UH Biocomputation Group - Maria Psarrou

Functional Neurology of a Brain System: A 3D Olfactory Bulb Model to Process Natural Odorants

2023-05-04T11:00:52+01:00

This week on Journal Club session Maria Psarrou will talk about a paper "Functional Neurology of a Brain System: A 3D Olfactory Bulb Model to Process Natural Odorants".

The network of interactions between mitral and granule cells in the olfactory bulb is a critical step in the processing of odor information underlying the neural basis of smell perception. We are building the first computational model in 3 dimensions of this network in order to analyze the rules for connectivity and function within it. , The initial results indicate that this network can be modeled to simulate experimental results on the activation of the olfactory bulb by natural odorants, providing a much more powerful approach for 3D simulation of brain neurons and microcircuits.

Papers:

M. Migliore, F. Cavarretta, M. Hines, G. Shepherd, "Functional Neurology of a Brain System: A 3D Olfactory Bulb Model to Process Natural Odorants", 2013 -10- 17, Functional Neurology, 28, 241--243

Date: 2023/05/05
Time: 14:00
Location: online

Manipulating Synthetic Optogenetic Odors Reveals the Coding Logic of Olfactory Perception

2022-02-02T17:57:48+00:00

This week on Journal Club session Maria Psarrou will talk about a paper "Manipulating Synthetic Optogenetic Odors Reveals the Coding Logic of Olfactory Perception".

How does neural activity generate perception? Finding the combinations of spatial or temporal activityfeatures (such as neuron identity or latency) that are consequential for perception remains challenging. We trained mice to recognize synthetic odors constructed from parametrically defined patterns ofoptogenetic activation, then measured perceptual changes during extensive and controlled perturbationsacross spatiotemporal dimensions. We modeled recognition as the matching of patterns to learnedtemplates. The templates that best predicted recognition were sequences of spatially identified units,ordered by latencies relative to each other (with minimal effects of sniff). Within templates, individualunits contributed additively, with larger contributions from earlier-activated units. Our syntheticapproach reveals the fundamental logic of the olfactory code and provides a general framework fortesting links between sensory activity and perception.

Papers:

E. Chong, M. Moroni, C. Wilson, S. Shoham, S. Panzeri, D. Rinberg, "Manipulating Synthetic Optogenetic Odors Reveals the Coding Logic of Olfactory Perception", 2020, Science, 368, eaba2357

Date: 2022/02/04
Time: 14:00
Location: online

Fast Odour Dynamics Are Encoded in the Olfactory System and Guide Behaviour

2021-05-06T11:21:00+01:00

This week on Journal Club session Maria Psarrou will talk about a paper "Fast Odour Dynamics Are Encoded in the Olfactory System and Guide Behaviour".

Odours are transported in turbulent plumes, which result in rapid concentration fluctuations that contain rich information about the olfactory scenery, such as the composition and location of an odour source. However, it is unclear whether the mammalian olfactory system can use the underlying temporal structure to extract information about the environment. Here we show that ten-millisecond odour pulse patterns produce distinct responses in olfactory receptor neurons. In operant conditioning experiments, mice discriminated temporal correlations of rapidly fluctuating odours at frequencies of up to 40 Hz. In imaging and electrophysiological recordings, such correlation information could be readily extracted from the activity of mitral and tufted cells the output neurons of the olfactory bulb. Furthermore, temporal correlation of odour concentrations reliably predicted whether odorants emerged from the same or different sources in naturalistic environments with complex airflow. Experiments in which mice were trained on such tasks and probed using synthetic correlated stimuli at different frequencies suggest that mice can use the temporal structure of odours to extract information about space. Thus, the mammalian olfactory system has access to unexpectedly fast temporal features in odour stimuli. This endows animals with the capacity to overcome key behavioural challenges such as odour source separation, figure ground segregation and odour localization by extracting information about space from temporal odour dynamics.

Papers:

T. Ackels, A. Erskine, D. Dasgupta, A. Marin, T. Warner, S. Tootoonian, I. Fukunaga, J. Harris, A. Schaefer, "Fast Odour Dynamics Are Encoded in the Olfactory System and Guide Behaviour", 2021, Nature

Date: 2021/05/06
Time: 14:00
Location: online

A comparison of deterministic and stochastic ion channel representations in a model of a cerebellar nucleus neuron

2018-05-21T14:55:04+01:00

Ion channels can either be modelled at a macroscopic level, using a deterministic representation such as the Hodgkin-Huxley formalism, or at a more detailed single-channel level, where their stochastic nature is taken into account by using a Markov formalism. The Hodgkin-Huxley model describes the combined collective effect of the channel population on the membrane potential, but it does not provide a comprehensive kinetic diagram. As a result, various aspects of the behaviour and consequently the functional role of individual channels can be overlooked. On the other hand, a more accurate alternative channel formalism is the Markov model. In Markov models, a single channel is represented by a kinetic scheme comprising a finite set of discrete intermediate states with probabilistic transitions from one state to another. Channel noise, introduced by the stochastic gating of the ion channels, can affect the generation and timing of action potentials and therefore potentially also single neuron computations.

In the present study, the voltage-gated channels of a morphologically realistic conductance based cerebellar nucleus (CN) neuron model were expressed as Markov formalisms and their behaviour was compared with their deterministic Hodgkin-Huxley type counterparts. Our results show that the majority of the deterministic CN channel models could easily be replaced by stochastic versions, without affecting neuronal behaviour. However, this was not the case for the fast sodium channel, where the parameter changes that had to be introduced in order to match the activity of the stochastic and deterministic models depended on the level of activation of the neuron, even for very small single channel conductances in the stochastic model.

Date: 25/05/2018
Time: 16:00
Location: LB252

An introduction to NEURON

2017-05-11T10:05:33+01:00

Biological computational modelling is a powerful tool to simulate a system and draw conclusions regarding its function. It also allows to make predictions for processes that still haven’t been investigated in the laboratory. NEURON [1, 2] is an simulation environment, where empirical data are combined with analytic mathematical expressions, in order to model single neurons or neural networks. Neuronal cells are created as a series of connected sections, able to form realistic morphologies, and where different membrane properties (ionic, synaptic and passive) can be inserted. The interface and programming syntax are designed to offer an intuitive environment and emphasise on the biological functions in detail, rather than the the programming or numerical methods. The purpose of this workshop is to give an introduction to the NEURON software and how it could be used, by building a simple neuronal cell model and testing its behaviour under different conditions.

Carnevale, N.T. and Hines, M.L. The NEURON Book. Cambridge, UK: Cambridge University Press, 2006.
NEURON for empirically-based simulations of neurons and networks of neurons (2017). [online] Available at: https://www.neuron.yale.edu/

Date: 12/05/2017
Time: 16:00
Location: LB252

Multiplicative gain changes are induced by excitation or inhibition alone

2016-12-15T13:18:29+00:00

Maria Psarrou's journal club session where she presents the paper, "Multiplicative gain changes are induced by excitation or inhibition alone (Murphy, B. K., & Miller, K. D. (2003))".

We model the effects of excitation and inhibition on the gain of cortical neurons. Previous theoretical work has concluded that excitation or inhibition alone will not cause a multiplicative gain change in the curve of firing rate versus input current. However, such gain changes in vivo are measured in the curve of firing rate versus stimulus parameter. We find that when this curve is considered, and when the nonlinear relationships between stimulus parameter and input current and between input current and firing rate in vivo are taken into account, then simple excitation or inhibition alone can induce a multiplicative gain change. In particular, the power-law relationship between voltage and firing rate that is induced by neuronal noise is critical to this result. This suggests an unexpectedly simple mechanism that may underlie the gain modulations commonly observed in cortex. More generally, it suggests that a smaller input will multiplicatively modulate the gain of a larger one when both converge on a common cortical target.

Date: 16/12/2016
Time: 16:00
Location: LB252

Gain modulation from background synaptic input

2016-01-27T17:22:15+00:00

Gain modulation is a prominent feature of neuronal activity recorded in behaving animals, but the mechanism by which it occurs is unknown. By introducing a barrage of excitatory and inhibitory synaptic conductances that mimics conditions encountered in vivo into pyramidal neurons in slices of rat somatosensory cortex, we show that the gain of a neuronal response to excitatory drive can be modulated by varying the level of "background" synaptic input. Simultaneously increasing both excitatory and inhibitory background firing rates in a balanced manner results in a divisive gain modulation of the neuronal response without appreciable signal-independent increases in firing rate or spike-train variability. These results suggest that, within active cortical circuits, the overall level of synaptic input to a neuron acts as a gain control signal that modulates responsiveness to excitatory drive.

The complete paper can be found here: http://www.sciencedirect.com/science/article/pii/S0896627302008206

Date: 29/01/2016
Time: 16:00
Location: LB252