Publications

Publications

Many models of cognition and of neural computations posit the use and estimation of prior stimulus statistics: it has long been known that working memory and perception are strongly impacted by previous sensory experience, even when that sensory history is not relevant to the current task at hand. Nevertheless, the neural mechanisms and regions of the brain that are necessary for computing and using such prior experience are unknown. Here we report that the posterior parietal cortex (PPC) is a critical locus for the representation and use of prior stimulus information. We trained rats in an auditory parametric working memory task, and found that they displayed substantial and readily quantifiable behavioural effects of sensory-stimulus history, similar to those observed in humans and monkeys. Earlier proposals that the PPC supports working memory predict that optogenetic silencing of this region would impair behaviour in our working memory task. Contrary to this prediction, we found that silencing the PPC significantly improved performance. Quantitative analyses of behaviour revealed that this improvement was due to the selective reduction of the effects of prior sensory stimuli. Electrophysiological recordings showed that PPC neurons carried far more information about the sensory stimuli of previous trials than about the stimuli of the current trial. Furthermore, for a given rat, the more information about previous trial sensory history in the neural firing rates of the PPC, the greater the behavioural effect of sensory history, suggesting a tight link between behaviour and PPC representations of stimulus history. Our results indicate that the PPC is a central component in the processing of sensory-stimulus history, and could enable further neurobiological investigation of long-standing questions regarding how perception and working memory are affected by prior sensory information.

Planning can be defined as action selection that leverages an internal model of the outcomes likely to follow each possible action. Its neural mechanisms remain poorly understood. Here we adapt recent advances from human research for rats, presenting for the first time an animal task that produces many trials of planned behavior per session, making multitrial rodent experimental tools available to study planning. We use part of this toolkit to address a perennially controversial issue in planning: the role of the dorsal hippocampus. Although prospective hippocampal representations have been proposed to support planning, intact planning in animals with damaged hippocampi has been repeatedly observed. Combining formal algorithmic behavioral analysis with muscimol inactivation, we provide causal evidence directly linking dorsal hippocampus with planning behavior. Our results and methods open the door to new and more detailed investigations of the neural mechanisms of planning in the hippocampus and throughout the brain.

Previous work from the lab has shown that perturbing the FOF during decision-making tasks produces a difficulty-independent bias. We sought to investigate what computational role for the FOF could produce such a bias. We found that when a simple two-node attractor network was asked to accumulate evidence or categorize already accumulated evidence into decisions, they could not fit the experimental data well. However, when the network was asked to remember an already formed decision, the networks could match the data very well. This network in a “post-categorization” role naturally accounts for a variety of other data from the lab about FOF tuning curves, as well as behavioral responses to fast timescale inactivations, reinforcing the idea that the FOF is more involved in maintaining the memory of a decision than in forming the decision itself.

Ben and Christine recorded from frontal and parietal cortex using two-photon calcium imaging techniques while rats performed a pulse-based accumulation of evidence task.  They found that each pulse of evidence triggered a wave of activity that slowly spread through the cortex. These waves continued to propagate throughout the cortex until the choice was made. Although the response of each individual neuron was relatively brief, together they contained enough information to accurately predict the number of light pulses that the animal saw. The activity may reflect a strategy to construct memories that are stable over time but that can also be easily modified as new information becomes available.

Many models of decision-making process include an “evidence accumulation” stage: evidence for or against different options is gradually accumulated over time, and it is the result of that accumulation process that drives the final decision. In these models (including work from our own lab), it is usually assumed that noise from inputs at different timepoints is independent of each other, in which case the total variance would increase linearly with the number of inputs. But is that true? Using a visual flashes accumulation task, we found that the data was better described by a model in which variance increased as the square of the number of inputs. In other words, the standard deviation scales linearly with the number of inputs, similar to “scalar variability” models used for numerical and time interval estimation. This result held both for freely moving rats and for head-restrained rats; the latter suggests the phenomenon can be investigated using cellular-resolution two-photon microscopy.

Short-term memory is an essential higher cognitive function, allowing us to mentally store information, and thereby separate sensory input from motor output. Guided by previous neural recordings conducted in the rat FOF by Jeff Erlich et al. 2011 during a simple memory guided orienting task, we sought to test the hypothesis that activity in the rat FOF is causally maintaining the rat’s choice for its upcoming motor act. Surprisingly, as information encoded in the FOF increased, the ability to perturb the rat’s choice by disrupting that information decreased. We conducted the same experiments on the deep motor layers of the superior colliculus (SC) and found nearly identical results.   In order to reconcile these seemingly contradictory optogenetic and electrophysiological results we adapted a simple mutually inhibiting dynamical attractor network model. At the start of a trial the network resides on the unstable hill separating two basins of attraction, at this point there is little information encoded, but unilateral inactivation of either the FOF or SC can easily push the network off the hill into one of the attractors, biasing the decision. Later, during the memory delay period, when the network resides in one of the attractors, there is a large amount of information encoded, but now the same perturbation is insufficient to push the network out of the attractor. These results provide the first direct causal test that short-term memory is maintained by a network distributed across cortical and subcortical regions and is well characterized by attractor dynamics.

Executive control, defined by flexible sensorimotor remapping in response to changing environmental demands, is a remarkable feature of adaptive behavior, and is predominantly studied in primates and putatively mediated by the prefrontal cortex (PFC). To study executive control in the rat, graduating student Ann Duan, together with Jeff and Carlos, developed a novel behavior in which subjects are cued, on each trial, to apply a sensorimotor association to orient either toward a visual target (“Pro”) or away from it (“Anti”). Multiple behavioral asymmetries suggest that Anti behavior is cognitively demanding while Pro is easier to learn and perform. This is consistent with a prominent hypothesis in the primate literature that Anti requires prefrontal cortex (PFC), whereas Pro could be mediated by midbrain superior colliculus (SC). First author Ann Duan directly tested this hypothesis via reversible inactivation of the PFC or SC during task performance. Pharmacological inactivation of rat medial PFC supported its expected role in Anti. Remarkably, bilateral SC inactivation substantially impaired Anti while leaving Pro essentially intact. Moreover, SC inactivation eliminated the performance cost of switching from Anti to Pro tasks. Our results establish a rodent model of single-trial sensorimotor remapping and suggest a critical role for SC in the cognitively demanding Anti task.

Numerous brain regions have been shown to have neural correlates of gradually accumulating evidence for decision-making, but the causal roles of these regions in decisions driven by accumulation of evidence have yet to be determined. Here, in rats performing an auditory evidence accumulation task, we inactivated the frontal orienting fields (FOF) and posterior parietal cortex (PPC), two rat cortical regions that have neural correlates of accumulating evidence and that have been proposed as central to decision-making. We used a detailed model of the decision process to analyze the effect of inactivations. Inactivation of the FOF induced substantial performance impairments that were quantitatively best described as an impairment in the output pathway of an evidence accumulator with a long integration time constant (>240 ms). In contrast, we found a minimal role for PPC in decisions guided by accumulating auditory evidence, even while finding a strong role for PPC in internally-guided decisions.

Tim Hanks and Chuck Kopec took our temporal accumulation of auditory evidence decision task (“Poisson Clicks”, Brunton et al. 2013), and used it to investigate encoding of accumulating evidence in posterior parietal cortex (PPC) and frontal orienting fields (FOF) of rats. The paper develops a new method to estimate tuning curves for accumulating evidence, allowing us to plot firing rate as a function of value of the accumulated evidence. This method revealed that while PPC encoded accumulating evidence in a graded manner, the FOF did so in a more binary-like categorical manner, more consistent with encoding response selection (“if the GO signal came now, which of the two options does the accumulated evidence tell me I should choose?”) than with encoding the gradually accumulating evidence itself.

Information about response selection needs to be read out, and will affect behavior, only around the time of the GO signal. Such an encoding thus predicts that perturbing the FOF should have an effect only if the perturbation occurs at the time of the GO signal. High time-resolution optogenetic inactivation of the FOF confirmed this prediction, supporting the idea that the FOF is part of transforming accumulated evidence into a categorical choice, but is not part of the gradually accumulating evidence process itself.

Ben Scott, a postdoc in the Brody and Tank labs, developed a system for voluntary head-fixation in rats. Based on the principles of kinematic mounts often used in optics, the system allows precise re-positioning of a rat’s head, across multiple trials of a behavior, with an accuracy of a few microns. This enables cellular-resolution calcium imaging in behaving rats. In addition, because the rats engage the system voluntarily, the approach is amenable to high-throughput training. Thanks to Ben’s work, we can now use the lab’s training facility to teach voluntarily head-fixing rats to perform complex cognitive behaviors requiring many months to train. We are using this is to perform the first cellular-resolution imaging assays of neural activity involved in higher cognitive processes.

Where do imperfections in decision-making come from? And can rats perform gradual accumulation of evidence for decision-making, like primates do? This paper describes  a decision-making task that is particularly well-suited to detailed quantitative modeling and analysis. Using this task together with a trial-by-trial model of the behavior, first-author Bing Brunton showed that rats, like humans, can gradually accumulate evidence for decision-making, and that their evidence accumulator is optimal in the sense of being noiseless and lossless. Thus imperfections in decision-making all came from imperfections in sensory processing, none from the evidence accumulation process.The modeling and analysis methods Bing developed are the most statistically powerful approach to date for characterizing properties of decision-making processes. The approach can be readily applied to other decision-making tasks and modalities, and provides a moment-by-moment, trial-by-trial estimate of the subject’s internal estimate of the accumulating evidence.

Nature published a News and Views piece on our paper.

Do rats have a brain region homologous to the primate Frontal Eye Fields (FEF)? We trained rats to perform memory-guided orienting, and used that to investigate the functional role of the rat’s Frontal Orienting Fields (FOF). Memory-guided orienting is a classic task in which a transient sensory cue tells you in which direction you should orient, but you must withhold your motion over a delay period, until a “go” cue arrives. (You must therefore keep either the sensory cue or your intended motion in memory during the delay period.) First-author Jeff Erlich recorded in the Frontal Orienting Fields (FOF), a rat cortical area that has been suggested as homologous to the primate FEF. Just as in the primate FEF, he found persistent neural activity during the delay period that predicts the subject’s subsequent orienting motion. Again as in the primate FEF, Jeff found that pharmacological inactivation of the FOF impairs memory-guided orienting markedly more than it impairs sensory-guided orienting. Our data support the rat FOF / primate FEF homology.  Full Description.

Which brain areas are involved in time discrimination? Using the same behavioral protocol and stimuli, rats were trained to either discriminate high versus low tones, or discriminate long versus short durations. First-author Shraddha Pai then lesioned either the auditory cortex (ACx) or the medial prefontal cortex (mPFC). ACx lesions led to a clear impairment in frequency discrimination, but no impairment in the matched duration discrimination task. mPFC lesions led to no detectable impairment in either task. The data suggests that time discrimination may bypass both ACx and mPFC.

Quantifying the location and/or number of features in a histological section of the brain currently requires one to first, manually register a corresponding section from a tissue atlas onto the experimental section and second, count the features. No automated method exists for the first process (registering), and most automated methods for the second process (feature counting) operate reliably only in a high signal-to-noise regime. To reduce experimenter bias and inconsistencies and increase the speed of these analyses, we developed Atlas Fitter, a semi-automated, open-source MatLab-based software package that assists in rapidly registering atlas panels onto histological sections. We also developed CellCounter, a novel fully automated cell counting algorithm that is designed to operate on images with non-uniform background intensities and low signal-to-noise ratios.

How do populations of neurons multiplex different types of information? Recordings in prefrontal cortices typically show neurons that have responses that depend on multiple aspects of the task being performed, and that are highly heterogeneous: although each neuron is reliable, no two neurons are alike. Faced with datasets of recordings of hundreds of such neurons, people often first use principal components analysis (PCA), so as to look at a reduced number of dimensions that captures most of the variance. But PCA doesn’t orient its axes in order to clearly distinguish task components. In this paper, we describe a method to automatically find the orientation of the PC axes that best distinguish between the components of the task we’re interested in. This allows a particularly clear and intelligible view of the data.

 

[this section on older papers needs filling in]

 

  • Machens, C.K., R. Romo, and C.D. Brody. 2005. Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307: 1121–1124.
  • Hopfield, J.J., and C.D. Brody. 2004. Learning rules and network repair in spike-timing-based computation networks. Proc. Natl. Acad. Sci. USA 101: 337–342.
  • Brody, C.D., and J.J. Hopfield. 2003. Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron 37: 843–852.
  • Hopfield, J.J., and C.D. Brody. 2001. What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. Proc. Natl. Acad. Sci. USA 98: 1282–1287.
  • Romo, R., C.D. Brody, A. Hernandez, and L. Lemus. 1999. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399: 470–473.