Event Abstract Back to Event Recording Single-Unit Data From Acute Cortical Slices On A High-Density (300 electrodes/mm2) 512-Channel Micro-Needle Array Douglas Young1, Pawel Hottowy2, Wladyslaw Dabrowski2, Alexander Sher3, John Beggs4, Alan Litke5, Keith Mathieson1 and Deborah E. Gunning6* 1 University of Strathclyde, Institute of Photonics, Department of Physics, United Kingdom 2 AGH University of Science and Technology, Institute of Physics, Poland 3 University of California, Santa Cruz, SCIPP, United States 4 Indiana University, Biocomplexity Institute, United States 5 University of California, Santa Cruz, SCIPP, United States 6 University of Strathclyde, Institute of Photonics, Department of Physics, United Kingdom Acute brain slices are an important model for the electrophysiological study of neural connectivity in vitro. A technological approach to bypassing the intrinsically damaged surface layer of these freshly cut slices, to record simultaneously from many connected neurons in the in tact interior volume of the slice, is to use of arrays of penetrating microelectrodes. The current state-of-the-art in devices of this nature work very well to record from neurons in acute (and in vivo) neural preparations but have widely spaced needles and as a result, limited spatial resolution. Addressing this limitation, we present a novel, high-density penetrating micro-needle electrode array and its performance in experiments with acute cortical slices from wild type rats. The developed micro-needle array has the unprecedented combination of: high electrode count (512); large area coverage (1mm x 2mm); high electrode density (60 μm pitch); variable needle heights (up to 300 μm); and small electrode diameters (down to 5μm). As an excellent first result, the array was shown to record from >50 acute cortical neurons simultaneously, in a single preparation, and with a signal to noise of ~10:1. Figure 1 Keywords: Electrodes, Cortex, Micro-needle, Acute slice Conference: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016. Presentation Type: oral Topic: MEA Meeting 2016 Citation: Young D, Hottowy P, Dabrowski W, Sher A, Beggs J, Litke A, Mathieson K and Gunning DE (2016). Recording Single-Unit Data From Acute Cortical Slices On A High-Density (300 electrodes/mm2) 512-Channel Micro-Needle Array. Front. Neurosci. Conference Abstract: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays. doi: 10.3389/conf.fnins.2016.93.00022 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 Jun 2016; Published Online: 24 Jun 2016. * Correspondence: Dr. Deborah E Gunning, University of Strathclyde, Institute of Photonics, Department of Physics, Glasgow, United Kingdom, deborah.gunning@strath.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Douglas Young Pawel Hottowy Wladyslaw Dabrowski Alexander Sher John Beggs Alan Litke Keith Mathieson Deborah E Gunning Google Douglas Young Pawel Hottowy Wladyslaw Dabrowski Alexander Sher John Beggs Alan Litke Keith Mathieson Deborah E Gunning Google Scholar Douglas Young Pawel Hottowy Wladyslaw Dabrowski Alexander Sher John Beggs Alan Litke Keith Mathieson Deborah E Gunning PubMed Douglas Young Pawel Hottowy Wladyslaw Dabrowski Alexander Sher John Beggs Alan Litke Keith Mathieson Deborah E Gunning Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

The function of a neural circuit is shaped by the computations performed by its interneurons, which in many cases are not easily accessible to experimental investigation. Here, we elucidate the transformation of visual signals flowing from the input to the output of the primate retina, using a combination of large-scale multi-electrode recordings from an identified ganglion cell type, visual stimulation targeted at individual cone photoreceptors, and a hierarchical computational model. The results reveal nonlinear subunits in the circuity of OFF midget ganglion cells, which subserve high-resolution vision. The model explains light responses to a variety of stimuli more accurately than a linear model, including stimuli targeted to cones within and across subunits. The recovered model components are consistent with known anatomical organization of midget bipolar interneurons. These results reveal the spatial structure of linear and nonlinear encoding, at the resolution of single cells and at the scale of complete circuits.

Retinal prostheses electrically stimulate neurons to produce artificial vision in people blinded by photoreceptor degenerative diseases. The limited spatial resolution of current devices results in indiscriminate stimulation of interleaved cells of different types, precluding veridical reproduction of natural activity patterns in the retinal output. Here we investigate the use of spatial patterns of current injection to increase the spatial resolution of stimulation, using high-density multielectrode recording and stimulation of identified ganglion cells in isolated macaque retina. As previously shown, current passed through a single electrode typically induced a single retinal ganglion cell spike with submillisecond timing precision. Current passed simultaneously through pairs of neighboring electrodes modified the probability of activation relative to injection through a single electrode. This modification could be accurately summarized by a piecewise linear model of current summation, consistent with a simple biophysical model based on multiple sites of activation. The generalizability of the piecewise linear model was tested by using the measured responses to stimulation with two electrodes to predict responses to stimulation with three electrodes. Finally, the model provided an accurate prediction of which among a set of spatial stimulation patterns maximized selective activation of a cell while minimizing activation of a neighboring cell. The results demonstrate that tailored multielectrode stimulation patterns based on a piecewise linear model may be useful in increasing the spatial resolution of retinal prostheses.

Understanding the detailed circuitry of functioning neuronal networks is one of the major goals of neuroscience. Recent improvements in neuronal recording techniques have made it possible to record the spiking activity from hundreds of neurons simultaneously with sub-millisecond temporal resolution. Here we used a 512-channel multielectrode array system to record the activity from hundreds of neurons in organotypic cultures of cortico-hippocampal brain slices from mice. To probe the network structure, we employed a wavelet transform of the cross-correlogram to categorize the functional connectivity in different frequency ranges. With this method we directly compare, for the first time, in any preparation, the neuronal network structures of cortex and hippocampus, on the scale of hundreds of neurons, with sub-millisecond time resolution. Among the three frequency ranges that we investigated, the lower two frequency ranges (gamma (30-80 Hz) and beta (12-30 Hz) range) showed similar network structure between cortex and hippocampus, but there were many significant differences between these structures in the high frequency range (100-1000 Hz). The high frequency networks in cortex showed short tailed degree-distributions, shorter decay length of connectivity density, smaller clustering coefficients, and positive assortativity. Our results suggest that our method can characterize frequency dependent differences of network architecture from different brain regions. Crucially, because these differences between brain regions require millisecond temporal scales to be observed and characterized, these results underscore the importance of high temporal resolution recordings for the understanding of functional networks in neuronal systems.

The propagation of visual signals from individual cone photoreceptors through parallel neural circuits was examined in the primate retina. Targeted stimulation of individual cones was combined with simultaneous recording from multiple retinal ganglion cells of identified types. The visual signal initiated by an individual cone produced strong responses with different kinetics in three of the four numerically dominant ganglion cell types. The magnitude and kinetics of light responses in each ganglion cell varied nonlinearly with stimulus strength but in a manner that was independent of the cone of origin after accounting for the overall input strength of each cone. Based on this property of independence, the receptive field profile of an individual ganglion cell could be well estimated from responses to stimulation of each cone individually. Together, these findings provide a quantitative account of how elementary visual inputs form the ganglion cell receptive field.

Electrical stimulation of retinal neurons with an advanced retinal prosthesis may eventually provide high-resolution artificial vision to the blind. However, the success of future prostheses depends on the ability to activate the major parallel visual pathways of the human visual system. Electrical stimulation of the five numerically dominant retinal ganglion cell types was investigated by simultaneous stimulation and recording in isolated peripheral primate (Macaca sp.) retina using multi-electrode arrays. ON and OFF midget, ON and OFF parasol, and small bistratified ganglion cells could all be activated directly to fire a single spike with submillisecond latency using brief pulses of current within established safety limits. Thresholds for electrical stimulation were similar in all five cell types. In many cases, a single cell could be specifically activated without activating neighboring cells of the same type or other types. These findings support the feasibility of direct electrical stimulation of the major visual pathways at or near their native spatial and temporal resolution.

Sensory neurons have been hypothesized to efficiently encode signals from the natural environment subject to resource constraints. The predictions of this efficient coding hypothesis regarding the spatial filtering properties of the visual system have been found consistent with human perception, but they have not been compared directly with neural responses. Here, we analyze the information that retinal ganglion cells transmit to the brain about the spatial information in natural images subject to three resource constraints: the number of retinal ganglion cells, their total response variances, and their total synaptic strengths. We derive a model that optimizes the transmitted information and compare it directly with measurements of complete functional connectivity between cone photoreceptors and the four major types of ganglion cells in the primate retina, obtained at single-cell resolution. We find that the ganglion cell population exhibited 80% efficiency in transmitting spatial information relative to the model. Both the retina and the model exhibited high redundancy (∼30%) among ganglion cells of the same cell type. A novel and unique prediction of efficient coding, the relationships between projection patterns of individual cones to all ganglion cells, was consistent with the observed projection patterns in the retina. These results indicate a high level of efficiency with near-optimal redundancy in visual signaling by the retina.

Objective. Modern multielectrode array (MEA) systems can record the neuronal activity from thousands of electrodes, but their ability to provide spatio-temporal patterns of electrical stimulation is very limited. Furthermore, the stimulus-related artifacts significantly limit the ability to record the neuronal responses to the stimulation. To address these issues, we designed a multichannel integrated circuit for a patterned MEA-based electrical stimulation and evaluated its performance in experiments with isolated mouse and rat retina. Approach. The Stimchip includes 64 independent stimulation channels. Each channel comprises an internal digital-to-analogue converter that can be configured as a current or voltage source. The shape of the stimulation waveform is defined independently for each channel by the real-time data stream. In addition, each channel is equipped with circuitry for reduction of the stimulus artifact. Main results. Using a high-density MEA stimulation/recording system, we effectively stimulated individual retinal ganglion cells (RGCs) and recorded the neuronal responses with minimal distortion, even on the stimulating electrodes. We independently stimulated a population of RGCs in rat retina, and using a complex spatio-temporal pattern of electrical stimulation pulses, we replicated visually evoked spiking activity of a subset of these cells with high fidelity. Significance. Compared with current state-of-the-art MEA systems, the Stimchip is able to stimulate neuronal cells with much more complex sequences of electrical pulses and with significantly reduced artifacts. This opens up new possibilities for studies of neuronal responses to electrical stimulation, both in the context of neuroscience research and in the development of neuroprosthetic devices.

To understand a neural circuit requires knowledge of its connectivity. Here we report measurements of functional connectivity between the input and ouput layers of the macaque retina at single-cell resolution and the implications of these for colour vision. Multi-electrode technology was used to record simultaneously from complete populations of the retinal ganglion cell types (midget, parasol and small bistratified) that transmit high-resolution visual signals to the brain. Fine-grained visual stimulation was used to identify the location, type and strength of the functional input of each cone photoreceptor to each ganglion cell. The populations of ON and OFF midget and parasol cells each sampled the complete population of long- and middle-wavelength-sensitive cones. However, only OFF midget cells frequently received strong input from short-wavelength-sensitive cones. ON and OFF midget cells showed a small non-random tendency to selectively sample from either long- or middle-wavelength-sensitive cones to a degree not explained by clumping in the cone mosaic. These measurements reveal computations in a neural circuit at the elementary resolution of individual neurons.

* These authors contributed equally. To understand a neural circuit requires knowing the pattern of connectivity between its inputs and outputs. For example, the role of the retina in color vision depends on the pattern of connectivity between the lattice of (L) ong,(M) iddle and (S) hort-wavelength sensitive cones and multiple types of retinal ganglion cells, each of which samples the visual field uniformly. In the vertebrate nervous system, this kind of comprehensive circuit information has generally been out of reach. Here we report the first measurements detailing functional connectivity between the input and output layers of the primate retina, and we use this information to probe the neural circuitry for color vision. We employed a unique multi-electrode technology to record simultaneously from complete populations of the ganglion cell types that mediate high-resolution vision in primates (midget, parasol, small bistratified). We then used fine-grained visual stimulation to separately identify the location and spectral type of each cone photoreceptor providing input to each ganglion cell. The populations of ON and OFF midget and

1 Systems Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 2 Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, CA 3 Department of Physics and Astronomy, University of Glasgow, Glasgow, UK 4 Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 23 30-059, Krakow, Poland 5 Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY * These authors contributed equally.

Recently, it has been shown [3] that it is possible to get a handle on both the input and the output cells of macaque retinas, and the functional connections between them: if sufficiently fine-grained stimuli are used to excite the retina, the Spike Triggered Average receptive field of ganglion cells appear to be composed of small islands of light sensitivity, which are in fact the receptive fields of individual cones. Here, we address the problem of identifying the number, locations and types of cones in a way that provides information on how certain we can be of our inference. This is done using Markov Chain Monte Carlo (MCMC) on a familiar encoding model of ganglion cells where the functional weights have been integrated out. We obtain inferences of higher quality than with the greedy method used in [3]. stimulus

We present the design and preliminary test result of a large-scale MEA-based system for spatio-temporal distributed stimulation and recording of neural activity. The system is based on a 512-electrode array with 60 �m inter-electrode spacing and dedicated multichannel integrated circuits for independent stimulation and recording on all the electrodes. In the preliminary tests we stimulated individual neurons in rat retina and cultured cortical slices and recorded the evoked spikes as soon as 55 �s after the stimulation pulse on the stimulating electrode, and 5 �s after the pulse on other electrodes and were able to detect and classify even the low-latency spikes from directly activated neurons.