Behavioral and Single-Neuron Sensitivity to Millisecond Variations in Temporally Patterned Communication Signals

Abstract

In many sensory pathways, central neurons serve as temporal filters for timing patterns in communication signals. However, how a population of neurons with diverse temporal filtering properties codes for natural variation in communication signals is unknown. Here we addressed this question in the weakly electric fish Brienomyrus brachyistius, which varies the time intervals between successive electric organ discharges to communicate. These fish produce an individually stereotyped signal called a scallop, which consists of a distinctive temporal pattern of ϳ8 -12 electric pulses. We manipulated the temporal structure of natural scallops during behavioral playback and in vivo electrophysiology experiments to probe the temporal sensitivity of scallop encoding and recognition. We found that presenting time-reversed, randomized, or jittered scallops increased behavioral response thresholds, demonstrating that fish's electric signaling behavior was sensitive to the precise temporal structure of scallops. Next, using in vivo intracellular recordings and discriminant function analysis, we found that the responses of interval-selective midbrain neurons were also sensitive to the precise temporal structure of scallops. Subthreshold changes in membrane potential recorded from single neurons discriminated natural scallops from time-reversed, randomized, and jittered sequences. Pooling the responses of multiple neurons improved the discriminability of natural sequences from temporally manipulated sequences. Finally, we found that single-neuron responses were sensitive to interindividual variation in scallop sequences, raising the question of whether fish may analyze scallop structure to gain information about the sender. Collectively, these results demonstrate that a population of interval-selective neurons can encode behaviorally relevant temporal patterns with millisecond precision.

Learning and recognition of tactile temporal sequences by mice and humans

“…Which mechanisms contribute to setting integration timescales? Single neurons can be sensitive to spatiotemporal input sequences ( Segundo et al, 1963 ; Branco et al, 2010 ; Baker et al, 2016 ). Learning to detect a specific sequence ( Hardy and Buonomano, 2016 ) can be accomplished by spike timing-dependent plasticity ( Masquelier et al, 2008 ; Klampfl and Maass, 2013 ; Gütig, 2014 ).…”

Learning and recognition of tactile temporal sequences by mice and humans

“…Which mechanisms contribute to setting integration timescales? Single neurons can be sensitive to spatiotemporal input sequences [57-59]. Learning to detect a specific sequence [60] can be accomplished by spike timing-dependent plasticity [61-63].…”

A History of Corollary Discharge: Contributions of Mormyrid Weakly Electric Fish

“…In addition, they suggested that, since the corollary discharge inhibition of the nELL can preserve the temporal information of communication signals from other fish, the downstream pathway should analyze temporal features of the signal. Indeed, future studies demonstrated that the ELa extracts information about temporal features of the EOD waveform that reflect the identity of signaling fish ( Friedman and Hopkins, 1998 ; Lyons-Warren et al, 2013 ), and that the posterior exterolateral nucleus (ELp), to which ELa sends its only output, extracts temporal patterns of inter-pulse intervals that reflect the behavioral state of signaling fish ( Carlson, 2009b ; Baker et al, 2016 ). Owing partly to this corollary discharge inhibition, mormyrid fishes provided a unique opportunity to study how the nervous system decodes temporal signals during communication ( Xu-Friedman and Hopkins, 1999 ; Baker et al, 2013 ).…”