Balanced amplification: a new mechanism of selective amplification of neural activity patterns

By Brendan K. Murphy, Kenneth D. Miller. 2009

351 citations recorded by the source corpus.

In cerebral cortex, ongoing activity absent a stimulus can resemble stimulus-driven activity in size and structure. In particular, spontaneous activity in cat primary visual cortex (V1) has structure significantly correlated with evoked responses to oriented stimuli. This suggests that, from unstructured input, cortical circuits selectively amplify specific activity patterns. Current understanding of selective amplification involves elongation of a neural assembly’s lifetime by mutual excitation among its neurons. We introduce a new mechanism for selective amplification without elongation of lifetime: “balanced amplification”. Strong balanced amplification arises when feedback inhibition stabilizes strong recurrent excitation, a pattern likely to be typical of cortex. Thus, balanced amplification should ubiquitously contribute to cortical activity. Balanced amplification depends on the fact that individual neurons project only excitatory or only inhibitory synapses. This leads to a hidden feedforward connectivity between activity patterns. We show in a detailed biophysical model that this can explain the cat V1 observations.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Normalization as a canonical neural computation (2011, neutral): “This circuit and more recently proposed circuits, such as one centred on balanced amplificatio”
Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression (2009, neutral): “The strong excitatory recurrence in ISNs allows the networks to perform complex computations, and yet remain stable enough to respond in a graded way to stimuli of different strengths [8].”
Insects have the capacity for subjective experience (2016, background): “Amplification provides a mechanism to increase signal-to-noise ratio and to produce ongoing neural activity after the initial sensory stimulus has ceased (Murphy and Miller, 2009).”
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics (2014, background): “Although these dynamics were clearly simplified in that they were fundamentally linear, such dynamical systems have been used to model a wide variety of neural processes [47, 48, 49].”
Neuronal circuits underlying persistent representations despite time varying activity. (2012, background): “After stimulus presentation at time zero, the network has sustained complex activity for over 30 neural time constants (Figure 2D), as would be expected from complex eigenvalues with real values close (but not equal to) unity, yet stimulus representation remains perfectly stable (in other FEVER netw”
Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding (2010, background): “Thus, even in a recurrent network, the flow of spiking activity on a local scale ('locality' being defined by connection delays, firing rates and refractoriness), can be treated as an FF”
Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework (2016, background): “The analogous constraint that all connection weights from a given unit must have the same sign can have a profound effect on the types of dynamics, such as non-normality [22], that operate in the circuit.”
Decorrelation of Neural-Network Activity by Inhibitory Feedback (2012, background): “In this sense, the resulting system has been termed ’feedforward’ [47].”
Efficient codes and balanced networks (2016, background): “…strong and have a similar structure—this effect will selectively amplify the components of the feedforward inputs that match the patterns of recurrent connectivity, a mechanism that could explain why patterns resembling orientation columns are observed during sponta-neous activity in cortex 66 .”