Abstract
It has been proposed that neural noise in the cortex arises from chaotic dynamics in the balanced state: in this model of cortical dynamics, the excitatory and inhibitory inputs to each neuron approximately cancel, and activity is driven by fluctuations of the synaptic inputs around their mean. It remains unclear whether neural networks in the balanced state can perform tasks that are highly sensitive to noise, such as storage of continuous parameters in working memory, while also accounting for the irregular behavior of single neurons. Here we show that continuous parameter working memory can be maintained in the balanced state, in a neural circuit with a simple network architecture. We show analytically that in the limit of an infinite network, the dynamics generated by this architecture are characterized by a continuous set of steady balanced states, allowing for the indefinite storage of a continuous parameter. In finite networks, we show that the chaotic noise drives diffusive motion along the approximate attractor, which gradually degrades the stored memory. We analyze the dynamics and show that the slow diffusive motion induces slowly decaying temporal cross correlations in the activity, which differ substantially from those previously described in the balanced state. We calculate the diffusivity, and show that it is inversely proportional to the system size. For large enough (but realistic) neural population sizes, and with suitable tuning of the network connections, the proposed balanced network can sustain continuous parameter values in memory over time scales larger by several orders of magnitude than the single neuron time scale.
Highlights
The consequences of irregular activity in the brain, and the mechanisms responsible for its emergence, are topics of fundamental interest in the study of brain function and dynamics
This work studies the effects of chaotic dynamics, a prominent feature of the balanced state model, on storage of continuous parameters in working memory
We propose a simple model of a balanced network with mutual inhibition, and show that it possesses a continuum of steady states, a commonly proposed mechanism for maintenance of continuous parameter working memory in the brain
Summary
The consequences of irregular activity in the brain, and the mechanisms responsible for its emergence, are topics of fundamental interest in the study of brain function and dynamics. In theoretical models of brain activity, the irregular dynamics observed in neuronal activity are often modeled as arising from noisy inputs or from intrinsic noise in the dynamics of single neurons. Theoretical and experimental works have suggested that explanations based on sources of noise in intrinsic neural dynamics are insufficient to account for the stochastic nature of activity in the cortex [1,2,3,4]. An alternative proposal is that noise in the cortex arises primarily from chaotic dynamics at the network level [3,4,5,6]. The overall dynamics are chaotic, resulting in an apparent stochasticity in the activity of single neurons, which can exist even in the absence of any sources of random noise intrinsic to the dynamics of single neurons and synapses
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