A Dense Conformal Electrode Array for High Spatial Resolution Stimulation of Electrosensory Systems.

Studies of electrosensory systems have led to insights into to a number of general issues in biology. However, investigations of these systems have been limited by the inability to precisely control spatial patterns of electrosensory input. In this paper, an electrode array and a system to selectively stimulate spatially restricted regions of an electroreceptor array is presented. The array has 96 channels consisting of chrome/gold electrodes patterned on a flexible parylene-C substrate and encapsulated with another parylene-C layer. The conformability of the electrode array allows for optimal current driving and surface interface conditions. Recordings of neural activity at the first central processing stage in weakly electric mormyrid fish support the potential of this system for high spatial resolution stimulation and mapping of electrosensory systems.

Neural readout of a latency code in the active electrosensory system.

The latency of spikes relative to a stimulus conveys sensory information across modalities. However, in most cases, it remains unclear whether and how such latency codes are utilized by postsynaptic neurons. In the active electrosensory system of mormyrid fish, a latency code for stimulus amplitude in electroreceptor afferent nerve fibers (EAs) is hypothesized to be read out by a central reference provided by motor corollary discharge (CD). Here, we demonstrate that CD enhances sensory responses in postsynaptic granular cells of the electrosensory lobe but is not required for reading out EA input. Instead, diverse latency and spike count tuning across the EA population give rise to graded information about stimulus amplitude that can be read out by standard integration of converging excitatory synaptic inputs. Inhibitory control over the temporal window of integration renders two granular cell subclasses differentially sensitive to information derived from relative spike latency versus spike count.

Spike Train Coactivity Encodes Learned Natural Stimulus Invariances in Songbird Auditory Cortex.

The capacity for sensory systems to encode relevant information that is invariant to many stimulus changes is central to normal, real-world, cognitive function. This invariance is thought to be reflected in the complex spatiotemporal activity patterns of neural populations, but our understanding of population-level representational invariance remains coarse. Applied topology is a promising tool to discover invariant structure in large datasets. Here, we use topological techniques to characterize and compare the spatiotemporal pattern of coactive spiking within populations of simultaneously recorded neurons in the secondary auditory region caudal medial neostriatum of European starlings (Sturnus vulgaris). We show that the pattern of population spike train coactivity carries stimulus-specific structure that is not reducible to that of individual neurons. We then introduce a topology-based similarity measure for population coactivity that is sensitive to invariant stimulus structure and show that this measure captures invariant neural representations tied to the learned relationships between natural vocalizations. This demonstrates one mechanism whereby emergent stimulus properties can be encoded in population activity, and shows the potential of applied topology for understanding invariant representations in neural populations.SIGNIFICANCE STATEMENT Information in neural populations is carried by the temporal patterns of spikes. We applied novel mathematical tools from the field of algebraic topology to quantify the structure of these temporal patterns. We found that, in a secondary auditory region of a songbird, these patterns reflected invariant information about a learned stimulus relationship. These results demonstrate that topology provides a novel approach for characterizing neural responses that is sensitive to invariant relationships that are critical for the perception of natural stimuli.

A cerebellum-like circuit in the lateral line system of fish cancels mechanosensory input associated with its own movements.

An animal's own movement exerts a profound impact on sensory input to its nervous system. Peripheral sensory receptors do not distinguish externally generated stimuli from stimuli generated by an animal's own behavior (reafference) - although the animal often must. One way that nervous systems can solve this problem is to provide movement-related signals (copies of motor commands and sensory feedback) to sensory systems, which can then be used to generate predictions that oppose or cancel out sensory responses to reafference. Here, we studied the use of movement-related signals to generate sensory predictions in the lateral line medial octavolateralis nucleus (MON) of the little skate. In the MON, mechanoreceptive afferents synapse on output neurons that also receive movement-related signals from central sources, via a granule cell parallel fiber system. This parallel fiber system organization is characteristic of a set of so-called cerebellum-like structures. Cerebellum-like structures have been shown to support predictive cancellation of reafference in the electrosensory systems of fish and the auditory system of mice. Here, we provide evidence that the parallel fiber system in the MON can generate predictions that are negative images of (and therefore cancel) sensory input associated with respiratory and fin movements. The MON, found in most aquatic vertebrates, is probably one of the most primitive cerebellum-like structures and a starting point for cerebellar evolution. The results of this study contribute to a growing body of work that uses an evolutionary perspective on the vertebrate cerebellum to understand its functional diversity in animal behavior.

Understanding cerebellum in vertebrate neuroethology: From sensing in sharks and electric fish to motor sequences in movement and birdsong.

The elaborate structure of the cerebellum has been long known, although its contribution to a remarkable diversity of behavior is only recently appreciated. Taking an evolutionary perspective, we consider the wider function of the cerebellum based on insight from the function of so-called cerebellum-like structures. Cerebellum-like structures cancel the effects of self-stimulation, a function that has been well characterized in both elasmobranch and weakly electric fish. This function depends on the implementation of an adaptive filter, which provides an input-output transformation modified by specific learning rules. We argue that the broad contribution of the cerebellum to behavior can be understood by thinking of the cerebellum as an array of adaptive filters that can be coopted to a wide range of tasks. Consistent with this, studies of mammalian cerebellum reveal operations characteristic of an adaptive filter, which we review here. We then describe a model for how a cerebellar adaptive filter architecture could interact with central pattern generators and sensory feedback to coordinate sequentially patterned behavior. Finally, we use the acquisition and production of complex vocal motor sequencing in birdsong as an example to explore how this model of cerebellar adaptive filter-central pattern generator interactions may contribute to other important domains of vertebrate neuroethology. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Subthreshold membrane responses underlying sparse spiking to natural vocal signals in auditory cortex.

Natural acoustic communication signals, such as speech, are typically high-dimensional with a wide range of co-varying spectro-temporal features at multiple timescales. The synaptic and network mechanisms for encoding these complex signals are largely unknown. We are investigating these mechanisms in high-level sensory regions of the songbird auditory forebrain, where single neurons show sparse, object-selective spiking responses to conspecific songs. Using whole-cell in vivo patch clamp techniques in the caudal mesopallium and the caudal nidopallium of starlings, we examine song-driven subthreshold and spiking activity. We find that both the subthreshold and the spiking activity are reliable (i.e. the same song drives a similar response each time it is presented) and specific (i.e. responses to different songs are distinct). Surprisingly, however, the reliability and specificity of the subthreshold response was uniformly high regardless of when the cell spiked, even for song stimuli that drove no spikes. We conclude that despite a selective and sparse spiking response, high-level auditory cortical neurons are under continuous, non-selective, stimulus-specific synaptic control. To investigate the role of local network inhibition in this synaptic control, we then recorded extracellularly while pharmacologically blocking local GABAergic transmission. This manipulation modulated the strength and the reliability of stimulus-driven spiking, consistent with a role for local inhibition in regulating the reliability of network activity and the stimulus specificity of the subthreshold response in single cells. We discuss these results in the context of underlying computations that could generate sparse, stimulus-selective spiking responses, and models for hierarchical pooling.