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

Abstract

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.

Hierarchical computation in the canonical auditory cortical circuit

“…Previously, we have shown that parameters that describe the stimulus-based content of receptive fields (e.g., characteristic frequency, excitatory bandwidth, envelope modulation preferences) are relatively independent of parameters that describe how the processing is accomplished (e.g., receptive field separability, feature selectivity, nonlinearity structure) (22). A factor analysis revealed potential interdependences among variables that characterize how spectrotemporal stimuli are processed.…”

“…However, neuronal stimulus preferences change widely across the tonotopic array, e.g., stimulus binaurality, bandwidth, or intensity, complicating a population analysis of general processing aspects (9,10,34). Here, we focus on layer-dependent auditory processing principles (''how'') that are largely independent from the stimulus content (''what'') (22).…”

“…The first aspect substantially reduced variability introduced by the large parametric range of cortical neurons in the thalamocortical input layers and by changes in physiological state in sequential recordings along electrode tracks (4,22). The second aspect used a new window on neuronal processing, the MIDs, uncovering hitherto hidden properties of auditory cortical processing (21).…”

“…In granular layers, the first STRF is more dominant and possesses a high degree of separability and feature selectivity. Its nonlinearity is highly asymmetric reflecting the high feature selectivity of the STRF (22). In supragranular layers, the MID1 contribution is reduced, MID2 strengthens, synergy increases, and the 2D nonlinearity is less separable.…”

Complementary Contributions of Prefrontal Neuron Classes in Abstract Numerical Categorization

“…Because the duration of extracellularly recorded spike waveforms is directly related to the duration of intracellularly recorded waveforms (Henze et al, 2000;Gold et al, 2006), narrow-spiking (NS; putative interneurons) and broad-spiking neurons (BS; putative pyramidal cells) can also be discriminated with extracellular recording techniques. This approach has proven successful in several studies in the somatosensory (Simons, 1978;McCormick et al, 1985;Swadlow and Gusev, 2002), visual (Swadlow and Weyand, 1987;Gur et al, 1999;Shapley et al, 2003;Mitchell et al, 2007;Nowak et al, 2008), auditory (Atencio and Schreiner, 2008), and prefrontal cortices (Wilson et al, 1994;Rao et al, 1999;Constantinidis and Goldman-Rakic, 2002) of different mammalian species.…”