Lukas Solanka PhD

Lukas Solanka


Biography

In broader terms, I am interested in the dynamics of activity in biologically plausible models of neural networks, related to storing and integrating sensory (or in general any kind of) information in animals.

For my PhD. I am working on a biophysically plausible models of grid cells. These cells have an intricate property of their firing fields being confined to specific positions of an animal's environment. Moreover, the firing pattern of a single cell repeats, forming a two dimensional, regular triangular grid. In the project, I am building on a hypothesis that an animal can integrate self-motion cues during its movement, and by using mechanism called path integration, can update a neural representation of its relative position, reflected in the firing of a grid cell. I want to investigate properties of spiking attractor networks in this context, their stability conditions in reproducing the grid cell properties in simulations, and the precision of path integration algorithm.

Publications:
2016
  Continuous attractor network models of grid cell firing based on excitatory-inhibitory interactions
Shipston-Sharman, O, Solanka, L & Nolan, M 2016, 'Continuous attractor network models of grid cell firing based on excitatory-inhibitory interactions' Journal of Physiology. DOI: 10.1113/JP270630
Neurons in the medial entorhinal cortex encode location through spatial firing fields that have a grid-like organisation. The challenge of identifying mechanisms for grid firing has been addressed through experimental and theoretical investigations of medial entorhinal circuits. Here, we discuss evidence for continuous attractor network models that account for grid firing by synaptic interactions between excitatory and inhibitory cells. These models assume that grid-like firing patterns are the result of computation of location from velocity inputs, with additional spatial input required to oppose drift in the attractor state. We focus on properties of continuous attractor networks that are revealed by explicitly considering excitatory and inhibitory neurons, their connectivity and their membrane potential dynamics. Models at this level of detail can account for theta-nested gamma oscillations as well as grid firing, predict spatial firing of interneurons as well as excitatory cells, show how gamma oscillations can be modulated independently from spatial computations, reveal critical roles for neuronal noise, and demonstrate that only a subset of excitatory cells in a network need have grid-like firing fields. Evaluating experimental data against predictions from detailed network models will be important for establishing the mechanisms mediating grid firing.
General Information
Organisations: Edinburgh Neuroscience.
Authors: Shipston-Sharman, Oliver , Solanka, Lukas & Nolan, Matthew.
Publication Date: 24 Jan 2016
Publication Information
Category: Article
Journal: Journal of Physiology
ISSN: 0022-3751
Original Language: English
DOIs: 10.1113/JP270630
2015
  Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks
Solanka, L, van Rossum, MCW & Nolan, MF 2015, 'Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks' eLIFE, vol 4. DOI: 10.7554/eLife.06444

Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength.


General Information
Organisations: Edinburgh Neuroscience.
Authors: Solanka, Lukas, van Rossum, Mark C W & Nolan, Matthew F.
Keywords: (attractor networks, cognition, entorhinal cortex, inhibition, oscillation, synapse. )
Number of pages: 75
Publication Date: 6 Jul 2015
Publication Information
Category: Article
Journal: eLIFE
Volume: 4
ISSN: 2050-084X
Original Language: English
DOIs: 10.7554/eLife.06444
2013
  Feedback Inhibition Enables Theta-Nested Gamma Oscillations and Grid Firing Fields
Pastoll, H, Solanka, L, van Rossum, MCW & Nolan, MF 2013, 'Feedback Inhibition Enables Theta-Nested Gamma Oscillations and Grid Firing Fields' Neuron, vol 77, no. 1, pp. 141-154. DOI: 10.1016/j.neuron.2012.11.032
Cortical circuits are thought to multiplex firing rate codes with temporal codes that rely on oscillatory network activity, but the circuit mechanisms that combine these coding schemes are unclear. We establish with optogenetic activation of layer II of the medial entorhinal cortex that theta frequency drive to this circuit is sufficient to generate nested gamma frequency oscillations in synaptic activity. These nested gamma oscillations closely resemble activity during spatial exploration, are generated by local feedback inhibition without recurrent excitation, and have clock-like features suitable as reference signals for multiplexing temporal codes within rate-coded grid firing fields. In network models deduced from our data, feedback inhibition supports coexistence of theta-nested gamma oscillations with attractor states that generate grid firing fields. These results indicate that grid cells communicate primarily via inhibitory interneurons. This circuit mechanism enables multiplexing of oscillation-based temporal codes with rate-coded attractor states.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Pastoll, Hugh, Solanka, Lukas, van Rossum, Mark C. W. & Nolan, Matthew F..
Keywords: (, , . )
Number of pages: 14
Pages: 141-154
Publication Date: 9 Jan 2013
Publication Information
Category: Article
Journal: Neuron
Volume: 77
Issue number: 1
ISSN: 0896-6273
Original Language: English
DOIs: 10.1016/j.neuron.2012.11.032
  Co-existence of theta-nested gamma oscillations and grid firing fields in an attractor network model
Solanka, L, Pastoll, H, Nolan, M & Van Rossum, MCW 2013, 'Co-existence of theta-nested gamma oscillations and grid firing fields in an attractor network model' Society for Neuroscience Annual Meeting 2013, San Diego, United States, 9/11/13 - 13/11/13, .
Grid cells in the medial entorhinal cortex (MEC) encode location through firing fields that form grid-like maps of the environment. At the same time network activity in the MEC is dominated by oscillations in the theta (4-12 Hz) and gamma (30-100 Hz) bands. The relationship between oscillatory activity and grid firing is not known. Our recent experimental data established that feedback inhibition between excitatory stellate cells and inhibitory fast spiking interneurons dominates the synaptic connectivity in layer II of the MEC. To determine if this circuit is sufficient to explain both the network oscillations and grid firing fields, we constructed a network of model stellate cells and interneurons. In this model, stellate cells connect exclusively to interneurons, while interneurons contact only stellate cells. We show that external excitatory conductances can drive the network into an attractor state. Feedback inhibition onto the model stellate cells has a synchronizing effect. Synaptic input to both populations of neurons is synchronized in the gamma frequency range (30 - 100 Hz). When coupled with a theta modulated external drive (8 Hz), we observed network synchronization during the trough of the theta signal only. When the borders of the network are connected with a twisted torus topology (Guanella et al. 2007) and velocity modulated inputs are applied to the circuit, excitatory neurons in the circuit generate grid-like firing fields. Due to limited stability of the attractor, the grid fields were blurred. However, it was possible to obtain stable grid fields by including a place cell resetting mechanism as an additional input to the network (Guanella et al. 2007). These results demonstrated that the same local circuit architecture - feedback inhibition - supports emergence of attractor states and nested gamma oscillations. This has implications for mechanisms of both formation of grid-like receptive fields as well as population coding and information transmission between brain areas.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Solanka, Lukas, Pastoll, Hugh, Nolan, Matthew & Van Rossum, Mark C. W..
Keywords: (#dtc-001. )
Publication Date: 2013
Publication Information
Category: Poster
Original Language: English
2012
  Co-existence of theta-nested gamma oscillations and grid firing fields in an attractor network model
Solanka, L, Pastoll, H, Nolan, M & van Rossum, MCW 2012, 'Co-existence of theta-nested gamma oscillations and grid firing fields in an attractor network model' Physiology 2012, Edinburgh, United Kingdom, 2/07/12 - 5/07/12, .
Grid cells in the medial entorhinal cortex (MEC) encode location through firing fields that form grid-like maps of the environment. At the same time network activity in the MEC is dominated by oscillations in the theta (4-12 Hz) and gamma (30-100 Hz) bands. The relationship between oscillatory activity and grid firing is not known. Our recent experimental data establishes that feedback inhibition between excitatory stellate cells and inhibitory fast spiking interneurons is the dominant form of synaptic connectivity within layer II of the MEC. To determine if this synaptic architecture is sufficient to explain network oscillations or grid firing fields, we constructed a network of model stellate cells and interneurons. The model contains 4096 excitatory stellate cells and 1024 inhibitory interneurons. In this model, stellate cells connect exclusively to interneurons, while interneurons contact only stellate cells. We show that external excitatory conductances can drive the network into an attractor state. Feedback inhibition onto the model stellate cells has a synchronising effect. The activity of both populations discharged in the gamma frequency range (30 - 100 Hz), and when coupled with a theta modulated external drive (8 Hz), we observed the synchronisation during the trough of the theta signal only. When the borders of the network are connected with a twisted torus topology and velocity modulated inputs are applied to the circuit, excitatory neurons in the circuit generate grid-like firing fields. Due to only a partial stability of the attractor state the grid fields appeared noisy. These results have implications for mechanisms of both formation of grid-like receptive fields and also population coding and information transmission between brain areas.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Solanka, Lukas, Pastoll, Hugh, Nolan, Matthew & van Rossum, Mark C. W..
Publication Date: 2012
Publication Information
Category: Poster
Original Language: English
  Multiplexing of theta-nested gamma oscillations and grid firing fields in an attractor network model of layer II of the medial entorhinal cortex
Solanka, L, Pastoll, H, Nolan, MF & Van Rossum, MCW 2012, 'Multiplexing of theta-nested gamma oscillations and grid firing fields in an attractor network model of layer II of the medial entorhinal cortex' Frontiers in Computational Neuroscience, no. 252. DOI: 10.3389/conf.fncom.2012.55.00252
Grid cells in the medial entorhinal cortex (MEC) encode location through firing fields that form grid-like maps of the environment. At the same time network activity in the MEC is dominated by oscillations in the theta (4-12 Hz) and gamma (30-100 Hz) bands. The relationship between oscillatory activity and grid firing is not known. Our recent experimental data established that feedback inhibition between excitatory stellate cells and inhibitory fast spiking interneurons dominates the synaptic connectivity in layer II of the MEC. To determine if this circuit is sufficient to explain both the network oscillations and grid firing fields, we constructed a network of model stellate cells and interneurons. In this model, stellate cells connect exclusively to interneurons, while interneurons contact only stellate cells. We show that external excitatory conductances can drive the network into an attractor state. Feedback inhibition onto the model stellate cells has a synchronizing effect. Synaptic input to both populations of neurons is synchronized in the gamma frequency range (30 - 100 Hz). When coupled with a theta modulated external drive (8 Hz), we observed network synchronization during the trough of the theta signal only. When the borders of the network are connected with a twisted torus topology (Guanella et al. 2007) and velocity modulated inputs are applied to the circuit, excitatory neurons in the circuit generate grid-like firing fields. Due to limited stability of the attractor, the grid fields were blurred. However, it was possible to obtain stable grid fields by including a place cell resetting mechanism as an additional input to the network (Guanella et al. 2007). These results demonstrated that the same local circuit architecture - feedback inhibition - supports emergence of attractor states and nested gamma oscillations. This has implications for mechanisms of both formation of grid-like receptive fields as well as population coding and information transmission between brain areas.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Solanka, Lukas, Pastoll, Hugh, Nolan, Matthew F & Van Rossum, Mark C W.
Keywords: (#dtc-001. )
Publication Date: 2012
Publication Information
Category: Meeting abstract
Journal: Frontiers in Computational Neuroscience
Issue number: 252
ISSN: 1662-5188
Original Language: English
DOIs: 10.3389/conf.fncom.2012.55.00252
2011
  Stability of two dimensional spiking attractor network models of grid cells
Solanka, L, Van Rossum, M & Nolan, M 2011, 'Stability of two dimensional spiking attractor network models of grid cells' UK Neuroinformatics: From Computational Models to Engineering and Cognition, Manchester, United Kingdom, 29/11/11 - 30/11/11, .
Stellate cells in rat medial entorhinal cortex (MEC) are important for the neural representation of location [1]. As an animal moves through its environment, they fire in locations that together form a periodic lattice, or grid, of activity. Each vertex of the lattice is surrounded by a hexagon of other vertices. The periodic response remains stable even in the absence of visual and olfactory cues [1]. Such coding provides a representation of space within the area an animal can explore [2].
While the precise mechanisms of information processing in the MEC are not known, it has been suggested that in order for the spatial position of the rat to be updated in the grid cell network, information about the rat's movement must be processed from the head direction input system [3]. Path integration, the integration of linear and angular self motion of the rat, could potentially be sufficient to account for the grid cell response. The existence of head direction cells and speed modulated responses of grid and head direction cells support this idea [3].
General Information
Organisations: Centre for Integrative Physiology.
Authors: Solanka, Lukas, Van Rossum, Mark & Nolan, Matthew.
Publication Date: 2011
Publication Information
Category: Poster
Original Language: English

Projects:
Neural network dynamics of oscillatory activity and spatial memory formation (PhD)