Because of this, the PFC can filter out distractors and up-modulate important sensory information before it even reaches the cortex. This type of attentional bias in the thalamus has been demonstrated in several studies

(Crick, 1984; McAlonan et al., 2006, 2008). The BF and mAChRs are also thought to influence sensory processing. click here Therefore, we tested how mAChR and BF stimulation affect between-trial correlations with and without attention applied to RF1. As indicated by comparing Fig. 11D and E (excitatory neurons), mAChR stimulation in RF1 seemed to have little effect on changing the reliability of the input. BF stimulation, however, was able to increase the reliability of both inputs to the cortex (Fig. 11, bottom). Goard & Dan (2009) also showed that stimulation of the BF leads to an increase in the reliability of neurons in the LGN and cortex. In addition, comparing Fig. 11E and F (excitatory neurons) shows that when the BF is stimulated, the reliability of RF2 increases to match that of RF1. This demonstrates that BF stimulation is able to override the attentional bias imposed onto RF1 and enhance both sensory inputs to the cortex. This happens as a result of GABAergic projections from the BF to the TRN, which have been Small Molecule Compound Library shown anatomically (Bickford et al., 1994). These projections make the BF very important for regulating the flow of information from the sensory periphery to the cortex. In

contrast to excitatory neurons, inhibitory neurons in our simulation showed hardly any increase in reliability when top-down attention was applied (Fig. 11, inhibitory neurons) and only a weak increase in reliability when the BF was stimulated (Fig. 11I and L). To see how the type of neuron affected between-trial correlations, we changed fast-spiking neurons in RF1 to regular-spiking neurons as above (Fig. 12). Comparing Fig. 12A–D with plots Fig. 11D, J, F and L, respectively, we see no significant changes. Thus, we can conclude that changing the spike waveform of inhibitory neurons appears not significantly to affect the between-trial correlations of either inhibitory or excitatory neurons.

The present model illustrates several important mechanisms underlying attention and neuronal correlations in visual cortex. First, our model accounts for the BF enhancement of both bottom-up sensory Carnitine dehydrogenase input and top-down attention through ‘local’ and ‘global’ neuromodulatory circuitry. Within the context of our model, glutamatergic projections from frontal cortex synapse onto cholinergic fibers in V1, causing local cholinergic transients, which, ultimately, lead to a local enhancement of top-down attention. In contrast, stimulation of the BF has a more global effect and can actually decrease the efficacy of top-down projections and increase sensory input by blocking top-down projections in the thalamus. Second, our model suggests an important role for mAChRs on both inhibitory and excitatory neurons.