Activation of a Visual Cortical Column by a Directionally Selective Thalamocortical Neuron

Visual Corticotectal Neurons in Awake Rabbits: Receptive Fields and Driving Monosynaptic Thalamocortical Inputs

The superior colliculus receives powerful synaptic inputs from corticotectal neurons in the visual cortex. The function of these corticotectal neurons remains largely unknown due to a limited understanding of their response properties and connectivity. Here, we use antidromic methods to identify corticotectal neurons in awake male and female rabbits, and measure their axonal conduction times, thalamic inputs and receptive field properties. All corticotectal neurons responded to sinusoidal drifting gratings with a nonlinear (nonsinusoidal) increase in mean firing rate but showed pronounced differences in their ON-OFF receptive field structures that we classified into three groups, Cx, S2, and S1. Cx receptive fields had highly overlapping ON and OFF subfields as classical complex cells, S2 had largely separated ON and OFF subfields as classical simple cells, and S1 had a single ON or OFF subfield. Thus, all corticotectal neurons are homogeneous in their nonlinear spatial summation but very heterogeneous in their spatial integration of ON and OFF inputs. The Cx type had the fastest conducting axons, the highest spontaneous activity, and the strongest and fastest visual responses. The S2 type had the highest orientation selectivity, and the S1 type had the slowest conducting axons. Moreover, our cross-correlation analyses found that a subpopulation of corticotectal neurons with very fast conducting axons and high spontaneous firing rates (largely "Cx" type) receives monosynaptic input from retinotopically aligned thalamic neurons. This previously unrecognized fast-conducting thalamic-mediated corticotectal pathway may provide specialized information to superior colliculus and prime recipient neurons for subsequent corticotectal or retinal synaptic input.

Parallel pathways carrying direction-and orientation-selective retinal signals to layer 4 of the mouse visual cortex

Parallel visual pathways from the retina to the primary visual cortex (V1) via the lateral geniculate nucleus are common to many mammalian species, including mice, carnivores, and primates. However, it remains unclear which visual features present in both retina and V1 may be inherited from parallel pathways versus extracted by V1 circuits in the mouse. Here, using calcium imaging and rabies circuit tracing, we explore the relationships between tuning of layer 4 (L4) V1 neurons and their retinal ganglion cell (RGC) inputs. We find that subpopulations of L4 V1 neurons differ in their tuning for direction, orientation, spatial frequency, temporal frequency, and speed. Furthermore, we find that direction-tuned L4 V1 neurons receive input from direction-selective RGCs, whereas orientation-tuned L4 V1 neurons receive input from orientation-selective RGCs. These results suggest that direction and orientation tuning of V1 neurons may be partly inherited from parallel pathways originating in the retina.

Efficient mapping of the thalamocortical monosynaptic connectivity in vivo by tangential insertions of high-density electrodes in the cortex

The thalamus provides the principal input to the cortex and therefore understanding the mechanisms underlying cortical integration of sensory inputs requires to characterize the thalamocortical connectivity in behaving animals. Here, we propose tangential insertions of high-density electrodes into mouse cortical layer 4 as a method to capture the activity of thalamocortical axons simultaneously with their synaptically connected cortical neurons. This technique can reliably monitor multiple parallel thalamic synaptic inputs to cortical neurons, providing an efficient approach to map thalamocortical connectivity in both awake and anesthetized mice.

High-density electrode recordings reveal strong and specific connections between retinal ganglion cells and midbrain neurons

The superior colliculus is a midbrain structure that plays important roles in visually guided behaviors in mammals. Neurons in the superior colliculus receive inputs from retinal ganglion cells but how these inputs are integrated in vivo is unknown. Here, we discovered that high-density electrodes simultaneously capture the activity of retinal axons and their postsynaptic target neurons in the superior colliculus, in vivo. We show that retinal ganglion cell axons in the mouse provide a single cell precise representation of the retina as input to superior colliculus. This isomorphic mapping builds the scaffold for precise retinotopic wiring and functionally specific connection strength. Our methods are broadly applicable, which we demonstrate by recording retinal inputs in the optic tectum in zebra finches. We find common wiring rules in mice and zebra finches that provide a precise representation of the visual world encoded in retinal ganglion cells connections to neurons in retinorecipient areas.

Site-dependent shaping of field potential waveforms

The activity of neuron populations gives rise to field potentials (FPs) that extend beyond the sources. Their mixing in the volume dilutes the original temporal motifs in a site-dependent manner, a fact that has received little attention. And yet, it potentially rids of physiological significance the time-frequency parameters of individual waves (amplitude, phase, duration). This is most likely to happen when a single source or a local origin is erroneously assumed. Recent studies using spatial treatment of these signals and anatomically realistic modeling of neuron aggregates provide convincing evidence for the multisource origin and site-dependent blend of FPs. Thus, FPs generated in primary structures like the neocortex and hippocampus reach far and cross-contaminate each other but also, they add and even impose their temporal traits on distant regions. Furthermore, both structures house neurons that act as spatially distinct (but overlapped) FP sources whose activation is state, region, and time dependent, making the composition of so-called local FPs highly volatile and strongly site dependent. Since the spatial reach cannot be predicted without source geometry, it is important to assess whether waveforms and temporal motifs arise from a single source; otherwise, those from each of the co-active sources should be sought.

Brain signal predictions from multi-scale networks using a linearized framework

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 10⁶ neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials (‘spikes’) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin.

Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for.

The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.

Understanding the brain’s function and activity in healthy and pathological states across spatial scales and times spanning entire lives is one of humanity’s great undertakings. In experimental and clinical work probing the brain’s activity, a variety of electric and magnetic measurement techniques are routinely applied. However interpreting the extracellularly measured signals remains arduous due to multiple factors, mainly the large number of neurons contributing to the signals and complex interactions occurring in recurrently connected neuronal circuits. To understand how neurons give rise to such signals, mechanistic modeling combined with forward models derived using volume conductor theory has proven to be successful, but this approach currently does not scale to the systems level (encompassing millions of neurons or more) where simplified or abstract neuron representations typically are used. Motivated by experimental findings implying approximately linear relationships between times of neuronal action potentials and extracellular population signals, we provide a biophysics-based method for computing causal filters relating spikes and extracellular signals that can be applied with spike times or rates of large-scale neuronal network models for predictions of population signals without relying on ad hoc approximations.

Laminar distribution and arbor density of two functional classes of thalamic inputs to primary visual cortex

Motion/direction-sensitive and location-sensitive neurons are the two major functional types in mouse visual thalamus that project to the primary visual cortex (V1). It is under debate whether motion/direction-sensitive inputs preferentially target the superficial layers in V1, as opposed to the location-sensitive inputs, which preferentially target the middle layers. Here, by using calcium imaging to measure the activity of motion/direction-sensitive and location-sensitive axons in V1, we find evidence against these cell-type-specific laminar biases at the population level. Furthermore, using an approach to reconstruct axon arbors with identified in vivo response types, we show that, at the single-axon level, the motion/direction-sensitive axons project more densely to the middle layers than the location-sensitive axons. Overall, our results demonstrate that motion/direction-sensitive thalamic neurons project extensively to the middle layers of V1 at both the population and single-cell levels, providing further insight into the organization of thalamocortical projection in the mouse visual system.

Three rules govern thalamocortical connectivity of fast-spike inhibitory interneurons in the visual cortex

Some cortical neurons receive highly selective thalamocortical (TC) input, but others do not. Here, we examine connectivity of single thalamic neurons (lateral geniculate nucleus, LGN) onto putative fast-spike inhibitory interneurons in layer 4 of rabbit visual cortex. We show that three 'rules' regulate this connectivity. These rules concern: (1) the precision of retinotopic alignment, (2) the amplitude of the postsynaptic local field potential elicited near the interneuron by spikes of the LGN neuron, and (3) the interneuron's response latency to strong, synchronous LGN input. We found that virtually all first-order fast-spike interneurons receive input from nearly all LGN axons that synapse nearby, regardless of their visual response properties. This was not the case for neighboring regular-spiking neurons. We conclude that profuse and highly promiscuous TC inputs to layer-4 fast-spike inhibitory interneurons generate response properties that are well-suited to mediate a fast, sensitive, and broadly tuned feed-forward inhibition of visual cortical excitatory neurons.