Canonical Microcircuits for Predictive Coding

Bastos, AM. Usrey, WM. Adams, RA. Mangun, GR. Fries, P. Friston, KJ. (2012) Canonical Microcircuits for Predictive Coding. Neuron 76: 695-711.

This looks like a good review that covers some papers I've been meaning to get to.

Predictive coding is the most plausible candidate for making generative models.

superficial layers of cortex show neuronal synchronization in gamma range, deep layers prefer alpha or beta - Maier 2010, Buffalo 2011. Feedforward connection originate from superficial layers, feedback from deep layers.

Statistical connections show that most are "feedforward" L4-L23-L5. Fewer feedback. feedback connections were typically seen when pyramidal cells in  one layer targeted inhibitory cells in another.

Feedforward connections are thought to be driving and can cause spiking, feedback connections are thought to modulate receptive field characteristincs according to the context. Feedforward have strong, depressing EPSPs, feedback have weak facilitating EPSPs. Sherman 2011 - retinal input to LGN is driving, cortical input is modulatory. But other studies suggest that feedback and feedforward can both have driving and modulatory effects.

Feedback connections convey predictions, feedforward connections convey prediction errors. Effective feedback "connectivity is generally assumed to be inhibitory." Prediction errors lead to more gamma activity - from superficial layers failing to supress deeper layers. Todorovic 2011, Wacongne 2011. Imaging studies also show less activity when stimuli are predictable. (seems that inhibition has biggest influence in the surround).

Most long-range feedback connections are glutamatergic, although some may be inhibitory. L1 inhibitory neurons could be mediating this inhibition.

Simple cells in L4, complex cells in L2/3 and deep layers. Simple cells have driving effects on complex cells.

Feedforward is sent through the gamma-band. Feedback is sent through alpha-beta frequencies

Predicitve coding = Bayesian inference. Hierarchical. Biology is minimizing surprise (entropy) which mean maximizing bayesian evidence for their generative model. Can build an entire model based on predictive coding equations, subtractive errors etc.

Figure 5: Left: the canonical microcircuit based on Haeusler and Maass (2007), in which we have removed inhibitory cells from the deep layers because they have very little interlaminar connectivity. The numbers denote connection strengths (mean amplitude of PSPs measured at soma in mV) and connection probabilities (in parentheses) according to Thomson et al. (2002). Right: the proposed cortical microcircuit for predictive coding, in which the quantities of the previous figure have been associated with various cell types. Here, prediction error populations are highlighted in pink. Inhibitory connections are shown in red, while excitatory
connections are in black. The dotted lines refer to connections that are not present in the microcircuit on the left (but see Figure 2). In this scheme, expectations (about causes and states) are assigned to (excitatory and inhibitory) interneurons in the supragranular layers, which are passed to infragranular layers. The
corresponding prediction errors occupy granular layers, while superficial pyramidal cells encode prediction errors that are sent forward to the next hierarchical level. Conditional expectations and prediction errors on hidden causes are associated with excitatory cell types, while the corresponding quantities for hidden
states are assigned to inhibitory cells. Dark circles indicate pyramidal cells. Finally, we have placed the precision of the feedforward prediction errors against the superficial pyramidal cells. This quantity controls the postsynaptic sensitivity or gain to (intrinsic and top-down) presynaptic inputs. We have previously discussed this in terms of attentional modulation, which may be intimately linked to the synchronization of presynaptic inputs and ensuing postsynaptic responses (Feldman and Friston, 2010; Fries et al., 2001).

This is based on equation 1:

And they mathematically describe how the different frequencies would dominate in the different layers based on these equations.