Biology subjects

Cunningham, J.

Publications and source records attributed to Cunningham, J..

5 recordsLinked to original sources

bioRxiv2018

Inhibitory and excitatory populations in parietal cortex are equally selective for decision outcome in both novices and experts

Inhibitory neurons, which play a critical role in decision-making models, are often simplified as a single pool of non-selective neurons lacking connection specificity. This assumption is supported by observations in primary visual cortex: inhibitory neurons are broadly tuned in vivo, and show non-specific connectivity in slice. Selectivity of excitatory and inhibitory neurons within decision circuits, and hence the validity of decision-making models, is unknown. We simultaneously measured excitatory and inhibitory neurons in posterior parietal cortex of mice judging multisensory stimuli. Surprisingly, excitatory and inhibitory neurons were equally selective for the animals choice, both at the single cell and population level. Further, both cell types exhibited similar changes in selectivity and temporal dynamics during learning, paralleling behavioral improvements. These observations, combined with modeling, argue against circuit architectures assuming non-selective inhibitory neurons. Instead, they argue for selective subnetworks of inhibitory and excitatory neurons that are shaped by experience to support expert decision-making.

neuroscience↗

bioRxiv2017

Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience

Modern large - scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single - neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control - developed in lockstep with advances in experimental neurotechnology - - promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time - series data with single - neuronal precision.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=46 SRC=\"FIGDIR/small/196949_fig1.gif\" ALT=\"Figure 1\">\nView larger version (13K):\norg.highwire.dtl.DTLVardef@16fff01org.highwire.dtl.DTLVardef@1ceacd4org.highwire.dtl.DTLVardef@1b49960org.highwire.dtl.DTLVardef@1c1ad13_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO The central role of data science in modern large - scale neuroscience.Topics reviewed herein are indicated in black.\n\nC_FIG

neuroscience↗

bioRxiv2017

Conservation of preparatory neural events regardless of how movement is initiated

Voluntary movement is believed to be preceded by a preparatory stage. Evidence arises from experiments where a delay separates instruction and execution cues. While this sequence emulates some real-world situations (e.g., swatting a fly upon landing) movements are commonly made at a moment of ones choosing (reaching for a coffee cup) or are made reactively (intercepting a falling cup). To ascertain whether neural events are conserved across such contexts, we examined motor cortex population-level responses in monkeys when reaches were initiated either after an imposed delay, at a self-chosen time, or reactively with very low latency. We found that the same preparatory and movement-related events were conserved. However, preparation was temporally flexible and could be remarkably brief. Our findings support the existing hypothesis that preparation is an obligatory stage that achieves a consistent state prior to movement. Yet our results reveal that preparation can unfold more rapidly than previously supposed.

neuroscience↗

bioRxiv2017

A Statistical Model of Shared Variability in the Songbird Auditory System

Vocal communication evokes robust responses in primary auditory cortex (A1) of songbirds, and single neurons from superficial and deep regions of A1 have been shown to respond selectively to songs over complex, synthetic sounds. However, little is known about how this song selectivity arises and manifests itself on the level of networks of neurons in songbird A1. Here, we examined the network-level coding of song and synthetic sounds in A1 by simultaneously recording the responses of multiple neurons in unanesthetized zebra finches. We developed a latent factor model of the joint simultaneous activity of these neural populations, and found that the shared variability in the activity has a surprisingly simple structure; it is dominated by an unobserved latent source with one degree-of-freedom. This simple model captures the structure of the correlated activity in these populations in both spontaneous and stimulus-driven conditions, and given both song and synthetic stimuli. The inferred latent variability is strongly suppressed under stimulation, consistent with similar observations in a range of mammalian cortical regions.

neuroscience↗

bioRxiv2016

Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.\n\nAuthor SummarySimultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these recordings is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is largely stymied by electrical stimulation artifacts across the array, which are typically larger than the signals of interest. We develop a novel computational framework to estimate and subtract away this contaminating artifact, enabling the large-scale analysis of responses of possibly hundreds of cells to tailored stimulation. Importantly, we suggest that this technology may also be helpful for the development of future high-resolution neural prosthetic devices (e.g., retinal prostheses).

neuroscience↗