Spatial Information in Large-Scale Neural Recordings

Cybulski, Thaddeus R; Glaser, Joshua I.; Marblestone, Adam H.; Zamft, Bradley M.; Boyden, Edward S.; Church, George M.; Körding, Konrad P.

doi:10.1101/002923

Cited by 5 publications

(5 citation statements)

References 59 publications

(57 reference statements)

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“…This procedure was repeated iteratively by choosing 1, 2, 4 or 5 out of 209 electrodes. Instead of analyzing the clustering results in the PCA space, other measures, such as Fisher information, could be used 44Fig.…”

Section: Resultsmentioning

confidence: 99%

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels

et al. 2015

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Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm(2)). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons.

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Section: Resultsmentioning

confidence: 99%

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels

et al. 2015

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“…First, there are correlations that are due simply to the physical properties of the measured electric field; this is referred to as volume conduction. The magnitude of electric fields decrease as a function of one over distance squared (Cybulski et al 2014). Second, there are functional correlations in neural networks due to synaptic connectivity.…”

Section: Discussionmentioning

confidence: 99%

Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography

et al. 2016

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Objective Electrocorticography (ECoG) has become an important tool in human neuroscience and has tremendous potential for emerging applications in neural interface technology. Electrode array design parameters are outstanding issues for both research and clinical applications, and these parameters depend critically on the nature of the neural signals to be recorded. Here, we investigate the functional spatial resolution of neural signals recorded at the human cortical surface. We empirically derive spatial spread functions to quantify the shared neural activity for each frequency band of the electrocorticogram. Approach Five subjects with high-density (4mm center-to-center spacing) ECoG grid implants participated in speech perception and production tasks while neural activity was recorded from the speech cortex, including superior temporal gyrus, precentral gyrus, and postcentral gyrus. The cortical surface field potential was decomposed into traditional EEG frequency bands. Signal similarity between electrode pairs for each frequency band was quantified using a Pearson correlation coefficient. Main results The correlation of neural activity between electrode pairs was inversely related to the distance between the electrodes; this relationship was used to quantify spatial falloff functions for cortical subdomains. As expected, lower frequencies remained correlated over larger distances than higher frequencies. However, both the envelope and phase of gamma and high gamma frequencies (30-150Hz) are largely uncorrelated (<90%) at 4mm, the smallest spacing of the high-density arrays. Thus, ECoG arrays smaller than 4mm have significant promise for increasing signal resolution at high frequencies, whereas less additional gain is achieved for lower frequencies. Significance Our findings quantitatively demonstrate the dependence of ECoG spatial resolution on the neural frequency of interest. We demonstrate that this relationship is consistent across patients and across cortical areas during activity.

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“…Analytically, optimal spacing can be addressed by several approaches that have been defined for emerging neurotechnologies . One important factor is defining a useful framework for quantifying the informatic limits for a given approach (Cybulski et al, 2014; Marblestone et al, 2013) . These informatic limits can help guide technology design by quantifying the information content of an electrode array as a function of the spacing between electrodes.…”

Section: Introductionmentioning

confidence: 99%

Towards Large-Scale, Human-Based, Mesoscopic Neurotechnologies

Chang

2015

Neuron

141

115

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Direct human brain recordings have transformed the scope of neuroscience in the past decade. Progress has relied upon currently available neurophysiological approaches in the context of patients undergoing neurosurgical procedures for medical treatment. While this setting has provided precious opportunities for scientific research, it also has presented significant constraints on the development of new neurotechnologies. A major challenge now is how to achieve high-resolution spatiotemporal neural recordings at a large-scale. By narrowing the gap between current approaches, new directions tailored to the mesoscopic (intermediate) scale of resolution may overcome the barriers towards safe and reliable human-based neurotechnology development, with major implications for advancing both basic research and clinical translation.

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Spatial Information in Large-Scale Neural Recordings

Cited by 5 publications

References 59 publications

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels

Spatial resolution dependence on spectral frequency in human speech cortex electrocorticography

Towards Large-Scale, Human-Based, Mesoscopic Neurotechnologies

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