Laboratory of Neural Computation Istituto Italiano di Tecnologia Rovereto, 38068 Rovereto TN, Italy

Principal Investigator: Stefano Panzeri L-to-R: Jan Bím, Daniel Gutierrez, Arno Onken, Manuel Molano, Vito de Feo, Valeria D’Andrea, Anna Cattani, Demetrio Ferro, Giuseppe Pica, Eugenio Piasini, Arezoo

Alizadehkhajehiem and Alessandro Vato. Top L-to-R: Yann Zerlaut, Houman Safaai, Daniel Chicharro, Diego Fasoli and Stefano Panzeri,

Investigating population coding across cortex

Overall Goals

Representative Publications

Models of neural networks

Mathematical approaches to study spike timing

Neural coding and neuromodulation

Processing and analysis of 2-photon microscopy data

Understanding cortical slow oscillations

Runyan, Piasini, Panzeri, Harvey, Nature 2017

Auditory cortex

Posterior parietal cortex

Question: How does the brain

use sensory information that

vary on very short time scales

(ms) to make decisions over

much longer timescales (s)?

Conclusion: Different cortical

areas engaged by the same

behavioral task can display

distinct patterns of collective

neural activity, which enable

them to process information on

different temporal scales.

Goal: Decompose the information encoded in the temporal

structure of a spike train into the unique, complementary

information contained in its different temporal scale component.

Results: The Information Jitter Derivative (IJD) method provides

a way of identifying the non-redundant information contained in

each temporal scale.

Retinal Ganglion Cells in

the salamander retina

use different strategies to

encode the position and

the identity of the

presented image:

coarser spatial features

are encoded in coarser

temporal scales.

Pica, Piasini, Chicharro, Panzeri Entropy 2017

Relationship between neural codes and behavior

Question: Which sensory information carried by a neuron (or population of neurons) is read out by the brain to inform the animal

choices?

Conclusion: The Intersection Information framework helps combining statistics, neural recordings and behavior to establish

the link between stimulus information and the information used by the brain to make a decision.

Responses of individual neurons to identical repetitions of a sensory stimulus are highly variable.

However, the brain can process information and take decisions based on single events. How the brain

achieves such stable representation of sensory events even with noisy computing elements is a central,

and yet unaddressed, question in neuroscience.

Research carried out in the lab lies at the interface between theory and experiment and aims at

understanding the principles of cortical information processing by developing new quantitative data

analysis techniques based on the principles of Information Theory and by developing computational

models of neural network function.

Zucca, D’Urso, Pasquale, Vecchia, Pica, Bovetti, Moretti, Varani,

Molano-Mazón, Chiappalone, Panzeri and Fellin, eLife 2017

Question: How are slow oscillations generated in the cortex?

Conclusion: Activity of the two major subtypes of cortical interneurons, parvalbumin and somatostatin positive cells, causally

contribute to up-to-down state transitions.

Dynamical model

GABA

cells

Glu

Glu

cells

, ,J E I

dVf V I I

dt

Goal: Analyse the dynamics of neural networks of arbitrary

finite size.

Conclusion: Mesoscopic networks are able to regulate their

degree of functional heterogeneity, which is thought to help

reducing the detrimental effect of noise correlations on

cortical information processing.

Question: How do cortical responses originate from the interplay of the sensory drive that cortical neurons receive, the

spontaneous dynamics of cortex and the effect of neuromodulation?

Conclusions: The temporal structure of locus coeruleus burst firing regulates the amplitude and timing of changes in cortical

excitability and selectively amplifies responses to salient sensory stimuli.

B Safaai, Neves, Eschenko, Logothetis, Panzeri PNAS 2015

Collaborators

Panzeri, Harvey, Piasini, Latham, Fellin Neuron 2017

Pica, Piasini, Safaai, Runyan, Harvey, Diamond, Kayser, Fellin,

Panzeri NIPS 2017

Fasoli, Cattani, Panzeri Plos Comp. Biol. 2016

Christopher Harvey. Harvard Medical School.

Nikos Logothetis. Max Planck Institute for Biological Cybernetics.

Tommaso Fellin. Istituto Italiano di Tecnologia.

Christoph Kayser. University of Glasgow.

Alessandro Gozzi. Istituto Italiano di Tecnologia.

Alexander Thiele. Newcastle University.

Mathew Diamond. International School for Advanced Studies.

Tim Gollisch. Bernstein Center for Computational Neuroscience.

Davide Zoccolan. International School for Advanced Studies.

10 s

Goal: Develop a calcium imaging data processing pipeline that optimizes the tradeoff between signal quality and

experimental and computational time.

Results: 1) Constrained Nonnegative Matrix Factorization (Pnevmatikakis et al. 2016) allows spatially and temporaly demixing

the calcium imaging data and extracting the activity of individual cells. 2) The smart-Line Scan protocol allows recording the

activity of a population of neurons at significantly higher sampling rates.

Smart-Line

scan

Information flow among neural populations

(Ince et al. Cerebral Cortex 2016)

EEG dataset (Rousselet et al. Journal of Vision 2014)

Goal: Develop a new measure that quantifies

information transfer about a specific stimulus and

test its behavior with real experimental data.

Theoretical results: We developed a new

measure of information transfer about a

particular stimulus that works in scenarios

where previous methods fail.

We show that the measure works in cases where

there is a hidden common driver with the same

probability distribution as stimulus of interest.

EEG results: We applied the measure to an EEG

dataset where the subjects had to tell whether

an image contains a face or a random texture.

Using our new method we were able to quantify

the transfer of information coming from the left

eye between the right occipito-temporal sensor

(ROT) and the left occipito-temporal sensor

(LOT).

In collaboration with Christopher Harvey’s lab

In collaboration with Tim Gollisch’s lab

In collaboration with Tommaso Fellin’s lab

In collaboration with Tommaso Fellin’s lab

In collaboration with Nikos Logothetis’ lab

Besserve, Lowe, Logothetis, Scholkopf, Panzeri PLoS Biology 2015

Panzeri, Macke, Gross, Kayser Trends in Cogn. Sciences 2015

agreement number

699829 (“ETIC”)