Linear Dynamics of Evidence Integration in Contextual Decision Making

Individual neurons in Prefrontal Cortex (PFC) exhibit a vast complexity in their responses. Central in Neuroscience is to understand how their collective activity underlies powerful computations responsible for higher order cognitive processes. In a recent study (Mante et al., 2013) two monkeys were trained to perform a contextual decision-making task, which required to selectively integrate the relevant evidence –either the color or the motion coherence of a random dots stimulus– and disregard the irrelevant one. A non-linear RNN trained to solve the same task found a solution that accounted for the selective integration computation, which could be understood by linearizing the dynamics of the network in each context. In this study, we took a different approach by explicitly fitting a Linear Dynamical System (LDS) model to the data from each context. We also fitted a novel jointly-factored linear model (JF), equivalent to the LDS but with no dynamical constraints and able to capture arbitrary patterns in time. Both models performed analogously, indicating that PFC data display systematic dynamics consistent with the LDS prior. Motion and color input signals were inferred and spanned independent subspaces. The input subspaces largely overlapped across contexts along dimensions that captured coherence and coherence magnitude related variance. The dynamics changed in each context so that relevant stimuli were strongly amplified. In one of the monkeys, however, the integrated color signal emerged via direct input modulation. The integration took place within subspaces spanned by multiple slow modes. These strongly overlapped along a single dimension across contexts, which was consistent with a globally identified decision axis. Interestingly, irrelevant inputs were not dynamically discarded, but were also integrated, although in a much lower extent. Finally, the model reproduced the main dynamical features of the population trajectories and accurately captured individual PSTHs. Our study suggests that a whole space of sensory-related input signals invariantly modulates PFC responses and that decision signals emerge as the inputs are shaped by a changing circuit dynamics. Our findings imply a novel mechanism by which sensory-related information is selected and integrated for contextual computations

Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics

The complex neural population activity of prefrontal cortex (PFC) is a hallmark of cognitive processes. How these rich dynamics emerge and support neural computations is largely unknown. Here, we infer mechanisms underlying the context-dependent selection and integration of sensory inputs by fitting dynamical models to PFC population responses of behaving monkeys. A class of models implementing linear dynamics driven by external inputs accurately captured the PFC responses within each context, achieving performance comparable to models without linear constraints. Two distinct mechanisms of input selection and integration were equally consistent with the data. One implemented context-dependent recurrent dynamics, as previously proposed, and relied on transient input amplification. The other relied on the subtle contextual modulation of the inputs, providing quantitative constraints on the attentional effects in sensory areas required to explain flexible PFC responses and behavior. Both mechanisms consistently revealed properties of inputs and recurrent dynamics missing in more simplified, incomplete descriptions of PFC responses. By revealing mechanisms consistent with rich cortical dynamics, our modeling approach provides a principled and general framework to link neural population activity and computation

Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics

The complex neural population activity of prefrontal cortex (PFC) is a hallmark of cognitive processes. How these rich dynamics emerge and support neural computations is largely unknown. Here, we infer mechanisms underlying the context-dependent selection and integration of sensory inputs by fitting dynamical models to PFC population responses of behaving monkeys. A class of models implementing linear dynamics driven by external inputs accurately captured the PFC responses within each context, achieving performance comparable to models without linear constraints. Two distinct mechanisms of input selection and integration were equally consistent with the data. One implemented context-dependent recurrent dynamics, as previously proposed, and relied on transient input amplification. The other relied on the subtle contextual modulation of the inputs, providing quantitative constraints on the attentional effects in sensory areas required to explain flexible PFC responses and behavior. Both mechanisms consistently revealed properties of inputs and recurrent dynamics missing in more simplified, incomplete descriptions of PFC responses. By revealing mechanisms consistent with rich cortical dynamics, our modeling approach provides a principled and general framework to link neural population activity and computation

Performance of synchrony and spectral-based features in early seizure detection: exploring feature combinations and effect of latency.

<p>Presentation and evaluation of a seizure detection algorithm at IWSP7, Melbourne, 3rd of August 2015</p> <p>Award: "Honourable Mention"</p> <p>Report available at: http://www.gatsby.ucl.ac.uk/~vincenta/kaggle/report.pdf</p

Behaviour-dependent recruitment of long-range projection neurons in somatosensory cortex

In the mammalian neocortex, segregated processing streams are thought to be important for forming sensory representations of the environment, but how local information in primary sensory cortex is transmitted to other distant cortical areas during behaviour is unclear. Here we show task-dependent activation of distinct, largely non-overlapping long-range projection neurons in the whisker region of primary somatosensory cortex (S1) in awake, behaving mice. Using two-photon calcium imaging, we monitored neuronal activity in anatomically identified S1 neurons projecting to secondary somatosensory (S2) or primary motor (M1) cortex in mice using their whiskers to perform a texture-discrimination task or a task that required them to detect the presence of an object at a certain location. Whisking-related cells were found among S2-projecting (S2P) but not M1-projecting (M1P) neurons. A higher fraction of S2P than M1P neurons showed touch-related responses during texture discrimination, whereas a higher fraction of M1P than S2P neurons showed touch-related responses during the detection task. In both tasks, S2P and M1P neurons could discriminate similarly between trials producing different behavioural decisions. However, in trials producing the same decision, S2P neurons performed better at discriminating texture, whereas M1P neurons were better at discriminating location. Sensory stimulus features alone were not sufficient to elicit these differences, suggesting that selective transmission of S1 information to S2 and M1 is driven by behaviour