this work, we describe a nonlinear Laguerre-Volterra model of the synaptic temporal integration of input spikes to postsynaptic potentials. This model is then efficiently implemented using analog subthreshold circuits and can serve as a foundation for future large-scale hardware systems that can emulate multi-input multi-output (MIMO) spike transformations in populations of neurons. The normalized mean square error in estimating real data using the circuit implementation of this model is less than 15%. The model components are modular and its parameters are adjustable for modeling temporal integration by neurons in other brain regions. The total power consumption of this nonlinear Laguerre-Volterra system is less than 5nW.

Modeling neuron-glia interactions: from parametric model to neuromorphic hardware.

Recent experimental evidence suggests that glial cells are more than just supporting cells to neurons - they play an active role in signal transmission in the brain. We herein propose to investigate the importance of these mechanisms and model neuron-glia interactions at synapses using three approaches: A parametric model that takes into account the underlying mechanisms of the physiological system, a non-parametric model that extracts its input-output properties, and an ultra-low power, fast processing, neuromorphic hardware model. We use the EONS (Elementary Objects of the Nervous System) platform, a highly elaborate synaptic modeling platform to investigate the influence of astrocytic glutamate transporters on postsynaptic responses in the detailed micro-environment of a tri-partite synapse. The simulation results obtained using EONS are then used to build a non-parametric model that captures the essential features of glutamate dynamics. The structure of the non-parametric model we use is specifically designed for efficient hardware implementation using ultra-low power subthreshold CMOS building blocks. The utilization of the approach described allows us to build large-scale models of neuron/glial interaction and consequently provide useful insights on glial modulation during normal and pathological neural function.