The work of this thesis concerns how cortical memories are stored and retrieved. In particular, large-scale simulations are used to investigate the extent to which associative attractor theory is compliant with known physiology and in vivo dynamics. The first question we ask is whether dynamical attractors can be stored in a network with realistic connectivity and activity levels. Using estimates of biological connectivity we demonstrated that attractor memories can be stored and retrieved in biologically realistic networks, operating on psychophysical timescales and displaying firing rate patterns similar to in vivo layer 2/3 cells. This was achieved in the presence of additional complexity such as synaptic depression and cellular adaptati...
Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation ...
A signature feature of cortical spike trains is their trial-to-trial variability. This variability i...
We investigated the dynamics of activity in feedback neural network models at low firing rates. The ...
The work of this thesis concerns how cortical memories are stored and retrieved. In particular, larg...
A fundamental problem in neuroscience is understanding how working memory—the ability to store infor...
The persistent and graded activity often observed in cortical circuits is sometimes seen as a signat...
The persistent and graded activity often observed in cortical circuits is some-times seen as a signa...
AbstractNested oscillations, where the phase of the underlying slow rhythm modulates the power of fa...
The theories of early brain scientists like Hebb and v. Hayek were in many ways analogous to modern ...
Cortical neurons are predominantly excitatory and highly interconnected. In spite of this, the corte...
INTRODUCTION Autoassociative attractor neural networks 1,2 provide a powerful paradigm for the st...
The notion of attractor networks is the leading hypothesis for how associative memories are stored a...
The notion of attractor networks is the leading hypothesis for how associative memories are stored a...
Memory is a fundamental part of computational systems like the human brain. Theoretical models ident...
Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation ...
Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation ...
A signature feature of cortical spike trains is their trial-to-trial variability. This variability i...
We investigated the dynamics of activity in feedback neural network models at low firing rates. The ...
The work of this thesis concerns how cortical memories are stored and retrieved. In particular, larg...
A fundamental problem in neuroscience is understanding how working memory—the ability to store infor...
The persistent and graded activity often observed in cortical circuits is sometimes seen as a signat...
The persistent and graded activity often observed in cortical circuits is some-times seen as a signa...
AbstractNested oscillations, where the phase of the underlying slow rhythm modulates the power of fa...
The theories of early brain scientists like Hebb and v. Hayek were in many ways analogous to modern ...
Cortical neurons are predominantly excitatory and highly interconnected. In spite of this, the corte...
INTRODUCTION Autoassociative attractor neural networks 1,2 provide a powerful paradigm for the st...
The notion of attractor networks is the leading hypothesis for how associative memories are stored a...
The notion of attractor networks is the leading hypothesis for how associative memories are stored a...
Memory is a fundamental part of computational systems like the human brain. Theoretical models ident...
Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation ...
Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation ...
A signature feature of cortical spike trains is their trial-to-trial variability. This variability i...
We investigated the dynamics of activity in feedback neural network models at low firing rates. The ...