Opposing influence of top-down and bottom-up input on different types of excitatory layer 2/3 neurons in mouse visual cortex

Rebecca Jordan, Georg Keller

Preprint posted on 23 March 2020

Article now published in Neuron at

Intracellular mismatch! Jordan and Keller record membrane voltage of V1 pyramidal neurons during visuomotor mismatch responses in mice, showing layer specific prediction error computations.

Selected by Mahesh Karnani

Categories: neuroscience, physiology


Predictive coding is a classic topic in neuroscience because prediction is central to the function of nervous systems. From accurately maintaining a 2-m distance with coughing people on the street to planning a week of remote work, generating predictions is a key brain process. In the past decade, Georg Keller’s lab has lead the effort in studying predictive processing in the mouse primary visual cortex (V1)1. Their work has typically consisted of Ca2+ imaging of neural activity across multiple neural cell types during a visuomotor mismatch paradigm2. This paradigm, also used in the preprint discussed here, entails a head-fixed mouse running in a visual virtual reality corridor where the speed of the visual flow is coupled to movement of a floating styrofoam ball, which the mouse moves as it runs. The visual flow is then suddenly stopped for 1 second, causing a mismatch between visual flow and mouse movement, which triggers robust mismatch responses in the V1 neurons.

The general framework of predictive coding with a forward model is well explained in [ref 1] and the below video. The hypothesis calls for the existence of three key nodes: representation neurons (observation of external world), prediction neurons (internal forward model) and prediction error neurons (comparison of observation and internal prediction). In the visuomotor mismatch paradigm, the internal forward model should predict expected visual flow speed based on locomotion speed, constituting an efference copy. If the hypothesis holds, the prediction error neurons should compute a difference of the input they get from representation and prediction neurons. The preprint demonstrates that this is indeed the case in L2/3 of V1.



Key findings

The preprint shows that L2/3 pyramidal neurons have heterogeneous subthreshold membrane potential responses to visuomotor mismatch. Many of them have depolarizing mismatch responses (dMM), and some have hyperpolarizing mismatch responses (hMM). While these had been indicated by previous data with Ca2+ imaging3, this is the first demonstration of these subtypes with whole cell recordings. The method is key here because it reveals subthreshold membrane potential (Vm) responses arising from synaptic input. It also allowed the authors to report electrophysiological properties of these cells for the first time.

The authors then record from the same neurons with uncoupled visual flow and locomotion to find out their separate influences on the cells. These recordings demonstrate that the sensory and locomotion computation in most of these neurons are consistent with the predictive coding hypothesis: in dMM neurons Vm correlation with locomotion speed is positive (prediction input) while correlation with  visual flow speed is negative (observation input), while the opposite is true of hMM neurons. This arrangement accounts for the mismatch responses of both cell types as a comparison of their sensory and locomotion responses, as expected for prediction error neurons. The dMM neurons had elevated resting membrane potential and spike threshold compared to hMM neurons, suggesting specialized electrophysiological ‘tuning’ to their functional roles.

The authors go on to record from L5/6 pyramidal neurons, showing that they have predominantly hyperpolarizing subthreshold mismatch responses. In other words, L5/6 neurons do not report prediction errors to downstream neurons. The L5/6 neurons have positive Vm correlations with locomotion and visual flow speed. So, instead of computing a prediction error response, they hyperpolarize either as a simple result of diminished visual input or through actively inhibitory input. This potential inhibition might arise from L5/6 interneurons that are excited by L2/3 dMM prediction error signals, as such connections can exist based on connectivity data4.

The authors end with a profound yet complex plot summarizing the differences between mismatch responses in L2/3 and L5/6 (their figure 6A,B, reprinted below). This key plot shows that mismatch responses in L2/3 are true prediction error responses because they arise from opposing signs of membrane potential correlation with locomotion speed vs visual flow speed. On the contrary, L5/6 hyperpolarizing mismatch responses are not prediction error responses because they arise from positive correlations of membrane potential with both locomotion and visual flow speeds (Figure 1).

Figure 1. Left, visuomotor mismatch responses in V1 L2/3, showing, for the first time, hyperpolarizing mismatch responses (hMM) as well as depolarizing subthreshold mismatch responses (dMM). Right top, correlation plots of membrane potential (Vm) with locomotion speed and visual flow speed in L2/3 and L5/6 color coded by the mismatch response of each neuron. Right bottom, schematic accounting for prediction error responses of L2/3 neurons. All panels taken with permission from Figure 2 and Figure 6 in Jordan et al 2010


Why I chose this preprint 

This study is important because it contains the first whole-cell recordings of mismatch responses from the Keller lab. In contrast to the previous Ca2+ imaging datasets, this new work shows subthreshold membrane potential responses in L2/3 neurons, as well as in L5/6 which has not been possible to reach with Ca2+ imaging. The findings show a good fit to what would be expected from prediction error computation in L2/3 pyramidal neurons. However, in L5/6 the neurons do not have the expected responses. Instead L5/6 pyramidal neurons fit the profile of representation neurons, where they would simply encode mouse speed through the world. Thus only L2/3 computes prediction errors. The work thereby demonstrates layer specific sensorimotor integration.



What next?

This work shows robust evidence for prediction error computation in V1 L2/3 and raises many interesting questions. Obviously the mechanisms generating the responses are of interest, and they are schematized by the authors in their Figure 6D. A previous study from the Keller lab concluded that somatostatin (SOM) and vasoactive intestinal peptide (VIP) interneurons are involved3. The delta oscillation seen in whole-cell traces during visual flow surely suggests involvement of a slowish excitatory/inhibitory oscillatory circuit somewhere in the mix. This could arise from burst firing of mutually inhibitory VIP and SOM interneuron populations. Top-down projections from the cingulate cortex are known to disinhibit V1 through primarily targeting VIP interneurons5. Previous work from the Keller lab indicates that this projection is also important for generating mismatch responses and has topographical organization on a gross level6. While it has not been quantified whether this projection is organized into finely clustered axonal fields in L1 of V1 (though Figure3A in Leinweber et al. 20176 suggests this may be the case), inputs from thalamus and secondary visual areas certainly are. Thalamic and secondary visual axons form an interleaved patchwork of ~60um diameter patches in V1, and these correspond to muscarinic receptor distributions and differences in interneuron circuitry7,8. Various other strands of evidence suggest existence of local interneuron teams at a similar spatial scale9. Might there be a topographical distribution of hyperpolarizing and depolarizing mismatch neurons associated with these patches?

How does V1 learn to generate these experience dependent responses?3 Are the responses ‘calibrated’ to match the visual flow conditions? For example, if a mouse were to move from the tall grass to arid plains, its visual flow might require retuning. For many eye movements, the cerebellum is thought to perform the role of a ‘teacher’, which is not required for the movement itself, but enables its tuning. Might loops between the cerebellum and prefrontal cortex, through thalamus and pontine nuclei10–12, also be involved in modifying V1 prediction error responses? It has been demonstrated that V1 SOM interneurons do not undergo marked plasticity while parvalbumin (PV) and neurogliaform neuropeptide Y (NPY) interneurons do1,3,13. PV and NPY interneurons inhibit SOM interneurons, pyramidal cells and VIP interneurons, which positions them as potential mediators of prediction error modifications.

How do V1 mismatch responses differ from those in other sensory cortices with similar circuitry, like somatosensory or auditory cortex? For example, is the hMM response in L5/6 somehow related to sensory suppression that occurs during saccades or self-generated sounds? The potential logic being that after reporting an observation, sensory representation should be suppressed during mismatch in order to prevent it from participating in further processing loops or modification.

Finally, how do the various mismatch neurons interact with each other? As the authors discuss, the predictive coding framework suggests the prediction error neurons and representative neurons would communicate to update predictions.1 As PV and NPY interneurons have strong, experience dependent mismatch responses3, perhaps dMMs drive them during mismatch causing hMM responses in L5/6. Would this inhibition be an update of the representation?



  1. Keller, G. B. & Mrsic-Flogel, T. D. Predictive Processing: A Canonical Cortical Computation. Neuron 100, 424–435 (2018).
  2. Keller, G. B., Bonhoeffer, T. & Hübener, M. Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron 74, 809–815 (2012).
  3. Attinger, A., Wang, B. & Keller, G. B. Visuomotor Coupling Shapes the Functional Development of Mouse Visual Cortex. Cell 169, 1291-1302.e14 (2017).
  4. Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462–aac9462 (2015).
  5. Zhang, S. et al. Selective attention. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660–5 (2014).
  6. Leinweber, M., Ward, D. R., Sobczak, J. M., Attinger, A. & Keller, G. B. A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions. Neuron 95, 1420-1432.e5 (2017).
  7. D’Souza, R. D., Bista, P., Meier, A. M., Ji, W. & Burkhalter, A. Spatial Clustering of Inhibition in Mouse Primary Visual Cortex. Neuron 104, 588-600.e5 (2019).
  8. Ji, W. et al. Modularity in the Organization of Mouse Primary Visual Cortex. Neuron 87, 632–643 (2015).
  9. Karnani, M. M. & Jackson, J. Interneuron Cooperativity in Cortical Circuits. Neuroscientist 24, 329–341 (2018).
  10. Gao, Z. et al. A cortico-cerebellar loop for motor planning. Nature 563, 113–116 (2018).
  11. Chabrol, F., Blot, A. & Mrsic-Flogel, T. D. Cerebellar contribution to preparatory activity in motor neocortex. bioRxiv 335703 (2018) doi:10.1101/335703.
  12. Proville, R. D. et al. Cerebellum involvement in cortical sensorimotor circuits for the control of voluntary movements. Nat. Neurosci. 17, 1233–9 (2014).
  13. Xue, M., Atallah, B. V. & Scanziani, M. Equalizing excitation–inhibition ratios across visual cortical neurons. Nature 511, 596–600 (2014).

Tags: in vivo patch clamp, mismatch, mouse, pyramidal neuron, v1, visual cortex, whole cell recording

Posted on: 7 April 2020


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Author's response

Rebecca Jordan shared

How does V1 learn to generate these experience dependent responses? Are the responses ‘calibrated’ to match the visual flow conditions?


Exactly how visuomotor errors are updated in cortex is a major question facing our research. We know that both the visuomotor relationships experienced during development, as well as those in recent history, affect the magnitude and specificity of mismatch responses (Attinger et al., 2017, Keller et al., 2012). A key feature of predictive coding computational models is that the firing of error neurons themselves mediates a change in prediction that acts to reduce firing of the error neuron. This minimization of error is potentially achievable within the local circuit, through a mechanism which acts to reduce the firing of the neuron. Given that our results suggest that excitation and inhibition from visual and locomotion-related sources are balanced in L2/3 pyramidal neurons, this plasticity could take the form of matching the influence of the two opposing kinds of input – something which imaging suggests to some degree (Attinger et al., 2017). Indeed, it has been known for some time that excitation and inhibition appear to be generally balanced in pyramidal neurons (Isaacson and Scanziani, 2011), indicating it is key to cortical function. Thus, there are likely several mechanisms for developing and maintaining this balance. In the case of visuomotor tuning, exactly which synapses undergo plasticity is unclear. Just considering dMM neurons, possible candidates hypothesized from the circuit diagram shown in Keller and Mrsic-Flogel, 2018 (Figure 2) include the synapse from SOM interneurons to pyramidal neurons (since SOM neurons themselves do not appear to undergo plasticity in their responses, at least at a population level), or the excitatory synapse from cingulate cortex (or other top-down sources) onto the pyramidal apical dendrite. However, it must be noted that these would be ‘non-classical’ forms of plasticity: when the dMM neuron fires, you would need an enhancement of inhibition from SOM neurons, and/or a reduction in excitation from cingulate axons (i.e. non-Hebbian plasticity). To my knowledge these specific types of plasticity have not yet been demonstrated in visual cortex. Certainly, more complex forms of circuit plasticity, as suggested by the author of this blog, could play a role. Indeed, some computational models hypothesize a major role for certain neuromodulators as teachers, broadcasting signals across cortex when the internal model of the world is wrong and enhancing plasticity (Sales et al., 2019).


How do V1 mismatch responses differ from those in other sensory cortices with similar circuitry, like somatosensory or auditory cortex?


Sensorimotor errors have recently been shown in auditory cortex (Schneider et al., 2014, 2018): here, when an animal was locomoting, a predictable auditory feedback was presented. During locomotion, the cortical circuit suppressed responses specifically to the predictable stimulus, while still firing to the same stimuli when unpredicted (during rest). In this scenario, error neurons with selectivity in only one direction seem to be present – i.e. neurons that only signal when there is more acoustic input than expected – analogous to hMM neurons in our paradigm – while neurons analogous for dMM neurons do not seem to be present. One reason for this may be the statistics of the different sensory modalities during development. In the visual world, errors of both kinds are relatively common: when the surrounding scenery is further away than previously, visual flow speed during movement will decrease for a given locomotion speed – i.e. less visual flow than expected, causing dMM neurons to fire. The opposite is true when the visual scenery is closer than it was before, leading to faster visual flow than predicted, necessitating the firing of hMM neurons. In the auditory world, errors of one type (more acoustic input than expected) may be statistically far more likely, especially for a mouse living in lab conditions. In somatosensory cortex, mismatches (sudden pauses) between texture speed and running speed do evoke responses in a subset (<20%) of L2/3 neurons (Ayaz et al., 2019). These were either activations or reductions in activity, although the responses of only those neurons activated by the mismatch (5% of neurons) were specific to the locomotion state (consistent with dMM neurons in visual cortex). Exactly why so few neurons responded to tactile mismatch when such a large portion of visual cortical L2/3 neurons are responsive to visuomotor mismatch (Keller et al., 2012), is unclear. Perhaps this could be to do with development of mice in a texture-poor home cage, leading to weak development of prediction in the circuit. It would be interesting to see whether the distribution of error neurons in sensory cortices shift to match the statistical likelihood of each kind of error during development.


How do the various mismatch neurons interact with each other?


Since we do not yet have markers for the individual types of mismatch neurons, we cannot yet do the necessary paired-patch slice studies needed to resolve their microcircuits. We can make some hypotheses, however. Mismatch neurons have a retinotopic receptive field (Zmarz and Keller, 2016), so it is possible that mismatch neurons of a given type (dMM or hMM) form ensembles that are specific for a particular region of retinotopic space, through reciprocal excitation or disinhibition. This could enhance the robustness of a given mismatch signal via consensus in the ensemble. We would not necessarily predict that dMM and hMM neurons mutually inhibit each other directly, even though they have opposing signals, as it is not clear that amplifying tiny errors, and reducing the linearity of the relationship between error size and firing rate would be beneficial. However, in the predictive processing framework (Keller and Mrsic-Flogel, 2018), layer 5 pyramidals act as representation neurons – neurons whose firing provide a best estimate of the current sensory environment. We would expect that dMM and hMM neurons have an opposing influence on these representation neurons: dMM neurons, signalling when there is less visual flow than predicted, should inhibit them, thereby shutting down the representation of visual flow. On the other hand, hMM neurons, signalling when there is more visual flow than predicted, should drive the representation neurons to enhance the representation of visual flow. These layer 5 representation neurons should feed back to the layer 2/3 error neurons, acting as a local prediction, i.e. driving dMM neurons and inhibiting hMM neurons. In this way dMM and hMM neurons could have indirect polysynaptic suppressive influence over each other. At this stage of our research, much of this remains conjecture.



Attinger, A., Wang, B., and Keller, G.B. (2017). Visuomotor Coupling Shapes the Functional Development of Mouse Visual Cortex. Cell 169, 1291-1302.e14.

Ayaz, A., Stäuble, A., Hamada, M., Wulf, M.A., Saleem, A.B., and Helmchen, F. (2019). Layer-specific integration of locomotion and sensory information in mouse barrel cortex. Nat. Commun. 10, 1–14.

Isaacson, J.S., and Scanziani, M. (2011). How inhibition shapes cortical activity. Neuron 72, 231–243.

Keller, G.B., and Mrsic-Flogel, T.D. (2018). Predictive Processing: A Canonical Cortical Computation. Neuron 100, 424–435.

Keller, G.B., Bonhoeffer, T., and Hübener, M. (2012). Sensorimotor mismatch signals in primary visual cortex of the behaving mouse. Neuron 74, 809–815.

Sales, A.C., Friston, K.J., Jones, M.W., Pickering, A.E., and Moran, R.J. (2019). Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: An Active Inference model. PLOS Comput. Biol. 15, e1006267.

Schneider, D.M., Nelson, A., and Mooney, R. (2014). A synaptic and circuit basis for corollary discharge in the auditory cortex. Nature 513, 189–194.

Schneider, D.M., Sundararajan, J., and Mooney, R. (2018). A cortical filter that learns to suppress the acoustic consequences of movement. Nature 561, 391–395.

Zmarz, P., and Keller, G.B. (2016). Mismatch Receptive Fields in Mouse Visual Cortex. Neuron 92, 766–772.


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