We believe this first wave of activity is consistent with a combination of intra-area processing and feedforward inter-area processing of the visual image.

The only known means of rapidly conveying information through the ventral pathway is via the spiking activity that travels along axons. Thus, we consider the neuronal representation in a given cortical area (e.g., the “IT representation”) to be the spatiotemporal pattern of spikes produced by the set of pyramidal neurons that project out of that area (e.g., the spiking patterns traveling along the population of axons that project out of IT; see Figure 3B). How is the spiking activity of individual neurons thought to encode visual information? Most studies have investigated the response properties of neurons in the ventral pathway by assuming a firing rate (or, equivalently, a spike Tyrosine Kinase Inhibitor Library ic50 Ruxolitinib count) code, i.e., by counting how many spikes each neuron fires over several tens or hundreds of milliseconds following the presentation of a visual image, adjusted for latency (e.g., see Figures 4A and 4B). Historically, this temporal window (here called the “decoding” window) was justified by the observation that its resulting spike rate is typically well modulated by relevant parameters of the presented visual images (such as object identity, position, or size; Desimone et al., 1984, Kobatake and Tanaka, 1994b, Logothetis and Sheinberg,

1996 and Tanaka, 1996) (see examples of IT neuronal responses in Figures 4A–4C), analogous to the well-understood firing isothipendyl rate modulation in area V1 by “low level” stimulus properties such as bar orientation (reviewed by Lennie and Movshon, 2005). Like all cortical neurons, neuronal spiking throughout the ventral pathway is variable in the ms-scale timing of spikes, resulting in rate variability for repeated presentations of a nominally identical visual stimulus. This spike timing variability is consistent with a Poisson-like

stochastic spike generation process with an underlying rate determined by each particular image (e.g., Kara et al., 2000 and McAdams and Maunsell, 1999). Despite this variability, one can reliably infer what object, among a set of tested visual objects, was presented from the rates elicited across the IT population (e.g., Abbott et al., 1996, Aggelopoulos and Rolls, 2005, De Baene et al., 2007, Heller et al., 1995, Hung et al., 2005, Li et al., 2009, Op de Beeck et al., 2001 and Rust and DiCarlo, 2010). It remains unknown whether the ms-scale spike variability found in the ventral pathway is “noise” (in that it does not directly help stimulus encoding/decoding) or if it is somehow synchronized over populations of neurons to convey useful, perhaps “multiplexed” information (reviewed by Ermentrout et al., 2008). Empirically, taking into account the fine temporal structure of IT neuronal spiking patterns (e.g.