The temporal resolution of neural codes: does response latency have a unique role?

Abstract

This article reviews the nature of the neural code in non-human primate cortex and assesses the potential for neurons to carry two or more signals simultaneously. Neurophysiological recordings from visual and motor systems indicate that the evidence for a role for precisely timed spikes relative to other spike times (ca. 1-10 ms resolution) is inconclusive. This indicates that the visual system does not carry a signal that identifies whether the responses were elicited when the stimulus was attended or not. Simulations show that the absence of such a signal reduces, but does not eliminate, the increased discrimination between stimuli that are attended compared with when the stimuli are unattended. The increased accuracy asymptotes with increased gain control, indicating limited benefit from increasing attention. The absence of a signal identifying the attentional state under which stimuli were viewed can produce the greatest discrimination between attended and unattended stimuli. Furthermore, the greatest reduction in discrimination errors occurs for a limited range of gain control, again indicating that attention effects are limited. By contrast to precisely timed patterns of spikes where the timing is relative to other spikes, response latency provides a fine temporal resolution signal (ca. 10 ms resolution) that carries information that is unavailable from coarse temporal response measures. Changes in response latency and changes in response magnitude can give rise to different predictions for the patterns of reaction times. The predictions are verified, and it is shown that the standard method for distinguishing executive and slave processes is only valid if the representations of interest, as evidenced by the neural code, are known. Overall, the data indicate that the signalling evident in neural signals is restricted to the spike count and the precise times of spikes relative to stimulus onset (response latency). These coding issues have implications for our understanding of cognitive models of attention and the roles of executive and slave systems.

Serotonin Shifts First-Spike Latencies of Inferior Colliculus Neurons

“…These selective changes are of interest because spike timing and spike count can differentially represent features of sensory stimuli. In striate cortex, spike latency varies with stimulus contrast far more than spike count, whereas spike count varies with stimulus orientation (Gawne et al, 1996;Reich et al, 2001;Oram et al, 2002). In auditory cortical region A2, the latencies of first spikes carry more information about stimulus location than does spike count, especially for a small number of stimulus repetitions (Furukawa and Middlebrooks, 2002).…”

“…Firstspike latencies can also represent different stimulus parameters than spike count does (Oram et al, 2002;Heil, 2004). For example, in striate cortex neurons, latency varies more directly with visual contrast than does spike count, whereas spike count varies more with stimulus orientation (Gawne et al, 1996;Reich et al, 2001;Oram et al, 2002). In some auditory cortex neurons, latency provides more information about the location of a stimulus than does spike count (Furukawa and Middlebrooks 2002).…”

“…For example, latency can vary with stimulus characteristics that define the receptive field of a neuron, such as the frequencies of auditory stimuli or the locations of somatosensory stimuli (Aitkin et al, 1970;Kitzes et al, 1978;Panzeri et al, 2001;Foffani et al, 2004). Firstspike latencies can also represent different stimulus parameters than spike count does (Oram et al, 2002;Heil, 2004). For example, in striate cortex neurons, latency varies more directly with visual contrast than does spike count, whereas spike count varies more with stimulus orientation (Gawne et al, 1996;Reich et al, 2001;Oram et al, 2002).…”

Visual Stimulation Decorrelates Neuronal Activity

“…Studies frequently report the Fano factor (variance/mean spike count in a given sample window). Although some studies report Fano factors Ͻ1 (Amarasingham et al 2006;Bair 1999;Gur and Snodderly 2006;Gur et al 1997;Kara et al 2000) or Ͼ2 (Vogels and Orban 1991), Fano factors are typically Ͼ1 and Ͻ1.5 in LGN (Levine and Troy 1986;Oram et al 1999;Reich et al 1997), V1 (Bradley et al 1987;Carandini 2004;Dean 1981;Geisler and Albrecht 1997;Tolhurst et al 1981Tolhurst et al , 1983Victor and Purpura 1996), V4 (McAdams and Maunsell 1999), inferotemporal cortex and anterior superior temporal sulcus (IT/STSa) (Amarasingham et al 2006;Baddeley et al 1997;Oram et al 2002;Vogels et al 1989;Wiener et al 2001), MT (Britten et al 1993;Buracas et al 1998;Snowden et al 1992;Uka and DeAngelis 2003), parietal and motor cortices (Lee et al 1998;Maynard et al 1999;Oram et al 2001), and supplementary motor cortex (Averbeck and Lee 2003). Although the variability of neuronal responses has typically been examined using a "fixed" analysis window, the variability of the initial response is less than that in the sustained or latter part of stimulus-or motor-induced activity in premotor cortex, V1, IT, and STSa (Amarasingham et al 2006;Churchland et al 2006;Muller et al 2001;Oram and Perrett 1992;Vogels and Orban 1991), suggesting that the initial transient response will be more informative than later parts of the response …”

Latency-Based Probabilistic Information Processing in Recurrent Neural Hierarchies

“…There is converging evidence from both physiological [7][8][9][10] and behavioral [11,12] investigations that latency plays a role in the neural encoding of information. Neurons in the striate cortex, for example, encode stimulus contrast into response latency [8], whereas decision making processes typically take longer depending on the number of conflicting alternatives [11], conceivably reflecting increased response latency on the neural level.…”