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Neuromodulation technologies are progressing from pacemaking and sensory operations to full closed-loop control. In particular, optogenetics—the genetic modification of light sensitivity into neural tissue allows for simultaneous optical stimulation and electronic recording. This paper presents a neural interface application-specified integrated circuit (ASIC) for intelligent optoelectronic probes. The architecture is designed to enable simultaneous optical neural stimulation and electronic recording. It provides four low noise (2.08 μ V $_{\text{rms}}$ ) recording channels optimized for recording local field potentials (LFPs) (0.1–300 Hz bandwidth, $\pm$ 5 mV range, sampled 10-bit@4 kHz), which are more stable for chronic applications. For stimulation, it provides six independently addressable optical driver circuits, which can provide both intensity (8-bit resolution across a 1.1 mA range) and pulse-width modulation for high-radiance light emitting diodes (LEDs). The system includes a fully digital interface using a serial peripheral interface (SPI) protocol to allow for use with embedded controllers. The SPI interface is embedded within a finite state machine (FSM), which implements a command interpreter that can send out LFP data whilst receiving instructions to control LED emission. The circuit has been implemented in a commercially available 0.35 μ m CMOS technology occupying a 1.95 mm $\times$ 1.10 mm footprint for mounting onto the head of a silicon probe. Measured results are given for a variety of bench-top, in vitro and in vivo experiments, quantifying system performance and also demonstrating concurrent recording and stimulation within relevant experimental models.

Neurons sensitive to visual motion change their response properties during prolonged motion stimulation. These changes have been interpreted as adaptive and were concluded, for instance, to adjust the sensitivity of the visual motion pathway to velocity changes or to increase the reliability of encoding of motion information. These conclusions are based on experiments with experimenter-designed motion stimuli that differ substantially with respect to their dynamical properties from the optic flow an animal experiences during normal behavior. We analyze for the first time motion adaptation under natural stimulus conditions. The experiments are done on the H1-cell, an identified neuron in the blowfly visual motion pathway that has served in many previous studies as a model system for visual motion computation. We reconstructed optic flow perceived by a blowfly in free flight and used this behaviorally generated optic flow to study motion adaptation. A variety of measures (variability in spike count, response latency, jitter of spike timing) suggests that the coding quality does not improve with prolonged stimulation. However, although the number of spikes decreases considerably during stimulation with natural optic flow, the amount of information that is conveyed stays nearly constant. Thus the information per spike increases, and motion adaptation leads to parsimonious coding without sacrificing the reliability with which behaviorally relevant information is encoded.