Project

Back to overview

Dendritic pointers and time multiplexing as cortical binding mechanisms

English title Dendritic pointers and time multiplexing as cortical binding mechanisms
Applicant Senn Walter
Number 133094
Funding scheme Project funding (Div. I-III)
Research institution Institut für Physiologie Medizinische Fakultät Universität Bern
Institution of higher education University of Berne - BE
Main discipline Neurophysiology and Brain Research
Start/End 01.05.2011 - 31.10.2014
Approved amount 415'000.00
Show all

All Disciplines (4)

Discipline
Neurophysiology and Brain Research
Biophysics
Mathematics
Information Technology

Keywords (11)

Computational neuroscience; learning; memory; neural networks; reinforcement learning; working memory; biophysics; sequence learning; spiking neuron models; synaptic plasticity models; hippocampus

Lay Summary (English)

Lead
Lay summary
At any moment in time, a wealth of sensory information impinges on the brain where it is integrated with expectation related internal activity. How the brain integrates these information streams while still being able to keep sensory and expectation signals apart, represents a largely unsolved problem. The proposal explores through mathematical modeling and computer simulations how the binding of the two information streams can be accomplished without loosing their identity. (A) Dendritic pointers. Neurons in the primary visual cortex (V1) are known to extract local visual features such as oriented edges and motion. It is unclear, however, how motion and orientation selective neurons interact in V1. We hypothesise that motion represents a kind of local attention signal which modulates the gain of the surrounding orientation selective neurons in V1, in a similar way as an expectation signal modulates incoming sensory information. On the single cell level, the motion signal is assumed to transiently increase the gain of the V1 pyramidal neurons through dendritic calcium spikes. We investigate how this type of multiplicative interaction among motion and orientation selective neurons boosts the classification of visual objects within a blurry and jittered scene. The predictions are planned to be tested based on rat in vivo experiments performed by M. Larkum. (B) Time multiplexing. A second, independent part of the project investigates how internally generated activity patterns can be distinguished from externally generated signals when they converge to the same cortical network. Representing both the sensory and imagery stream within the same network has the advantage that learning acquired in the sensory mode also transfers to the imagery mode and vice verse. But this also bears the danger of hallucinating by confusing the two types of inputs. We investigate how the two information streams can be integrated in a single network while still keeping their identity. Although the different types of inputs are believed to project to different parts of a cortical pyramidal neuron, a readout neuron will not be able to distinguish the sources. We explore the idea that both signals are alternately represented in quick oscillations (say each 8 times per second), allowing the inputs to interact while they can still be told apart by reading the network activity at the right phase within each oscillation period. According to this idea, at each "moment" the time axis is subdivided ("multiplexed") into adjacent bins which represent an object in either the sensory or the imagery mode.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Publications

Publication
Scale-Free Navigational Planning by Neuronal Traveling Waves
Khajeh-Alijani Azadeh, Urbanzcik Robert, Senn Walter (2015), Scale-Free Navigational Planning by Neuronal Traveling Waves, in PLoS ONE, 10(7), 1-15.
Matching Recall and Storage in Sequence Learning with Spiking Neural Networks
Johanni Brea Walter Senn & Jean-Pascal Pfister (2013), Matching Recall and Storage in Sequence Learning with Spiking Neural Networks, in The Journal of Neuroscience, 33(23), 9565-9575.
Sequence learning with hidden units in spiking neural networks
J. Brea W. Senn and J.-P. Pfister (2012), Sequence learning with hidden units in spiking neural networks, in Advances in Neural Information Processing Systems NIPS, MIT Press, http://papers.nips.cc.
Spike-based Decision Learning of Nash Equilibria in Two-Player Games
Johannes Friedrich & Walter Senn (2012), Spike-based Decision Learning of Nash Equilibria in Two-Player Games, in PLoS Computational Biology, 8(9), 1-12.
Modulation of orientation-selective Neurons by motion: when additive, when multiplicative?
Torsten Lüdge Robert Urbanczik Walter Senn, Modulation of orientation-selective Neurons by motion: when additive, when multiplicative?, in Frontiers in Computational Neuroscience, 8(67), 1-12.

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
Computational and Systems Neuroscience (Cosyne) 2013 Poster Matching storage and recall in sequence learning 28.02.2013 Salt Lake City, Utah, USA, United States of America Brea Johanni; Senn Walter;
7th Clinical Neuroscience Meeting Poster Planning with traveling waves 04.12.2012 Bern, Switzerland Nygren Erik; Brea Johanni; Khajeh Alijani Azadeh; Senn Walter;
Conference of the Federation of European Neurosciences (FENS) 2011 Poster Learning temporal sequences in a network with hidden neurons 14.07.2012 Barcelona, Spain Brea Johanni;
NIPS Conference 2011 Poster Sequence learning with hidden units in spiking neural networks. 11.12.2011 Granada, Spain, Spain Brea Johanni;
6th Clinical Neuroscience Meeting Talk given at a conference Synaptic plasticity for learning temporal sequences 29.11.2011 Bern, Switzerland Senn Walter; Brea Johanni; Nygren Erik; Pfister Jean-Pascal; Khajeh Alijani Azadeh;


Communication with the public

Communication Title Media Place Year
Print (books, brochures, leaflets) Das egoistische Gehirn (UniPress Bern) German-speaking Switzerland 2013
New media (web, blogs, podcasts, news feeds etc.) Wie das Gehirn verschiedene Melodien lernen kann Medienmitteilung Universität Bern German-speaking Switzerland 2013

Awards

Title Year
Jean-Pascal Pfister received an SNF-ambiizione grant during the course of his employment (later also a SNF-Assistance professorship). 2011

Associated projects

Number Title Start Funding scheme
118084 Learning via top-down signaling in the neocortex 01.10.2007 Interdisciplinary projects
156863 Prospective coding with pyramidal neurons 01.12.2015 Project funding (Div. I-III)
156863 Prospective coding with pyramidal neurons 01.12.2015 Project funding (Div. I-III)
122697 State representation in reward based learning -- from spiking neuron models to psychophysics 01.01.2009 Sinergia
105966 Top-down gain modulation and learning across cortical areas 01.10.2004 Project funding (Div. I-III)
118084 Learning via top-down signaling in the neocortex 01.10.2007 Interdisciplinary projects
144941 Preparation visit for a Human Frontiers proposal writing 01.07.2012 International short research visits

Abstract

At any moment in time, various types of sensory information impinge on the brain, where it is integrated with internally generated activity. How these signal streams interact on a neuronal level in space and time, and how the brain is able to keep sensory and expectation signals apart, represents a largely unsolved problem. The proposal explores through mathematical modeling and computer simulations how such binding can be accomplished via dendritic gain modulation and time multiplexing, without loosing spatial and temporal feature identities. We investigate how sensory signals of different qualities can modulate neuronal firing, and how past, present and future events can be represented in the same neurons at specific time slots relative to a global background oscillation.The project splits into two subprojects, one for a PhD student and one for a postdoctoral student.(A) Dendritic pointers. The PhD project builds on our earlier work on task-dependent gain modulation of pyramidal neurons in the primary visual cortex (V1). We consider moving visual stimuli and hypothesize that these are analyzed based on a series of static images obtained through stroboscopic snap-shots. Motion information is integrated in V1 and in higher cortical areas and fed back to modulate the static image analysis. Unlike a global attentional signal, however, this modulation can specifically highlight the spots of the actual and expected motion. The motion signal is assumed to transiently modulate the gain of V1 pyramidal neurons through dendritic calcium spikes. We build a network model of V1 which is able to reconstruct edge orientations in moving images based on motion integration, although the motion signal itself only highlights edge location. We also formulate a mathematical framework in which we derive the type of interactions between motion and orientation selective V1 neurons which best serves contour enhancement. The predictions are planned to be tested based on rat in vivo experiments performed by M. Larkum.(B) Time multiplexing. The postdoc project will explore temporal binding mechanisms in the context of sequence learning and predicting distal future events. To bridge the temporal gap between past, present and future states of a sequence we consider the binding of these states in individual theta cycles (with period length of roughly 150ms). While a new stimulus drives the corresponding neuronal assembly at a certain phase within the theta cycle, past events are represented at earlier and expected future events at later phases. This leads to a time-compressed representation of sequences by nesting several gamma cycles (of period length 20ms) into one theta cycle, as has been observed in hippocampal place field recordings during navigation. We will extend these ideas and apply them to the prediction of non-spatial and non-Markovian stimulus sequences where the choice of a next stimulus may depend on a combination of previous stimuli. To prevent a temporal low-pass filtering of the stimulus information we suggest to multiply represent the same sequence across different areas, each endowed with its own phase shift. This allows us to predict close and distal events with the same precision. Predicting future events instead of future reward moreover supports a fast re-evaluation of putative decisions in a quickly changing context. We will evaluate the model based on in vivo recordings from delayed match-to-sample experiments planned by G. Rainer.
-