scholarly journals Signal integration at spherical bushy cells enhances representation of temporal structure but limits its range

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Christian Keine ◽  
Rudolf Rübsamen ◽  
Bernhard Englitz
Keyword(s):  
Temporal Structure ◽  
Gain Control ◽  
Auditory Brainstem ◽  
Neural Population ◽  
Signal Integration ◽  
Signal Representation ◽  
Population Responses ◽  
Bushy Cell ◽  
Neuronal Inhibition ◽  
Dynamic Stimulus

Neuronal inhibition is crucial for temporally precise and reproducible signaling in the auditory brainstem. Previously we showed that for various synthetic stimuli, spherical bushy cell (SBC) activity in the Mongolian gerbil is rendered sparser and more reliable by subtractive inhibition (Keine et al., 2016). Here, employing environmental stimuli, we demonstrate that the inhibitory gain control becomes even more effective, keeping stimulated response rates equal to spontaneous ones. However, what are the costs of this modulation? We performed dynamic stimulus reconstructions based on neural population responses for auditory nerve (ANF) input and SBC output to assess the influence of inhibition on acoustic signal representation. Compared to ANFs, reconstructions of natural stimuli based on SBC responses were temporally more precise, but the match between acoustic and represented signal decreased. Hence, for natural sounds, inhibition at SBCs plays an even stronger role in achieving sparse and reproducible neuronal activity, while compromising general signal representation.

scholarly journals On the Subspace Invariance of Population Responses

2018 ◽  
Author(s):  
Elaine Tring ◽  
Dario L. Ringach
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Power Distribution ◽  
Gain Control ◽  
Population Coding ◽  
Neural Population ◽  
Angular Deviation ◽  
Strong Negative Correlation ◽  
Population Responses ◽  
The Individual

The response of a neural population in cat primary visual cortex to the linear combination of two sinusoidal gratings (a plaid) can be well approximated by a weighted sum of the population responses to the individual gratings – a property we refer to as subspace invariance. We tested subspace invariance in mouse primary visual cortex by measuring the angle between the population response to a plaid and the plane spanned by the population responses to its individual components. We found robust violations of subspace invariance, represented by a median angular deviation of ~55 deg. The cause of this departure is a strong, negative correlation between the mean responses a neuron to the individual gratings and its response to the plaid. We suggest that an early nonlinearity may distort the power distribution of grating and plaid stimuli such that plaids have a prominent power component at ±45 deg off the fundamental orientations. We conclude that subspace invariance does not hold in mouse V1. This finding rules out a large class of possible models of population coding, including vector averaging and gain control.

scholarly journals Optimal Temporal Decoding of Neural Population Responses in a Reaction-Time Visual Detection Task

2008 ◽  
Vol 99 (3) ◽  
pp. 1366-1379 ◽  
Author(s):  
Yuzhi Chen ◽  
Wilson S. Geisler ◽  
Eyal Seidemann
Keyword(s):  
Reaction Time ◽  
Initial Response ◽  
Detection Task ◽  
Ideal Observer ◽  
Neural Population ◽  
Population Responses ◽  
Temporal Correlations ◽  
Lower Amplitude ◽  
Temporal Decorrelation ◽  
Speed And Accuracy

Behavioral performance in detection and discrimination tasks is likely to be limited by the quality and nature of the signals carried by populations of neurons in early sensory cortical areas. Here we used voltage-sensitive dye imaging (VSDI) to directly measure neural population responses in the primary visual cortex (V1) of monkeys performing a reaction-time detection task. Focusing on the temporal properties of the population responses, we found that V1 responses are consistent with a stimulus-evoked response with amplitude and latency that depend on target contrast and a stimulus-independent additive noise with long-lasting temporal correlations. The noise had much lower amplitude than the ongoing activity reported previously in anesthetized animals. To understand the implications of these properties for subsequent processing stages that mediate behavior, we derived the Bayesian ideal observer that specifies how to optimally use neural responses in reaction time tasks. Using the ideal observer analysis, we show that 1) the observed temporal correlations limit the performance benefit that can be attained by accumulating V1 responses over time, 2) a simple temporal decorrelation operation with time-lagged excitation and inhibition minimizes the detrimental effect of these correlations, 3) the neural information relevant for target detection is concentrated in the initial response following stimulus onset, and 4) a decoder that optimally uses V1 responses far outperforms the monkey in both speed and accuracy. Finally, we demonstrate that for our particular detection task, temporal decorrelation followed by an appropriate running integrator can approach the speed and accuracy of the optimal decoder.

Mechanisms for generating temporal filters in the electrosensory system

1999 ◽  
Vol 202 (10) ◽  
pp. 1281-1289 ◽  
Author(s):  
G.J. Rose ◽  
E.S. Fortune
Keyword(s):  
Nervous System ◽  
Discharge Rate ◽  
Temporal Structure ◽  
Sensory Information ◽  
Gain Control ◽  
Membrane Properties ◽  
Control Mechanisms ◽  
Temporal Features ◽  
Level Of Activity ◽  
The Central Nervous System

Temporal patterns of sensory information are important cues in behaviors ranging from spatial analyses to communication. Neural representations of the temporal structure of sensory signals include fluctuations in the discharge rate of neurons over time (peripheral nervous system) and the differential level of activity in neurons tuned to particular temporal features (temporal filters in the central nervous system). This paper presents our current understanding of the mechanisms responsible for the transformations between these representations in electric fish of the genus Eigenmannia. The roles of passive and active membrane properties of neurons, and frequency-dependent gain-control mechanisms are discussed.

scholarly journals Reinforcement Learning With Modulated Spike Timing–Dependent Synaptic Plasticity

2007 ◽  
Vol 98 (6) ◽  
pp. 3648-3665 ◽  
Author(s):  
Michael A. Farries ◽  
Adrienne L. Fairhall
Keyword(s):  
Synaptic Plasticity ◽  
Reinforcement Learning ◽  
General Purpose ◽  
Spike Timing ◽  
Neural Population ◽  
Minimal Set ◽  
Population Responses ◽  
Wide Range ◽  
Output Neurons ◽  
Novel Model

Spike timing–dependent synaptic plasticity (STDP) has emerged as the preferred framework linking patterns of pre- and postsynaptic activity to changes in synaptic strength. Although synaptic plasticity is widely believed to be a major component of learning, it is unclear how STDP itself could serve as a mechanism for general purpose learning. On the other hand, algorithms for reinforcement learning work on a wide variety of problems, but lack an experimentally established neural implementation. Here, we combine these paradigms in a novel model in which a modified version of STDP achieves reinforcement learning. We build this model in stages, identifying a minimal set of conditions needed to make it work. Using a performance-modulated modification of STDP in a two-layer feedforward network, we can train output neurons to generate arbitrarily selected spike trains or population responses. Furthermore, a given network can learn distinct responses to several different input patterns. We also describe in detail how this model might be implemented biologically. Thus our model offers a novel and biologically plausible implementation of reinforcement learning that is capable of training a neural population to produce a very wide range of possible mappings between synaptic input and spiking output.

scholarly journals Nonlinear Lateral Interactions in V1 Population Responses Explained by a Contrast Gain Control Model

2018 ◽  
Vol 38 (47) ◽  
pp. 10069-10079
Author(s):  
Melchi M. Michel ◽  
Yuzhi Chen ◽  
Eyal Seidemann ◽  
Wilson S. Geisler
Keyword(s):  
Gain Control ◽  
Control Model ◽  
Lateral Interactions ◽  
Population Responses ◽  
Contrast Gain ◽  
Contrast Gain Control
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