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Neural circuit dynamics underlying expectation and their impact on the variability of perceptual choices

Periodic Reporting for period 2 - PRIORS (Neural circuit dynamics underlying expectation and their impact on the variability of perceptual choices)

Reporting period: 2018-03-01 to 2019-08-31

In this project we investigate the mechanisms underlying the perception of the environment. In particular we investigate the neural basis of how experience shapes our perception, or how expectations formed by our previous actions and their outcomes, are (1) built, (2) combined with sensory information and (3) processed to yield decisions. Expectations is an often overlooked topic in sensory systems neuroscience, in which the hierarchical circuitry starting from sensory receptors all the way up to the cortex has been finely characterized in various modalities (e.g. vision, audition, tactile, …). Current models of perceptual decision making assume that, during psychophysical tasks in which subjects need to discriminate ambiguous stimuli, a sensory representation formed in early cortical areas is integrated by associative areas in order to yield choices. The choice variability ubiquitous to all choices rising from stimuli at the perceptual threshold is thought to come from neuronal variability at the level of population of cortical sensory neurons. We hypothesize however, that a fraction of this variability might be caused by variability in the prior expectations formed by subjects based on previous decisions.

Understanding the neural mechanisms of decision making is a fundamental goal in neuroscience. Despite the advancement done in the last two decades there are still fundamental questions unresolved. How we weight evidence to take choices and learn from past experiences and adapt to a changing world is a central function of the brain which is affected in multiple psychiatric and neurological disorders such as schizophrenia, obsessive compulsive disorder, addictions, Parkinson disease, etc. Characterising the neural circuit mechanisms underlying the weighting of evidence and the learning from previous experiences will establish a conceptual framework under which we will be able to develop hypothesis for what goes wrong in each of these disorders and what kind of treatments may be effective.

The overall goal of this project is to characterize how we form perceptual expectations from our previous experiences, which are the brain areas involved in their computation and how these expectation are combined with sensory evidence in order to yield decisions. We will use both rodents and humans in a novel two-alternative forced choice task that promotes the development and exploitation of prior expectations. We will characterize choice dynamics using behavioral modeling and statistical learning techniques. We will combine these behavioral tasks with electrophysiological and manipulation of neuronal activity using optogenetics and pharmacology. Our findings will shed light onto expectations, an instrumental part of our perceptual experiences which may be altered in certain brain pathologies.
In the project we have covered the following activities along the lines described in the Description of Action (DoA).

Activity 1 : Development of a high-throughput Open source behavioral system for rodents.
In order to automatise and optimise the very time-consuming process of rodent training in cognitive tasks, we developed a high-throughput behavioral platform. As the platform is composed of many behavioral boxes, it was necessary to adjust the cost of the equipment and the software used to control each of these boxes, as well as to automatise the process in the way that the “bandwidth” of a single researcher can be maximized to supervise the training of many animals. The novel high-throughput platform for rodent training is composed of (1) PyBpod, an open software suited for the design and control of behavioural experiments, (2) custom made hardware aimed to (i) be fully compatible with Linux, (ii) minimize the cost and physical space of each box, (iii) maximize the temporal precision of sensors and actuators, the performed number of trials per session, and the reproducibility of the data.

Activity 2 : Rats behavioural characterization using a Reinforcement Learning statistical model of expectations [within Aim 1.1 of the DoM].
Using this high-throughput behavioral training set-up, we have performed many experiments using our nove perceptual discrimination two-alternative forced choice task (2AFC). Using these behavioral data we have finely characterized the across-trials dynamics of expectation build-up and its combination with auditory stimuli in order to yield choices. The results of this analysis and modeling of the behavior have been published in Hermoso-Mendizabal et al 2018.

Activity 3 : Permanent or reversible Inactivation of specific brain areas during the task [within Aim 1.2. Of the DoA]
We have performed numerous pharmacological and lesion studies in rats performing the 2AFC task. To explore the role of association cortices and in the striatum, we permanently or transiently abolished the activity of medial prefrontal cortex (mPFC), the medial posterior parietal cortex (PPC) and dorso-medial striatum (DMS). For the permanent-abolishing approach we performed bilateral neurotoxic lesions in the mPFC of 4 animals and tested the performance of the animals in the task after recovery. For the transiently-abolishing approach we bilateraly injected muscimol (a drug that inactivates the activity at the level of an entire neural circuit transiently) in mPFC, PPC or DMS in additional animals. The performance of the animals under the effect of this drug can be contrasted with the same animal’s performance injected with saline solution. We found no significant effects when inactivating mPFC or PPC. We did find however a significant reduction of the expectation choice bias when inactivating the DMS.
Activity 4 : Psychophysical Experiments to investigate the temporal integration of stimulus evidence [withing Aim 2.1. Of the DoA].
Before characterizing the integration of expectation and sensory stimulus, we have spent some time studying how subjects temporally integrate the sensory stream of information. The canonical models stimulus integration are based on what is called a diffusion process of a particle in a potential. The shape of this potential is what determines the integration dynamics of the system. We did psychophysical experiments in both humans and rats where we interleaved different magnitude of the stimulus fluctuations. This manipulation allowed us to test whether the perceptual decision relies more in the initial evidence of the stimulus (a Primacy effect) or in contrast is based on the late evidence (a Recency effect). The results of these experiments show that both humans (n=16) and rats (n=5) have very heterogeneous strategies to solve these tasks, ranging from Primacy to Recency. We also tested the impact of the stimulus fluctuation on the categorization accuracy as Double-Well attractor
1. We have developed a high-throughput open behavioral training platform with features that go beyond the state of the art: (1) following the Do it yourself (DIY) and open science philosophy we have designed behavioral boxes and acoustic isolation boxes that meet commercial standards for a much lower budget and most importantly that are just tailored to accommodate our needs. (2) they are all based on open-source software (Linux and Python) and all the codes are available (e.g. PyBPod). (3) they allow minimal human intervention during the training, reducing stress in animals and maximizing reproducibility of the data.

2. We have modelled the history biases of rats during a 2AFC task much beyond the state of the art. In particular we have identify different types of biases which are often confused, and set methods to isolate them and interpret them. We have been the first to separate the estimated probabilities animals may have about future events from the impact those predictions may have on future choices (i.e. in our case via chaice biases).

3. We have exploited the analysis of the movement of the animals during the task using novel published methods (e.g. DeepCut). We have been able to extract much more information from the behavior of the animals than standard methods based on nose pokes or tracking the overall position of the animal. For instance, we are now able to identify trials in which animals made changes of mind (CoM) by characterizing the fine orienting movements of their snouts towards one side port or the other. We are currently training deep convolutional networks to try to identify choice biases in the body position when they start a new trial (before taking the decision). Preliminary results show that animals “embody” many of these prediction strategies meaning that their body movements are different for instance when they are expecting a repeating stimulus from an alternating stimulus. We think that the combination of video analysis methods with the behavioral modeling and the electrophysiology will be a very powerful tool to understand the mechanisms underlying expectation and choice biases.

4. Our electrophysiology data from dorso-medial striatum has put forward the role of the basal ganglia in decision making and particularly in mediating the predictions and choice biases computed based on previous trials and maintained in memory during several trials in order to condition future choices. We have found several different types of neuronal population which, to our knowledge, had not been described. We are currently investigating the dynamics of these variables from a population perspective and will try to come up with network models that can subserve those computations observed in the DMS.

5. Our pharmacological results showing that medial Prefrontal Cortex (mPFC) and posterior parietal cortex (PPC)

The expected results for the second half of the project are:

1. Understand the extent to which the choice biases characterized so far are adaptive to our task. In oder words, why are animals adopting these expectation strategy? Can they modify the strategy in a context dependent manner? Can they adapt to more complex sequences (e.g. third order correlations in the stimulus sequence)?

2. Understand the urgency signals behind the timing of the responses in the reaction-time version of our task. This will require both behavioral controls (e.g. remove the perceptual decision and make just a timing task) and analysis of the electrophysiological data we have from basal ganglia and premotor areas (e.g. M2 area).

3. Be able to train Recurrent Neural Networks to solve out expectation-based 2AFC task and investigate the circuit mechanism that the trained RNNs use to form and maintain expectations as well as to combine them with the ambiguous sensory stimulus.

4. Perform optogenetics inactivation of DMS and mPFC during brief periods of the task to see how the different biases can be affected.

5. Be ab