Responses of neurons in primary and inferior temporal visual cortices to natural scenes

Abstract

The primary visual cortex (V1) is the ¢rst cortical area to receive visual input, and inferior temporal (IT) areas are among the last along the ventral visual pathway. We recorded, in area V1 of anaesthetized cats and area ITof awake macaque monkeys, responses of neurons to videos of natural scenes. Responses were analysed to test various hypotheses concerning the nature of neural coding in these two regions. A variety of spike-train statistics were measured including spike-count distributions, interspike interval distributions, coe¤cients of variation, power spectra, Fano factors and di¡erent sparseness measures. All statistics showed non-Poisson characteristics and several revealed self-similarity of the spike trains. Spike-count distributions were approximately exponential in both visual areas for eight di¡erent videos and for counting windows ranging from 50 ms to 5 seconds. The results suggest that the neurons maximize their information carrying capacity while maintaining a ¢xed long-term-average ¢ring rate, or equivalently, minimize their average ¢ring rate for a ¢xed information carrying capacity. I N T RO DUC T IONIt has been suggested that visual representations are optimized to transmit the maximum information about the images encountered in everyday life (Uttley 1973;Linsker 1987;Barlow 1989). This simple assumption has proven su¤cient to account for the characteristics of large monopolar cells in the £y (Srinivasan et al. 1982;Van Hateren 1992;Laughlin 1981), the temporal characteristics of retinal ganglion cells (Dong & Atick 1995), human spatial frequency thresholds (Atick & Redlich 1992;Van Hateren 1993), and the psychophysics of orientation perception for short presentation times (Baddeley & Hancock 1991).Maximization of information is a powerful theoretical principle that leads to testable predictions about the ¢ring patterns of neurons. However, to generate speci¢c predictions we must make some assumptions about the nature of the neural code and the type of constraint that limits its information carrying capacity. To apply information maximization to neuronal spike trains, we must identify which of their characteristics carry information. In our analysis, we will consider two possibilities: that ¢ring rates, or more precisely, spike counts over discrete intervals of time, are the information carrying elements; or that interspike intervals play this role. Without any constraints on the rate or precision of neuronal spiking, the information carrying capacity of a spike train is in¢nite. Thus, constraints play a crucial role in any information maximization procedure. We will consider three possibilities, constraints on the maximum ¢ring rate, the average ¢ring rate, or a quantity known as the sparseness of the ¢ring-rate distribution. Identifying the nature of the constraint that limits information carrying capacity has important implications for the biophysical mechanisms that underlie neural coding.Assuming the ¢ring rates carry information, Laughlin (1981) proposed a constraint on the maximum ¢...

Statistical structure of neural spiking under non-Poissonian or other non-white stimulation

“…5A, red vs. blue line). Such power spectra are observed in cortical cells (Bair et al 1994;Baddeley et al 1997;Compte et al 2003). The burst-like input leads to extended positive input correlations, which, similar to the case of positive power-law correlations, result in a more skewed ISI distribution and pronounced positive serial correlations (Fig.…”

“…(Brunel 2000)). However, in biological neural networks the Poisson assumption is rarely strictly fulfilled (Baddeley et al 1997). Sources that induce temporal correlations are signal-related processes (Baddeley et al 1997), neuronal refractoriness (Câteau and Reyes 2006) and bursting (Bair et al 1994), adaptation (Wang 1998) and network-generated oscillations (Buzsáki and Draguhn 2004).…”

“…However, in biological neural networks the Poisson assumption is rarely strictly fulfilled (Baddeley et al 1997). Sources that induce temporal correlations are signal-related processes (Baddeley et al 1997), neuronal refractoriness (Câteau and Reyes 2006) and bursting (Bair et al 1994), adaptation (Wang 1998) and network-generated oscillations (Buzsáki and Draguhn 2004). Also the synapse with its conductance dynamics (Koch 1999) as well as short-term synaptic plasticity (Fortune and Rose 2001) contribute to correlate the effective input seen by the postsynaptic cell.…”

“…Also the synapse with its conductance dynamics (Koch 1999) as well as short-term synaptic plasticity (Fortune and Rose 2001) contribute to correlate the effective input seen by the postsynaptic cell. As a consequence of both the nonlinear neural dynamics and the temporally structured driving, the interspike-interval (ISI) statistics of the driven cell is complex (Bair et al 1994;Baddeley et al 1997;Compte et al 2003;Nawrot et al 2007).…”

The direction-selective contrast response of area 18 neurons is different for first- and second-order motion

“…The potential degradation of selectivity by saturation (Robson, 1975) is largely prevented by a contrast gain control in striate cortex, which preserves the CRF shape for nonoptimal stimuli (Albrecht & Hamilton, 1982;Skottun et al, 1987;Albrecht & Geisler, 1991;Bonds, 1991;Geisler & Albrecht, 1992;Carandini & Heeger, 1994). The CRF shape also has theoretical implications for efficiency of sensory coding (Simoncelli, 2003), both for enhancement of sparse coding (Olshausen & Field, 2004) and for ensuring efficient distribution of firing rates in response to natural images (Laughlin, 1981;Baddeley et al, 1998).…”