organisation - the Instituto Gulbenkian de Ciência

LEARNING
LABORATORY
THIS GROUP IS A MEMBER OF THE CHAMPALIMAUD NEUROSCIENCE PROGRAMME AT THE IGC
Joseph Paton Research Fellow
PhD in Neurobiology and Behaviour, Columbia University, 2008
Research Fellow at the IGC since 2008
link to external website
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.
GROUP MEMBERS
Gonçalo Lopes (PhD student, shared with Adam Kampff)
Gustavo Mello (PhD student)
Rui Azevedo (PhD student, shared with Zach Mainen))
Sofia Soares (Technician)
COLLABORATORS
Joshua T. Dudman (Janelia Farm Research Campus, USA)
Adam R. Kampff (Champalimaud Neuroscience Programme, Portugal)
FUNDING
Champalimaud Foundation (CF),Portugal
Fundação para a Ciência e a Tecnologia (FCT), Portugal
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 behaviour.
In the past year Rui Azevedo has activated dopamine neurons using optogenetics
in brain slices, and in behaving mice. He gained behavioural evidence
of successful activation by showing that he could condition mice to prefer a
particular spatial location by illuminating transfected neurons specifically when
mice entered a particular region. He is currently training transgenic mice on a
timing task, and will test whether manipulation of dopamine neuron activity
affects interval timing.
NEUROPHYSIOLOGY OF TIME ENCODING IN THE RODENT STRIATUM
Lesion, pharmacology, and genetic studies all suggest that the ability to estimate
the passage of time on the scale of seconds to minutes is produced in
the striatum, a major input area of the basal ganglia. Thus, we trained rats to
estimate time intervals and recorded from striatal neurons as they behaved
and asked how the passage of time could be encoded in the firing patterns we
observed. In addition, the basal ganglia is thought to implement reinforcement
learning mechanisms, helping the animal learn how to act in response to a given
situation based on past experience. We sought to place the neural signals we
recorded into a computational framework that reconciles interval timing and
reinforcement learning. Towards that end, we are developing a computational
model of interval timing that includes signals related to those we observe experimentally,
but that also can solve reinforcement learning problems.
We currently have a manuscript in the final stages of preparation describing the
neural signals we observe during an interval timing task. We are also actively
extending these studies to gain more continuous measures of the animals’ behaviour
during our task. This will be important for continuing to rule out behavioural
sources of variance in the firing of neurons we record.
Modelling rats’ behaviour using mechanisms derived from experimental data. Output
of a timing model running on a task that we have previously trained rats to
perform. At the top are scalable temporal basis functions that resemble the activity
profiles of neurons we have recorded in the striatum of rats during this task.
These are used as rate functions to produce Poisson spike trains. These spike
trains are then decoded to estimate time (blue trace) and subsequently drive behaviour
(red threshold).
IGC ANNUAL REPORT ‘11
RESEARCH FELLOWS
79