Craig A. Atencio

Spectrotemporal Processing Differences between Auditory Cortical Fast-Spiking and Regular-Spiking Neurons

Excitatory pyramidal neurons and inhibitory interneurons constitute the main elements of cortical circuitry and have distinctive morphologic and electrophysiological properties. Here, we differentiate them by analyzing the time course of their action potentials (APs) and characterizing their receptive field properties in auditory cortex. Pyramidal neurons have longer APs and discharge as regular-spiking units (RSUs), whereas basket and chandelier cells, which are inhibitory interneurons, have shorter APs and are fast-spiking units (FSUs). To compare these neuronal classes, we stimulated cat primary auditory cortex neurons with a dynamic moving ripple stimulus and constructed single-unit spectrotemporal receptive fields (STRFs) and their associated nonlinearities. FSUs had shorter latencies, broader spectral tuning, greater stimulus specificity, and higher temporal precision than RSUs. The STRF structure of FSUs was more separable, suggesting more independence between spectral and temporal processing regimens. The nonlinearities associated with the two cell classes were indicative of higher feature selectivity for FSUs. These global functional differences between RSUs and FSUs suggest fundamental distinctions between putative excitatory and inhibitory interneurons that shape auditory cortical processing.

Cooperative Nonlinearities in Auditory Cortical Neurons

Cortical receptive fields represent the signal preferences of sensory neurons. Receptive fields are thought to provide a representation of sensory experience from which the cerebral cortex may make interpretations. While it is essential to determine a neurons receptive field, it remains unclear which features of the acoustic environment are specifically represented by neurons in the primary auditory cortex (AI). We characterized cat AI spectrotemporal receptive fields (STRFs) by finding both the spike-triggered average (STA) and stimulus dimensions that maximized the mutual information between response and stimulus. We derived a nonlinearity relating spiking to stimulus projection onto two maximally informative dimensions (MIDs). The STA was highly correlated with the first MID. Generally, the nonlinearity for the first MID was asymmetric and often monotonic in shape, while the second MID nonlinearity was symmetric and nonmonotonic. The joint nonlinearity for both MIDs revealed that most first and second MIDs were synergistic and thus should be considered conjointly. The difference between the nonlinearities suggests different possible roles for the MIDs in auditory processing.

Hierarchical computation in the canonical auditory cortical circuit

Sensory cortical anatomy has identified a canonical microcircuit underlying computations between and within layers. This feed-forward circuit processes information serially from granular to supragranular and to infragranular layers. How this substrate correlates with an auditory cortical processing hierarchy is unclear. We recorded simultaneously from all layers in cat primary auditory cortex (AI) and estimated spectrotemporal receptive fields (STRFs) and associated nonlinearities. Spike-triggered averaged STRFs revealed that temporal precision, spectrotemporal separability, and feature selectivity varied with layer according to a hierarchical processing model. STRFs from maximally informative dimension (MID) analysis confirmed hierarchical processing. Of two cooperative MIDs identified for each neuron, the first comprised the majority of stimulus information in granular layers. Second MID contributions and nonlinear cooperativity increased in supragranular and infragranular layers. The AI microcircuit provides a valid template for three independent hierarchical computation principles. Increases in processing complexity, STRF cooperativity, and nonlinearity correlate with the synaptic distance from granular layers.

Columnar Connectivity and Laminar Processing in Cat Primary Auditory Cortex

Background Radial intra- and interlaminar connections form a basic microcircuit in primary auditory cortex (AI) that extracts acoustic information and distributes it to cortical and subcortical networks. Though the structure of this microcircuit is known, we do not know how the functional connectivity between layers relates to laminar processing. Methodology/Principal Findings We studied the relationships between functional connectivity and receptive field properties in this columnar microcircuit by simultaneously recording from single neurons in cat AI in response to broadband dynamic moving ripple stimuli. We used spectrotemporal receptive fields (STRFs) to estimate the relationship between receptive field parameters and the functional connectivity between pairs of neurons. Interlaminar connectivity obtained through cross-covariance analysis reflected a consistent pattern of information flow from thalamic input layers to cortical output layers. Connection strength and STRF similarity were greatest for intralaminar neuron pairs and in supragranular layers and weaker for interlaminar projections. Interlaminar connection strength co-varied with several STRF parameters: feature selectivity, phase locking to the stimulus envelope, best temporal modulation frequency, and best spectral modulation frequency. Connectivity properties and receptive field relationships differed for vertical and horizontal connections. Conclusions/Significance Thus, the mode of local processing in supragranular layers differs from that in infragranular layers. Therefore, specific connectivity patterns in the auditory cortex shape the flow of information and constrain how spectrotemporal processing transformations progress in the canonical columnar auditory microcircuit.

Hierarchical Representations in the Auditory Cortex

Understanding the neural mechanisms of invariant object recognition remains one of the major unsolved problems in neuroscience. A common solution that is thought to be employed by diverse sensory systems is to create hierarchical representations of increasing complexity and tolerance. However, in the mammalian auditory system many aspects of this hierarchical organization remain undiscovered, including the prominent classes of high-level representations (that would be analogous to face selectivity in the visual system or selectivity to birds own song in the bird) and the dominant types of invariant transformations. Here we review the recent progress that begins to probe the hierarchy of auditory representations, and the computational approaches that can be helpful in achieving this feat.

Laminar Diversity of Dynamic Sound Processing in Cat Primary Auditory Cortex

For primary auditory cortex (AI) laminae, there is little evidence of functional specificity despite clearly expressed cellular and connectional differences. Natural sounds are dominated by dynamic temporal and spectral modulations and we used these properties to evaluate local functional differences or constancies across laminae. To examine the layer-specific processing of acoustic modulation information, we simultaneously recorded from multiple AI laminae in the anesthetized cat. Neurons were challenged with dynamic moving ripple stimuli and we subsequently computed spectrotemporal receptive fields (STRFs). From the STRFs, temporal and spectral modulation transfer functions (tMTFs, sMTFs) were calculated and compared across layers. Temporal and spectral modulation properties often differed between layers. On average, layer II/III and VI neurons responded to lower temporal modulations than those in layer IV. tMTFs were mainly band-pass in granular layer IV and became more low-pass in infragranular layers. Compared with layer IV, spectral MTFs were broader and their upper cutoff frequencies higher in layers V and VI. In individual penetrations, temporal modulation preference was similar across layers for roughly 70% of the penetrations, suggesting a common, columnar functional characteristic. By contrast, only about 30% of penetrations showed consistent spectral modulation preferences across layers, indicative of functional laminar diversity or specialization. Since local laminar differences in stimulus preference do not always parallel the main flow of information in the columnar cortical microcircuit, this indicates the influence of additional horizontal or thalamocortical inputs. AI layers that express differing modulation properties may serve distinct roles in the extraction of dynamic sound information, with the differing information specific to the targeted stations of each layer.

Receptive field dimensionality increases from the auditory midbrain to cortex.

In the primary auditory cortex, spectrotemporal receptive fields (STRFs) are composed of multiple independent components that capture the processing of disparate stimulus aspects by any given neuron. The origin of these multidimensional stimulus filters in the central auditory system is unknown. To determine whether multicomponent STRFs emerge prior to the forebrain, we recorded from single neurons in the main obligatory station of the auditory midbrain, the inferior colliculus. By comparing results of different spike-triggered techniques, we found that the neural responses in the inferior colliculus can be accounted for by a single stimulus filter. This was observed for all temporal response patterns, from strongly phasic to tonic. Our results reveal that spectrotemporal stimulus encoding undergoes a fundamental transformation along the auditory neuraxis, with the emergence of multidimensional receptive fields beyond the auditory midbrain.

Auditory cortical local subnetworks are characterized by sharply synchronous activity

In primary auditory cortex (AI), broadly correlated firing has been commonly observed. In contrast, sharply synchronous firing has rarely been seen and has not been well characterized. Therefore, we examined cat AI local subnetworks using cross-correlation and spectrotemporal receptive field (STRF) analysis for neighboring neurons. Sharply synchronous firing responses were observed predominantly for neurons separated by <150 μm. This high synchrony was independent of layers and was present between all distinguishable cell types. The sharpest synchrony was seen in supragranular layers and between regular spiking units. Synchronous spikes conveyed more stimulus information than nonsynchronous spikes. Neighboring neurons in all layers had similar best frequencies and similar STRFs, with the highest similarity in supragranular and granular layers. Spectral tuning selectivity and latency were only moderately conserved in these local, high-synchrony AI subnetworks. Overall, sharp synchrony is a specific characteristic of fine-scale networks within the AI and local functional processing is well ordered and similar, but not identical, for neighboring neurons of all cell types.

Spectrotemporal Processing in Spectral Tuning Modules of Cat Primary Auditory Cortex

Spectral integration properties show topographical order in cat primary auditory cortex (AI). Along the iso-frequency domain, regions with predominantly narrowly tuned (NT) neurons are segregated from regions with more broadly tuned (BT) neurons, forming distinct processing modules. Despite their prominent spatial segregation, spectrotemporal processing has not been compared for these regions. We identified these NT and BT regions with broad-band ripple stimuli and characterized processing differences between them using both spectrotemporal receptive fields (STRFs) and nonlinear stimulus/firing rate transformations. The durations of STRF excitatory and inhibitory subfields were shorter and the best temporal modulation frequencies were higher for BT neurons than for NT neurons. For NT neurons, the bandwidth of excitatory and inhibitory subfields was matched, whereas for BT neurons it was not. Phase locking and feature selectivity were higher for NT neurons. Properties of the nonlinearities showed only slight differences across the bandwidth modules. These results indicate fundamental differences in spectrotemporal preferences - and thus distinct physiological functions - for neurons in BT and NT spectral integration modules. However, some global processing aspects, such as spectrotemporal interactions and nonlinear input/output behavior, appear to be similar for both neuronal subgroups. The findings suggest that spectral integration modules in AI differ in what specific stimulus aspects are processed, but they are similar in the manner in which stimulus information is processed.

Spectral Processing in Auditory Cortex

Historically, the main purpose of the auditory system has been interpreted as a frequency analyzer (Ohm 1843; von Helmholtz 1863) that provides a faithful spectral representation of the received acoustic waveform. Analysis and characterization of spectral processing, beginning with the principle of parallel signal processing in narrow, partially overlapping frequency channels in the cochlea, has provided a framework for all subsequent stages of computation, information extraction and encoding in the auditory system, including the auditory cortex. This still evolving bottom-up characterization around the concept of a set of parallel frequency filters has been significantly enhanced by including temporal or dynamic and nonlinear aspects of spectral processing. Quantitative and rigorous systems and information analysis approaches have resulted in more complete characterizations of spectral encoding and decoding abilities throughout the auditory system.