Neuronal circuit function is governed by precise patterns of connectivity between specialized groups of neurons. The diversity of GABAergic interneurons is a hallmark of cortical circuits, yet little is known about their targeting to individual postsynaptic dendrites. We examined synaptic connectivity between molecularly defined inhibitory interneurons and CA1 pyramidal cell dendrites using correlative light-electron microscopy and large-volume array tomography. We show that interneurons can be highly selective in their connectivity to specific dendritic branch types and, furthermore, exhibit precisely targeted connectivity to the origin or end of individual branches. Computational simulations indicate that the observed subcellular targeting enables control over the nonlinear integration of synaptic input or the initiation and backpropagation of action potentials in a branch-selective manner. Our results demonstrate that connectivity between interneurons and pyramidal cell dendrites is more precise and spatially segregated than previously appreciated, which may be a critical determinant of how inhibition shapes dendritic computation.

The subcellular locations of synapses on pyramidal neurons strongly influences dendritic integration and synaptic plasticity. Despite this, there is little quantitative data on spatial distributions of specific types of synaptic input. Here we use array tomography (AT), a high-resolution optical microscopy method, to examine thalamocortical (TC) input onto layer 5 pyramidal neurons. We first verified the ability of AT to identify synapses using parallel electron microscopic analysis of TC synapses in layer 4. We then use large-scale array tomography (LSAT) to measure TC synapse distribution on L5 pyramidal neurons in a 1.00 × 0.83 × 0.21 mm(3) volume of mouse somatosensory cortex. We found that TC synapses primarily target basal dendrites in layer 5, but also make a considerable input to proximal apical dendrites in L4, consistent with previous work. Our analysis further suggests that TC inputs are biased toward certain branches and, within branches, synapses show significant clustering with an excess of TC synapse nearest neighbors within 5-15 μm compared to a random distribution. Thus, we show that AT is a sensitive and quantitative method to map specific types of synaptic input on the dendrites of entire neurons. We anticipate that this technique will be of wide utility for mapping functionally-relevant anatomical connectivity in neural circuits.

DNA sequencing-by-synthesis (SBS) technology, using a polymerase or ligase enzyme as its core biochemistry, has already been incorporated in several second-generation DNA sequencing systems with significant performance. Notwithstanding the substantial success of these SBS platforms, challenges continue to limit the ability to reduce the cost of sequencing a human genome to $100,000 or less. Achieving dramatically reduced cost with enhanced throughput and quality will require the seamless integration of scientific and technological effort across disciplines within biochemistry, chemistry, physics and engineering. The challenges include sample preparation, surface chemistry, fluorescent labels, optimizing the enzyme-substrate system, optics, instrumentation, understanding tradeoffs of throughput versus accuracy, and read-length/phasing limitations. By framing these challenges in a manner accessible to a broad community of scientists and engineers, we hope to solicit input from the broader research community on means of accelerating the advancement of genome sequencing technology.

We review recent progress in neural probes for brain recording, with a focus on the Neuropixels platform. Historically the number of neurons’ recorded simultaneously, follows a Moore’s law like behavior, with numbers doubling every 6.7 years. Using traditional techniques of probe fabrication, continuing to scale up electrode densities is very challenging. We describe a custom CMOS process technology that enables electrode counts well beyond 1000 electrodes; with the aim to characterize large neural populations with single neuron spatial precision and millisecond timing resolution. This required integrating analog and digital circuitry with the electrode array, making it a standalone integrated electrophysiology recording system. Input referred noise and power per channel is 7.5µV and <50µW respectively to ensure tissue heating <1°C. This approach enables doubling the number of measured neurons every 12 months.

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density ( 10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron’s electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to 3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

To study the neural basis of behavior, we require methods to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology is a principal method for achieving this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To overcome these limitations, we developed a silicon probe with significantly smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device measures neuronal activity at ultra-high densities (>1300 sites per mm, 10 times higher than previous probes), with 6 µm center-to-center spacing and low noise. This device effectively comprises an implantable voltage-sensing camera that captures a planar image of a neuron's electrical field. We introduce a new spike sorting algorithm optimized for these probes and use it to find that the yield of visually-responsive neurons in recordings from mouse visual cortex improves ∼3-fold. Recordings across multiple brain regions and four species revealed a subset of unexpectedly small extracellular action potentials not previously reported. Further experiments determined that, in visual cortex, these do not correspond to major subclasses of interneurons and instead likely reflect recordings from axons. Finally, using ground-truth identification of cortical inhibitory cell types with optotagging, we found that cell type was discriminable with approximately 75% success among three types, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, sampling bias, and cell type classification.

The simultaneous visualization, identification and targeting of neurons during patch clamp-mediated electrophysiological recordings is a basic technique in neuroscience, yet it is often complicated by the inability to visualize the pipette tip, particularly in deep brain tissue. Here we demonstrate a novel approach in which fluorescent quantum dot probes are used to coat pipettes prior to their use. The strong two-photon absorption cross sections of the quantum dots afford robust contrast at significantly deeper penetration depths than current methods allow. We demonstrate the utility of this technique in multiple recording formats both in vitro and in vivo where imaging of the pipettes is achieved at remarkable depths (up to 800 microns). Notably, minimal perturbation of cellular physiology is observed over the hours-long time course of neuronal recordings. We discuss our results within the context of the role that quantum dot nanoprobes may play in understanding neuronal cell physiology.

The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.