Epileptic micronetworks and their clinical relevance
A core aspect of clinical epileptology revolves around relating epileptic field potentials to underlying neural sources (e.g. an “epileptogenic focus”). Yet still, how neural population activity relates to epileptic field potentials and ultimately clinical phenomenology, remains far from being understood. After a brief overview on this topic, this seminar will focus on unpublished work, with an emphasis on seizure-related focal spreading depression. The presented results will include hippocampal and neocortical chronic in vivo two-photon population imaging and local field potential recordings of epileptic micronetworks in mice, in the context of viral encephalitis or optogenetic stimulation. The findings are corroborated by invasive depth electrode recordings (macroelectrodes and BF microwires) in epilepsy patients during pre-surgical evaluation. The presented work carries general implications for clinical epileptology, and basic epilepsy research.
Neural circuit redundancy, stability, and variability in developmental brain disorders
Despite the consistency of symptoms at the cognitive level, we now know that brain disorders like Autism and Schizophrenia can each arise from mutations in >100 different genes. Presumably there is a convergence of “symptoms” at the level of neural circuits in diagnosed individuals. In this talk I will argue that redundancy in neural circuit parameters implies that we should take a circuit-function rather that circuit-component approach to understanding these disorders. Then I will present our recent empirical work testing a circuit-function theory for Autism: the idea that neural circuits show excess trial-to-trial variability in response to sensory stimuli, and instability in the representations across a timescale of days. For this we analysed in vivo neural population activity data recorded from somatosensory cortex of mouse models of Fragile-X syndrome, a disorder related to autism. Work with Beatriz Mizusaki (Univ of Bristol), Nazim Kourdougli, Anand Suresh, and Carlos Portera-Cailliau (Univ of California, Los Angeles).
Cortical circuits for olfactory navigation
Olfactory navigation is essential for the survival of living beings from unicellular organisms to mammals. In the wild, rodents combine odor information with an internal spatial representation of the environment for foraging and navigation. What are the neural circuits in the brain that implement these behaviours? My research addresses this question by examining the synaptic circuits and neural population activity in the olfactory cortex to understand the integration of olfactory and spatial information. Primary olfactory (piriform) cortex (PCx) has long been recognized as a highly associative brain structure. What is the behavioural and functional role of these associative synapses in PCx? We designed an odor-cued navigation task, where rats must use both olfactory and spatial information to obtain water rewards. We recorded from populations of posterior piriform cortex (pPCx) neurons during behaviour and found that individual neurons were not only odor-selective, but also fired differentially to the same odor sampled at different locations, forming an “olfactory place map”. Spatial locations can be decoded from simultaneously recorded pPCx population, and spatial selectivity is maintained in the absence of odors, across behavioural contexts. This novel olfactory place map is consistent with our finding for a dominant role of associative excitatory synapses in shaping PCx representations, and suggest a role for PCx spatial representations in supporting olfactory navigation. This work not only provides insight into the neural basis for how odors can be used for navigation, but also reveals PCx as a prime site for addressing the general question of how sensory information is anchored within memory systems and combined with cognitive maps to guide flexible behaviour.
Decoding of Chemical Information from Populations of Olfactory Neurons
Information is represented in the brain by the coordinated activity of populations of neurons. Recent large-scale neural recording methods in combination with machine learning algorithms are helping understand how sensory processing and cognition emerge from neural population activity. This talk will explore the most popular machine learning methods used to gather meaningful low-dimensional representations from higher-dimensional neural recordings. To illustrate the potential of these approaches, Pedro will present his research in which chemical information is decoded from the olfactory system of the mouse for technological applications. Pedro and co-researchers have successfully extracted odor identity and concentration from olfactory receptor neuron low-dimensional activity trajectories. They have further developed a novel method to identify a shared latent space that allowed decoding of odor information across animals.