Temporal Coding of Visual Information in the Thalamus

Abstract

The amount of information a sensory neuron carries about a stimulus is directly related to response reliability. We recorded from individual neurons in the cat lateral geniculate nucleus (LGN) while presenting randomly modulated visual stimuli. The responses to repeated stimuli were reproducible, whereas the responses evoked by nonrepeated stimuli drawn from the same ensemble were variable. Stimulus-dependent information was quantified directly from the difference in entropy of these neural responses. We show that a single LGN cell can encode much more visual information than had been demonstrated previously, ranging from 15 to 102 bits/sec across our sample of cells. Information rate was correlated with the firing rate of the cell, for a consistent rate of 3.6 Ϯ 0.6 bits/spike (mean Ϯ SD). This information can primarily be attributed to the high temporal precision with which firing probability is modulated; many individual spikes were timed with better than 1 msec precision. We introduce a way to estimate the amount of information encoded in temporal patterns of firing, as distinct from the information in the time varying firing rate at any temporal resolution. Using this method, we find that temporal patterns sometimes introduce redundancy but often encode visual information. The contribution of temporal patterns ranged from Ϫ3.4 to ϩ25.5 bits/sec or from Ϫ9.4 to ϩ24.9% of the total information content of the responses.Key words:LGN; neural coding; information theory; entropy; white noise; reliability; variability Cells in the lateral geniculate nucleus of the thalamus (LGN) respond to spatial and temporal changes in light intensity within their receptive fields. The collective responses of many such cells constitute the input to visual cortex. All stimulus discrimination at the perceptual level must ultimately be supported by reliable differences in the neural response at the level of the LGN cell population. We are therefore interested in measuring the statistical discriminability of LGN responses elicited by different visual stimuli.It has been shown that the LGN can respond to visual stimuli with remarkable temporal precision (Reich et al., 1997). This implies that LGN neurons have the capability to signal information at high rates. Previous estimates of the information in LGN responses have used two general approaches. The first approach, stimulus reconstruction, relies on an explicit model of what the neuron is encoding, as well as an algorithm for decoding it (Bialek et al., 1991;Rieke et al., 1997). This method has been used to place lower bounds on the information encoded by single neurons (Reinagel et al., 1999) or pairs of neurons (Dan et al., 1998) in the LGN in response to dynamic visual stimuli.The second approach, the "direct" method, relies instead on statistical properties of the responses to different stimuli (the entropy of the responses). Because this involves only comparisons of spike trains, without reference to stimulus parameters, we need not know what features of the stimulus the cell ...

Information transfer in entrained cortical neurons

“…This happened when the output jitter was much smaller than the bin width so that all spike phases fell in one bin. The bin width of 1 ms used here is consistent with previous results on typical spike-time precisions (Mainen andSejnowski 1995, Reinagel andReid 2000). The maximum of S min averaged over all input distributions P n is an upper bound of the channel capacity.…”

“…Recent information-theoretical analyses of the neuronal spike trains in the fly (de Ruyter van Steveninck et al 1997, Warzecha andEgelhaaf 1999) and in the cat lateral geniculate nucleus (Reinagel et al 1999, Reinagel andReid 2000) indicate that the precise spike times contain more information about the input than the firing rate alone. The question of how the information encoded by these precise spike times could be used in cortex is an open problem (Shadlen and Newsome 1994, Softky 1995, Gur and Snodderly 1997, Shadlen and Newsome 1998, Oram et al 1999.…”

Regulation of spike timing in visual cortical circuits

“…Precision increased as stimulus contrast increased, because of an enlargement in the somatic amplitude of the inputs. Neurons in the lateral geniculate nucleus, which are driven by retinal ganglion cells, have been shown to respond precisely and reliably to a sequence of spatially uniform image frames with a fluctuating luminance 3,43 . When recordings from cells of the same type in different animals were compared, most of the events occurred at similar times during the stimulus presentation 43 .…”

“…1c). For a single neuron, the potential information content of precise and reliable spike times is many times larger than that which is contained in the firing rate, which is averaged across a typical interval of a hundred milliseconds [3][4][5][6] . The information contained in spike timing is available immediately, rather than after an averaging period.…”

“…The information contained in spike timing is available immediately, rather than after an averaging period. Furthermore, the timing of patterns of spikes can potentially transmit even more information than the timing of the individual constituent spikes 3,7 . The potential relevance of spike patterns becomes apparent when we consider neurons at the population level: when a group of similar neurons (a 'pool') produces precise and reliable spike trains, the neurons they project to receive volleys of synchronous spikes 8,9 .…”