Churchland lab

Today’s commentary brought to you from (in alphabetical order): Lital Chartarifsky, Anne Churchland, Ashley Juavinett, Farzaneh Najafi, Anne Urai, & Sashank Pisupati. Feel free to comment, correct, express skepticism, etc. You can do so here, on Biorxiv or on Twitter (@anne_churchland). Let’s get a conversation going!

Paper #1: Psychophysical reverse correlation reflects both sensory and decision-making processes (Okazawa, She, Purcell & Kiani)

Big question: Psychophysical kernels are a powerful method to derive the spatiotemporal filter that transforms sensory information into a decision. However, can psychophysical kernels be interpreted as reflecting such sensory weighting profiles when measures in realistic decision-making scenarios?

Summary: First, tasks with a fixed stimulus duration cannot correctly retrieve sensory filtering timecourses, since the temporal weighting function may just as well reflect the process of bound-crossing during evidence accumulation (and the experimenter doesn’t have access to the time of the decision). Second, variable non-decision time (even in a reaction time task) results in decaying kernels. The authors then demonstrate different ways to draw informative conclusions from psychophysical kernels. First, they compare kernels in a motion direction discrimination RT task to explicit predictions derived from the DDM, and show that kernel shape can be predicted by a DDM with stationary sensory weights. The investigate a range of models that all have stationary sensory weights, and show (Figure 7) that these can generate a diversity of kernel dynamics.

Take home: Be very careful when interpreting psychophysical kernels as reflecting purely sensory weights!

Paper #2: Cortical neural activity predicts sensory acuity under optogenetic manipulation. John J. Briguglio, Mark Aizenberg, Vijay Balasubramanian, Maria N. Geffen (Note that its now in J. Neurosci).

Big question: Why does stimulation (optical, chemical, electrical) cause idiosyncratic changes in behavior, sometimes in opposing directions?

Take home: Behavioral variability occurs because the changes to neurons are also variable. In this paper, the authors showed that changes in psychophysical threshold following A1 optogenetic stimulation were variable and that this variability could be understood if one took into account the change in the neurometric threshold at the site of stimulation.To estimate neurometric threshold, the authors recorded neural activity at the same sites where they stimulated and they measured neurometric threshold using Fisher information. To estimate behavioral threshold, the authors used a pre-pulse inhibition task in which an auditory tone, if the animal’s heard it, could ward off a startle reflex in response to a loud white noise burst.

Skeptics’ corner: Changes in psychometric functions were related to changes in neurometric functions (cool!), but I was left wondering why the changes in neurometric functions were idiosyncratic. The direction of the change in neurometric threshold was idiosyncratic across sites, and even across stimulation methods. In other words, the same kind of stimulation (e.g., ChR2 stimulation in pyramidal neurons) sometimes made both neurometric threshold go up, and other times made it go down.

Paper #3: Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex Petr Znamenskiy, Mean-Hwan Kim, Dylan R. Muir, Maria Florencia Iacaruso, Sonja B. Hofer, and Thomas D. Mrsic-Flogel

Question: Do inhibitory neurons connect broadly to all their nearby excitatory neurons? Or is there specific connectivity in the connection of inhibitory to excitatory neurons?

Take home message: Inhibitory neurons connect more strongly to nearby excitatory neurons with similar responses to visual stimulation, suggesting that connections between inhibitory and excitatory neurons are organized under a similar rule to excitatory-excitatory connections. In more detail, although inhibitory neurons are less tuned to visual stimuli than excitatory neurons, their response selectivity is not merely a reflection of their surrounding neurons: inhibitory neurons selectivity out-performs that of their surrounding neurons. This is due to their selective connectivity to excitatory neurons with similar tuning properties.

Skeptics’ corner: If there is selective connectivity between excitatory and inhibitory neurons, why are inhibitory neurons still less tuned? 2) Do the same conclusions apply to other subtypes of inhibitory neurons?

Paper #4: Stable representation of sounds in the posterior striatum during flexible auditory decisions (Guo, Walker, Ponvert, Penix, Jaramillo)

Big question: What is the role of posterior striatum during auditory-driven decisions in mice?

Take home message: Posterior striatum (also known as “auditory striatum”) is causal in an auditory discrimination task.Bilateral muscimol inactivation of this area impaired performance, while unilateral optogenetic activation during sound presentation biased the animals’ choices contralaterally. The authors also showed that the activity of neurons in posterior striatum reliably encoded stimulus features, but were minimally influenced by the animals’ choices, suggesting that neurons in the posterior striatum provide sensory information downstream, while providing little information about behavioral choice.

Skeptics’ corner: The result showing impaired performance after bilateral muscimol inactivations was averaged across 4 sessions, however the authors note that on individual sessions the mouse was idiosyncratically biased to either the left or the right side. This side bias was probably caused by unbalanced muscimol injection. This is something that we should be mindful of when interpreting performance after bilateral manipulations.

Paper #5: Limitations of proposed signatures of Bayesian confidence
(William T. Adler, Wei Ji Ma)

Big question: A bayesian model of confidence was previously proposed by Hangya et. al, in which confidence reflects the subject’s estimate of posterior probability of the chosen option. Do the proposed signatures of bayesian confidence generalize?

Take home: Proposed signatures of bayesian confidence (i.e. divergence of mean confidence as a function of stimulus magnitude on correct and error trials, mean confidence of 0.75 on uninformative trials) are not necessary if the category-conditioned stimulus distributions are overlapping, especially in certain noise regimes, and yet others can be predicted by non-bayesian models. Hence favor model comparison over signatures!

Skeptics’ corner: The authors mention that an alternate model of confidence, the distance of an observation from the category boundary, can account for some of the signatures. However the question remains whether the Bayesian model makes unique predictions that distinguish it from alternatives, for instance predicted effects of changing subjects’ priors. Enumerating such unique predictions would help in directly testing the model experimentally, and ease the burden on model comparison.