Maximum decoding abilities of temporal patterns and synchronized firings: application to auditory neurons responding to click trains and amplitude modulated white noise

Abstract

Simultaneous recordings of an increasing number of neurons have recently become available, but few methods have been proposed to handle this activity. Here, we extract and investigate all the possible temporal neural activity patterns based on synchronized firings of neurons recorded on multiple electrodes, or based on bursts of single-electrode activity in cat primary auditory cortex. We apply this to responses to periodic click trains or sinusoïdal amplitude modulated noise by obtaining for each pattern its temporal modulation transfer function. An algorithm that maximizes the mutual information between all patterns and stimuli subsequently leads to the identification of patterns that optimally decode modulation frequency (MF). We show that stimulus information contained in multi-electrode synchronized firing is not redundant with single-electrode firings and leads to improved efficiency of MF decoding. We also show that the combined use of firing rate and temporal codes leads to a better discrimination of the MF.

Coordinated neuronal ensembles in primary auditory cortical columns

“…Technological advances in large-scale recordings, including calcium imaging and high-density multi-channel electrodes, have allowed the monitoring of the simultaneous activity of large populations of neurons. This has led to the demonstration of coordinated activity within groups of recorded neurons, identified and verified by statistical approaches ( Billeh et al, 2014 ; Gourévitch and Eggermont, 2010 ; Lopes-dos-Santos et al, 2013 ; Miller et al, 2014 ; Peyrache et al, 2010 ; Pipa et al, 2008 ).…”

“…However, these studies did not identify groups of cooperating neurons, treating all simultaneously recorded neurons as equivalent information-processing entities. Meanwhile, synchronous activity between local pairs of AC neurons can reflect shared and unique stimulus aspects, enabling enhanced information encoding ( Atencio and Schreiner, 2016 ; Atencio and Schreiner, 2013 ; Brosch and Schreiner, 1999 ; Eggermont, 2006 ; 1992 ; 2013 ; Gourévitch and Eggermont, 2010 ). While neuronal pairs represent the smallest possible ensemble, larger columnar or distributed networks of synchronously active neurons could reveal specific and particularly meaningful information-bearing network events ( Bathellier et al, 2012 ; Bharmauria et al, 2016 ).…”

Detection Thresholds for Amplitude Modulations of Tones in Budgerigar, Rabbit, and Human

“…In particular, a study of AM detection in rabbits has shown that they are insensitive to low-frequency modulations of tone carriers (Carney et al 2009). Gourevitch and Eggermont (2010) found, in a physiological study of auditory cortex in cat, that low-frequency AM is most effectively coded by the timing of discharges, whereas high-frequency AM is effectively coded in terms of average discharge rates. Thus, differences across species in the ability to detect low-frequency AM may indicate differences in the ability to make use of temporal information in neural responses.…”

Dynamic Correlations between Intrinsic Connectivity and Extrinsic Connectivity of the Auditory Cortex in Humans

“…The AC contains neuronal circuits at multiple scales ( Mitani et al, 1985 ; Barbour and Callaway, 2008 ; Lee and Winer, 2011 ; Winer, 2011 ), forming a hub that both extracts and integrates ascending auditory spectro-temporal features ( Griffiths and Warren, 2002 ; Eggermont, 2007 ; Gourévitch and Eggermont, 2010 ; Atencio and Schreiner, 2016 ). Although previous studies have suggested that iFC within the AC is functionally related with the pitch perception ( Albouy et al, 2013 ), auditory stream segregation ( Giani et al, 2015 ), and integration of different auditory features ( Tardif and Clarke, 2001 ), it is still not clear how iFC of the AC interacts with eFC of the AC for achieving this functions.…”

“…Bidirectional interactions have been discovered between the primary AC and the associate AC ( Fontolan et al, 2014 ). iFC of the AC is important for spectro-temporal analyses, feature extraction/integration, and learning-induced reorganization ( Griffiths and Warren, 2002 ; Eggermont, 2007 ; Gourévitch and Eggermont, 2010 ). It is of interest to know whether iFC of the AC both bottom-up modulates higher-order non-auditory cortices and is top-down modulated by the high-order cortices.…”