Pawel Hottowy

A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro.

Multineuron firing patterns are often observed, yet are predicted to be rare by models that assume independent firing. To explain these correlated network states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, with these minimal assumptions they predicted 90–99% of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this initial work was done largely on retinal tissue, and its applicability to cortical circuits is mostly unknown. This work also did not address the temporal evolution of correlated states. To investigate these issues, we applied the model to multielectrode data containing spontaneous spikes or local field potentials from cortical slices and cultures. The model worked slightly less well in cortex than in retina, accounting for 88 ± 7% (mean ± SD) of network correlations. In addition, in 8 of 13 preparations, the observed sequences of correlated states were significantly longer than predicted by concatenating states from the model. This suggested that temporal dependencies are a common feature of cortical network activity, and should be considered in future models. We found a significant relationship between strong pairwise temporal correlations and observed sequence length, suggesting that pairwise temporal correlations may allow the model to be extended into the temporal domain. We conclude that although a second-order maximum entropy model successfully predicts correlated states in cortical networks, it should be extended to account for temporal correlations observed between states.

What does the eye tell the brain?: Development of a system for the large-scale recording of retinal output activity

A multielectrode array system has been developed to study how the retina processes and encodes visual images. This system can simultaneously record the extracellular electrical activity from hundreds of retinal output neurons as a dynamic visual image is focused on the input neurons. The retinal output signals detected can be correlated with the visual input to study the neural code used by the eye to send information about the visual world to the brain. The system consists of the following components: (1) a 32 /spl times/ 16 rectangular array of 512 planar microelectrodes with a sensitive area of 1.7 square mm. The electrode spacing is 60 microns and the electrode diameter is 5 microns. (Hexagonal arrays with 519 electrodes are under development); (2) eight 64-channel custom-designed integrated circuits to platinize the electrodes and AC couple the signals; (3) eight 64-channel integrated circuits to amplify, band-pass filter and analog multiplex the signals; (4) a data acquisition system; and (5) data processing software. This paper will describe the design of the system, the experimental and data analysis techniques, and some first results with live retina. The system is based on techniques and expertise acquired in the development of silicon microstrip detectors for high energy physics experiments.

A wireless multi-channel neural amplifier for freely moving animals

Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from rats. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them into one signal and transmits that output by radio frequency to a receiver up to 60 m away. The system introduces <4 μV of electrode-referred noise, comparable to wired recording systems, and outperforms existing rodent telemetry systems in channel count, weight and transmission range. This allows effective recording of brain signals in freely behaving animals. We report measurements of neural population activity taken outdoors and in tunnels. Neural firing in the visual cortex was relatively sparse, correlated even across large distances and was strongly influenced by locomotor activity.

High-resolution electrical stimulation of primate retina for epiretinal implant design

The development of retinal implants for the blind depends crucially on understanding how neurons in the retina respond to electrical stimulation. This study used multielectrode arrays to stimulate ganglion cells in the peripheral macaque retina, which is very similar to the human retina. Analysis was restricted to parasol cells, which form one of the major high-resolution visual pathways in primates. Individual cells were characterized using visual stimuli, and subsequently targeted for electrical stimulation using electrodes 9–15 μm in diameter. Results were accumulated across 16 ON and 9 OFF parasol cells. At threshold, all cells responded to biphasic electrical pulses 0.05–0.1 ms in duration by firing a single spike with latency lower than 0.35 ms. The average threshold charge density was 0.050 ± 0.005 mC/cm2, significantly below established safety limits for platinum electrodes. ON and OFF ganglion cells were stimulated with similar efficacy. Repetitive stimulation elicited spikes within a 0.1 ms time window, indicating that the high temporal precision necessary for spike-by-spike stimulation can be achieved in primate retina. Spatial analysis of observed thresholds suggests that electrical activation occurred near the axon hillock, and that dendrites contributed little. Finally, stimulation of a single parasol cell produced little or no activation of other cells in the ON and OFF parasol cell mosaics. The low-threshold, temporally precise, and spatially specific responses hold promise for the application of high-density arrays of small electrodes in epiretinal implants.

Loss of Responses to Visual But Not Electrical Stimulation in Ganglion Cells of Rats With Severe Photoreceptor Degeneration

Retinal implants are intended to help patients with degenerative conditions by electrically stimulating surviving cells to produce artificial vision. However, little is known about how individual retinal ganglion cells respond to direct electrical stimulation in degenerating retina. Here we used a transgenic rat model to characterize ganglion cell responses to light and electrical stimulation during photoreceptor degeneration. Retinas from pigmented P23H-1 rats were compared with wild-type retinas between ages P37 and P752. During degeneration, retinal thickness declined by 50%, largely as a consequence of photoreceptor loss. Spontaneous electrical activity in retinal ganglion cells initially increased two- to threefold, but returned to nearly normal levels around P600. A profound decrease in the number of light-responsive ganglion cells was observed during degeneration, culminating in retinas without detectable light responses by P550. Ganglion cells from transgenic and wild-type animals were targeted for focal electrical stimulation using multielectrode arrays with electrode diameters of approximately 10 microns. Ganglion cells were stimulated directly and the success rate of stimulation in both groups was 60-70% at all ages. Surprisingly, thresholds (approximately 0.05 mC/cm(2)) and latencies (approximately 0.25 ms) in P23H rat ganglion cells were comparable to those in wild-type ganglion cells at all ages and showed no change over time. Thus ganglion cells in P23H rats respond normally to direct electrical stimulation despite severe photoreceptor degeneration and complete loss of light responses. These findings suggest that high-resolution epiretinal prosthetic devices may be effective in treating vision loss resulting from photoreceptor degeneration.

Changes in physiological properties of rat ganglion cells during retinal degeneration

Retinitis pigmentosa (RP) is a leading cause of degenerative vision loss, yet its progressive effects on visual signals transmitted from the retina to the brain are not well understood. The transgenic P23H rat is a valuable model of human autosomal dominant RP, exhibiting extensive similarities to the human disease pathology, time course, and electrophysiology. In this study, we examined the physiological effects of degeneration in retinal ganglion cells (RGCs) of P23H rats aged between P37 and P752, and compared them with data from wild-type control animals. The strength and the size of visual receptive fields of RGCs decreased rapidly with age in P23H retinas. Light responses mediated by rod photoreceptors declined earlier (∼ P300) than cone-mediated light responses (∼ P600). Responses of ON and OFF RGCs diminished at a similar rate. However, OFF cells exhibited hyperactivity during degeneration, whereas ON cells showed a decrease in firing rate. The application of synaptic blockers abolished about half of the elevated firing in OFF RGCs, indicating that the remodeled circuitry was not the only source of degeneration-induced hyperactivity. These results advance our understanding of the functional changes associated with retinal degeneration.

Spatially Patterned Electrical Stimulation to Enhance Resolution of Retinal Prostheses

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.

Rich-Club Organization in Effective Connectivity among Cortical Neurons

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a “rich club.” We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. SIGNIFICANCE STATEMENT Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases.

Focal electrical stimulation of major ganglion cell types in the primate retina for the design of visual prostheses.

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.

High-Degree Neurons Feed Cortical Computations.

Recent work has shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, recent theoretical developments now make it possible to quantify how neurons modify information from the connections they receive. Therefore, it is now possible to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree) or sends out (out-degree). To do this, we recorded the simultaneous spiking activity of hundreds of neurons in cortico-hippocampal slice cultures using a high-density 512-electrode array. This preparation and recording method combination produced large numbers of neurons recorded at temporal and spatial resolutions that are not currently available in any in vivo recording system. We utilized transfer entropy (a well-established method for detecting linear and nonlinear interactions in time series) and the partial information decomposition (a powerful, recently developed tool for dissecting multivariate information processing into distinct parts) to quantify computation between neurons where information flows converged. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to receive connections from high out-degree neurons. However, the in-degree of a neuron was not related to the amount of information it computed. To gain insight into these findings, we developed a simple feedforward network model. We found that a degree-modified Hebbian wiring rule best reproduced the pattern of computation and degree correlation results seen in the real data. Interestingly, this rule also maximized signal propagation in the presence of network-wide correlations, suggesting a mechanism by which cortex could deal with common random background input. These are the first results to show that the extent to which a neuron modifies incoming information streams depends on its topological location in the surrounding functional network.