Attractor Dynamics in Realistic Hippocampal Networks

Menschik, Elliot D.; Yen, Shih-Cheng; Finkel, Leif H.

doi:10.1007/978-1-4615-4831-7_77

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…The attractor states of this biological network directly correspond to those in a traditional Hopfield network constructed with a similar synaptic matrix (22). Similar results are found in larger networks, and with a range of different model neurons.…”

Section: Memory As a Dynamical Attractor Statesupporting

confidence: 72%

Neuroengineering Models of Brain Disease

Finkel

2000

Annu. Rev. Biomed. Eng.

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The techniques of computational simulation have begun to be applied to modeling neurological disease and mental illness. Such neuroengineering models provide a conceptual bridge between molecular/cellular pathology and cognitive performance. We consider models of Alzheimer's disease, Parkinson's disease, and schizophrenia. Each of these diseases involves a disorder of neuromodulation coupled with underlying neuronal pathology. Parallels arising between these models suggests that a common set of computational mechanisms may account for functional loss across a spectrum of brain diseases. In particular, we focus on attractor-based network dynamics and how they arise from neural architectures, on mechanisms for linking sequences of attractor states and their role in cognition, and on the role of neuromodulation in controlling these processes. These studies suggest new approaches to understanding the forebrain circuits underlying cognition, and point toward a new tool for dissecting the pathophysiology of brain disease.

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Section: Memory As a Dynamical Attractor Statesupporting

confidence: 72%

Neuroengineering Models of Brain Disease

Finkel

2000

Annu. Rev. Biomed. Eng.

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“…For example, it is thought that brain rhythms are sinusoidal, and even though spikes can be entrained to the rhythm, they may occur at any phase [16]. Although we did not explore these more biologically plausible rhythms, previous work from Menschik et al demonstrates that a moderate-strength sinusoidal rhythm still leads to attractor-like dynamics in a recurrent network [12].…”

Section: Discussionmentioning

confidence: 98%

“…We presented a recipe for implementing common neural network algorithms using spiking neurons. We build on previous work [12] by making explicit parallels to statistical mechanics. To our knowledge, this is the first time a measure of thermodynamic temperature has been linked to neurons that spike probabilistically.…”

Section: Discussionmentioning

confidence: 99%

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The thermodynamic temperature of a rhythmic spiking network

Merolla,

Ursell,

Arthur

2010

Preprint

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Artificial neural networks built from two-state neurons are powerful computational substrates, whose computational ability is well understood by analogy with statistical mechanics. In this work, we introduce similar analogies in the context of spiking neurons in a fixed time window, where excitatory and inhibitory inputs drawn from a Poisson distribution play the role of temperature. For single neurons with a "bandgap" between their inputs and the spike threshold, this temperature allows for stochastic spiking. By imposing a global inhibitory rhythm over the fixed time windows, we connect neurons into a network that exhibits synchronous, clock-like updating akin to neural networks. We implement a single-layer Boltzmann machine without learning to demonstrate our model.

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