Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons

Beck, Jeffrey M.; Bejjanki, Vikranth R.; Pouget, Alexandre

doi:10.1162/neco_a_00125

Cited by 62 publications

(93 citation statements)

References 26 publications

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“…The broad, graded responses (as opposed to all-or-none) to preferred and non-preferred stimuli (Fig. 2 G, H) are in accord with experimental results (6,9,10,31,32) and confirm earlier theoretical studies arguing that sharp tuning is not a necessary feature for a sparse sensory representation (33)(34)(35). The sparsity of the response to each signal is a direct consequence of the detailed balance of correlated excitatory and inhibitory synapses as described above, not of the specificity of the tuning curve.…”

supporting

confidence: 90%

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Vogels

Sprekeler

Zenke

et al. 2011

Science

712

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."A Hebbian form of synaptic plasticity at inhibitory synapses generates balanced input currents and sparse neuronal responses that stabilize memory traces in neuronal networks" Cortical neurons receive balanced excitatory and inhibitory membrane currents.Here, we show that such a balance can be established and maintained in an experiencedependent manner by synaptic plasticity at inhibitory synapses. The mechanism we put forward provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. We show that the introduction of inhibitory plasticity in suitable recurrent networks provides a homeostatic mechanism that leads to asynchronous irregular network states. Further, it can accommodate synaptic memories with activity patterns that become indiscernible from the background state, but can be re-activated by external stimuli. Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry. 1The balance of excitatory and inhibitory membrane currents a neuron experiences during stimulated and ongoing activity has been the topic of many recent studies (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). This balance, first defined as equal average amounts of de-and hyperpolarizing membrane currents (from hereon referred to as "global balance") is thought to be essential for maintaining stability of cortical networks (1, 2). In the balanced state networks display asynchronous irregular (AI) dynamics that mimic activity patterns observed in cortical neurons. Such asynchronous network states facilitate rapid responses to small changes in the input (2-4), providing an ideal substrate for cortical signal processing (5,15,16). Pathologies that disrupt the balance of excitation and inhibition have often been implicated in neurological diseases such as epilepsy or schizophrenia (17, 18).Moreover, the input currents to a given cortical neuron are not merely globally balanced. Excitatory and inhibitory inputs are coupled also in time (6-8) and co-tuned for different stimulus features (9,10). The tight coupling of excitation and inhibition suggests a more precise, detailed balance, in which each excitatory input arrives at the cell together with an inhibitory counterpart, supposedly supplied through feedforward inhibition (Fig. 1 A). These observations fit well with models of cortical processing in which balanced sensory inputs are left unattended, but can be transiently (11), or persistently turned on by targeted disruptions of the balance (12-14).Although it is widely thought that the excitatory-inhibitory balance plays an important role for stability and information processing in cortical networks, it is still not understood by which mechanisms this balance is established and maintained in the presence of ongoing sensory experiences. Inspired by recent experimental results (9), we investigate the hypothesis that synaptic plastic...

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supporting

confidence: 90%

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Vogels

Sprekeler

Zenke

et al. 2011

Science

712

1,046

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“…In particular, competition induces negative noise correlations across the two oppositely tuned sensory populations, a condition that impairs discrimination accuracy1150. Understanding how variability ultimately constrains function will require a systematic characterization of the relation between connectivity, dynamics and correlations in networks designed to carry out specific computations5152.…”

Section: Discussionmentioning

confidence: 99%

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

et al. 2015

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Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability.

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“…However, we expect our analysis to remain valid at least qualitatively even if there are substantial correlations in the data that are due to other sources. Second, by modeling attention on the phenomenological level and treating it as a common gain, we have ignored the question of how such a gain modulation may be implemented in a neural network (Bejjanki et al, 2011) and reduced attentional fluctuations to modulations in one-dimensional subspaces. Although this simplification will miss any changes in the correlation structure that are due to the underlying network mechanisms, we note that there are very few experimental data available to constrain more mechanistic, network-level models.…”

Section: Discussionmentioning

confidence: 99%

“…To show that the second term above is O(1), we assume that the amplitudes of the neurons' tuning curves are independent random variables (Shamir and Sompolinsky, 2006;Ecker et al, 2011). In this case, the quantity of interest is the expected value with respect to different realizations of the heterogeneity:…”

Section: Coding Accuracy Under Fluctuations Of Attentional Gainmentioning

confidence: 99%

On the Structure of Neuronal Population Activity under Fluctuations in Attentional State

et al. 2016

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Attention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations. However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in attentional state affect the structure of population responses in a simple model of spatial and feature attention. In our model, attention acts on the neural response exclusively by modulating each neuron's gain. Neurons are conditionally independent given the stimulus and the attentional gain, and correlated activity arises only from trial-to-trial fluctuations of the attentional state, which are unknown to the experimenter. We find that this simple model can readily explain many aspects of neural response modulation under attention, such as increased response gain, reduced individual and shared variability, increased correlations with firing rates, limited range correlations, and differential correlations. We therefore suggest that attention may act primarily by increasing response gain of individual neurons without affecting their correlation structure. The experimentally observed reduction in correlations may instead result from reduced variability of the attentional gain when a stimulus is attended. Moreover, we show that attentional gain fluctuations, even if unknown to a downstream readout, do not impair the readout accuracy despite inducing limited-range correlations, whereas fluctuations of the attended feature can in principle limit behavioral performance.

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Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons

Cited by 62 publications

References 26 publications

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

On the Structure of Neuronal Population Activity under Fluctuations in Attentional State

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