Local mAChR activation via top-down attentional signals is also important in our model for facilitating top-down attention in V1 and helps to both increase the firing rate and decrease noise correlations between these neurons (Herrero et al., 2008; Goard & Dan, 2009). Specifically, our model highlights how mAChR stimulation of excitatory neurons is important for attentional modulation BVD-523 purchase while mAChR stimulation of inhibitory neurons is important for maintaining low levels of excitatory–excitatory correlations when
excitatory drive is increased. Contrary to recent experimental studies, which suggest a decrease in excitatory–excitatory correlations between neurons with BF stimulation and top-down attention, our model indicates that
attention and mAChR stimulation in V1 lead to a decrease in excitatory–inhibitory correlations, but cause no change in excitatory–excitatory correlations. Thus, because it is difficult to distinguish between excitatory and inhibitory neurons experimentally (Nowak et al., 2003; Vigneswaran et al., 2011), it is possible that experimenters are seeing excitatory–inhibitory rather than selleck chemicals excitatory–excitatory decorrelations. This is a strong prediction of our model. We suggest inhibition may act as a mechanism for absorbing additional excitatory input that may result from increased excitatory drive from top-down attentional signals or Lonafarnib activation of mAChRs on excitatory neurons in order to extinguish excess excitatory–excitatory correlations. A model was developed that contained two cortical columns, simulating two receptive fields, and was subject to both neuromodulation by the BF and top-down
attention (see Fig. 3). Input to the model was a movie of a natural scene as described below. Our goal was to see how neuromodulatory and top-down attention signals interacted and influenced between-trial and between-neuron correlations in the simulated cortical columns. Our experiment consisted of 60 trials, in which a 12-s natural scene video was input to the spiking neural network. We used this natural stimulus because it is similar to that used in Goard & Dan’s (2009) experiments and affords comparison of our model’s responses with their results. The video was obtained from the van Hateren movie database to the network (http://biology.ucsd.edu/labs/reinagel/pam/NaturalMovie.html). Experiments consisted of six blocks of ten trials (see Fig. 2A). In each block of ten trials, five were performed without BF stimulation, top-down attention and/or mAChR stimulation (control) followed by five trials with BF stimulation, top-down attention and/or mAChR stimulation (non-control). In between each trial and block, 1 and 4 s, respectively, of random, Poissonian spikes was injected into the network at a rate of 2 Hz to allow network activity to settle. The total simulation time of the experiment was 13.4 min.