Learning to adaptively respond to cues in the environment that predict behaviourally relevant events is critical for survival. However, in the natural world, where animals are exposed to myriad sensory stimuli, learning the predictive value of cues is non-trivial. How do animals figure out which cues are predictive, and of what? This is called the credit assignment problem. Conceiving of this problem as statistical inference in the time domain offers a parsimonious account of animals’ learning abilities. In other words, when cues occur relative to meaningful events is what determines their information content, their usefulness, and thus, whether they warrant learning about. However, we still do not understand how the brain might keep track of times. We aim to reveal neural mechanisms for time by observing and manipulating neurophysiology in behaving rodents performing tasks that lead them to estimate intervals.
LearningPaton Lab firstname.lastname@example.org
Neural encoding of timing in the rat striatum
The striatum forms a major input structure for the basal ganglia, and is a site of degeneration in diseases such as Parkinson’s and Huntington’s disease. It receives input broadly from cortex and thalamus, as well as from neuromodulatory systems, and funnels the resulting activity into a successively decreasing number of neurons before transmitting its output to the thalamus. This architecture is thought the facilitate selection of representations of actions, events, thoughts and relationships between them, from its various input patterns. Interestingly, perturbing normal function there through lesions or pharmacology can cause deficits in timing behavior, and, depending on site and type of manipulation, different aspects of learning. By parametrically varying a time interval that a rat estimates, while simultaneously recording action potentials from single neurons in the striatum, we aim to identify neurons whose activity is correlated with a change in estimated interval. The signals we identify will provide a starting point for modeling efforts as well as perturbation studies, wherein specific neural signals can be manipulated. We have begun by recording from neurons in the striatum of the rat. We will test the hypothesis that some of these neurons will shift their response rate and/or latency as we shift the time interval that rats estimate. By identifying neurons with such “tuning” for a temporal interval, we hope to identify a neural substrate for the time-based computations that may underlie learning.
In the past year, we have succeeded in training rats to press a lever in order to gain rewards at defined intervals, classically called operant conditioning on a Fixed Interval (FI) reinforcement schedule. We have adapted the classical FI schedule such that in blocks of 30 trials, on average, we shift the fixed interval over a range of about one minute. We call this schedule a Serial Fixed Interval (SFI) schedule of reinforcement and have analyzed rats’ behavior and initiated neurophysiological recordings during this task. Rats normally start responding just after the midpoint of the reinforcement interval, peaking around the time of reinforcement. We then shift the interval of reinforcement in blocks of trials to a different interval and animals shift the time at which they begin to respond, thus giving us a behavioural readout that reflects the animals changing knowledge about time until reward. Animals learn the interval associated with each block quickly, usually adapting their response times within five or fewer cycles of reward. This allows us to test animals on as many as ten distinct intervals that vary in duration over a range of about one minute during single sessions. This wide range of variation in estimated interval gives us statistical power when searching for neural correlates of timing behavior.
Optogenetic investigation of interval timing in mice
In the past year, we have initiated a parallel set of timing studies in mice in order to take advantage the increased molecular power of the mouse relative to the rat. We have trained mice on a classic temporal reproduction task, called the peak interval task, and are currently training mice on the SFI task mentioned above. By combining viruses dependent on CRE recombinase activity for expression of transgenes, with mouse lines expressing CRE in specific basal ganglia cell types, we plan to express light sensitive channels and pumps in targeted locations within the basal ganglia circuit. Stimulating these proteins with light during experiments will provide us with two potentially powerful pieces of data. First, we will be able to ask what type of cell we are recording from in vivo much more easily and in higher volume than was available with older techniques. Second, we can test hypotheses about the role of activity in specific populations of neurons for timing behavior.
Paton JJ; Buonomano DV (2018) The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions Neuron (doi:10.1016/j.neuron.2018.03.045)
Burgess CP, Lak A, Steinmetz NA, Zatka-Haas P, Bai Reddy C, Jacobs EAK, Linden JF, Paton JJ, Ranson A, Schröder S, Soares S, Wells MJ, Wool LE, Harris KD, Carandini M. (2017) High-Yield Methods for Accurate Two-Alternative Visual Psychophysics in Head-Fixed Mice Cell Rep (doi:10.1016/j.celrep.2017.08.047)
Saez RA, Saez A, Paton JJ, Lau B, Salzman CD (2017) Distinct Roles for the Amygdala and Orbitofrontal Cortex in Representing the Relative Amount of Expected Reward Neuron 95 (1), 70-77 (doi:http://dx.doi.org/10.1016/j.neuron.2017.06.012)
Brian Lau, Tiago Monteiro, Joseph J Paton (2017) The many worlds hypothesis of dopamine prediction error: implications of a parallel circuit architecture in the basal ganglia Curr. Opin. Neurobiol. 46 , 241-247 (doi:10.1016/j.conb.2017.08.015)
Soares S, Atallah BV, Paton JJ. (2016) Midbrain dopamine neurons control judgment of time Science 354 ((6317)), 1273-1277 (doi:10.1126/science.aah5234)
Gouvêa* TS, Monteiro* T, Motiwala A, Soares S, Machens CK, Paton JJ (2015) Striatal dynamics explain duration judgments eLife 4 (e11386), [Epub ahead of print] (doi:10.7554/eLife.11386)
Mello GB, Soares S, Paton JJ. (2015) A Scalable Population Code for Time in the Striatum Curr. Biol. S0960-9822 (15), 00205-5 (doi:10.1016/j.cub.2015.02.036)
Lopes G, Bonacchi N, Frazão J, Neto JP, Atallah BV, Soares S, Moreira L, Matias S, Itskov PM, Correia PA, Medina RE, Calcaterra L, Dreosti E, Paton JJ, Kampff AR (2015) Bonsai: An event-based framework for processing and controlling data streams Front. Neuroinform. 9 (7) (doi:10.3389/fninf.2015.00007)
Paton JJ, Lau B (2015) Tread softly and carry a clock's tick Nat. Neurosci. (18), 329–330 (doi:10.1038/nn.3959)
Gomez-Marin A, Paton JJ, Kampff AR, Costa RM, Mainen ZF. (2014) Big behavioral data: psychology, ethology and the foundations of neuroscience Nat. Neurosci. (17), 1455–1462 (doi: 10.1038/nn.3812.)
Gouvêa, T.S., Monteiro, T., Soares, S., Atallah, B.V., Paton, J.J. (2014) Ongoing behavior predicts perceptual report of interval duration Front. Neurorobot. 8 (doi:10.3389/fnbot.2014.00010)
Zhang W, Schneider DM, Belova MA, Morrison SE, Paton JJ, Salzman CD. (2013) Functional Circuits and Anatomical Distribution of Response Properties in the Primate Amygdala J. Neurosci. 33 (2), 722-733 (doi:10.1523/JNEUROSCI.2970-12.2013)
Joseph J Paton, Kenway Louie (2012) Reward and punishment illuminated Nat. Neurosci. 15 (6), 807-809 (doi:10.1038/nn.3122)
Belova MA, Paton JJ, Salzman CD. (2008) Moment-to-moment tracking of state value in the amygdala. J. Neurosci. 28 (40), 10023-30 (doi:10.1523/JNEUROSCI.1400-08.2008)
Salzman CD, Paton JJ, Belova MA, Morrison SE. (2007) Flexible neural representations of value in the primate brain. Ann. N. Y. Acad. Sci. 1121 , 336-54 (doi:10.1196/annals.1401.034)
Belova MA, Paton JJ, Morrison SE, Salzman CD. (2007) Expectation modulates neural responses to pleasant and aversive stimuli in primate amygdala. Neuron 55 (6), 970-84 (doi:10.1016/j.neuron.2007.08.004)
Paton JJ, Belova MA, Morrison SE, Salzman CD. (2006) The primate amygdala represents the positive and negative value of visual stimuli during learning. Nature 439 , 865-870 (doi:10.1038/nature04490)
Salzman CD, Belova MA, Paton JJ (2005) Beetles, boxes and brain cells: neural mechanisms underlying valuation and learning. Curr. Opin. Neurobiol. 15 (6), 721-9 (doi:10.1016/j.conb.2005.10.016,)