scholarly journals Changes within neural population codes can be inferred from psychophysical threshold studies

2020 ◽  
Author(s):  
Jason Hays ◽  
Fabian A. Soto
Keyword(s):  
External Noise ◽  
Neural Population ◽  
Specific Suppression ◽  
Population Responses ◽  
Visual Neuroscience ◽  
Change Mechanism ◽  
Psychophysical Data ◽  
Population Codes ◽  
Code Changes ◽  
Psychophysical Studies

AbstractThe use of population encoding models has come to dominate the study of human visual neuroscience, serving as a primary tool for making inferences about neural code changes based on indirect measurements. A popular approach in computational neuroimaging is to use such models to obtain estimates of neural population responses via inverted encoding modeling. Recent research suggests that this approach may be prone to identifiability problems, with multiple mechanisms of encoding change producing similar changes in the estimated population responses. Psychophysical data might be able to provide additional constraints to infer the encoding change mechanism underlying some behavior of interest. However, computational work aimed at determining to what extent different mechanisms can be differentiated using psychophysics is lacking. Here, we used simulation to explore exactly which of a number of changes in neural population codes could be differentiated from observed changes in psychophysical thresholds. Eight mechanisms of encoding change were under study, chosen because they have been proposed in the previous literature as mechanisms for improved task performance (e.g., due to attention or learning): specific and nonspecific gain, specific and nonspecific tuning, specific suppression, specific suppression plus gain, and inward and outward tuning shifts. We simulated psychophysical thresholds as a function of both external noise (TvN curves) or stimulus value (TvS curves) for a number of variations of each one of the models. With the exception of specific gain and specific tuning, all studied mechanisms produced qualitatively different patterns of change in the TvN and TvS curves, suggesting that psychophysical studies can be used as a complement to inverted encoding modeling, and provide strong constraints on inferences based on the latter. We use our results to provide recommendations for interested researchers and to re-interpret previous psychophysical data in terms of mechanisms of encoding change.

Reason for optimism: How a shifting focus on neural population codes is moving cognitive neuroscience beyond phrenology

2016 ◽  
Vol 39 ◽  
Author(s):  
Carolyn Parkinson ◽  
Thalia Wheatley
Keyword(s):  
Cognitive Neuroscience ◽  
Brain Function ◽  
Pattern Analysis ◽  
Brain Regions ◽  
Neural Population ◽  
Multivariate Pattern Analysis ◽  
Population Responses ◽  
Population Codes ◽  
Multivariate Pattern ◽  
Insight Into

AbstractMultivariate pattern analysis can address many of the challenges for cognitive neuroscience highlighted in After Phrenology (Anderson 2014) by illuminating the information content of brain regions and by providing insight into whether functional overlap reflects the recruitment of common or distinct computational mechanisms. Further, failing to consider submaximal but reliable population responses can lead to an overly modular account of brain function.

scholarly journals Reassessing optimal neural population codes with neurometric functions

2011 ◽  
Vol 108 (11) ◽  
pp. 4423-4428 ◽  
Author(s):  
P. Berens ◽  
A. S. Ecker ◽  
S. Gerwinn ◽  
A. S. Tolias ◽  
M. Bethge
Keyword(s):  
Neural Population ◽  
Population Codes

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.

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 Influence of Context and Behavior on Stimulus Reconstruction From Neural Activity in Primary Auditory Cortex

2009 ◽  
Vol 102 (6) ◽  
pp. 3329-3339 ◽  
Author(s):  
Nima Mesgarani ◽  
Stephen V. David ◽  
Jonathan B. Fritz ◽  
Shihab A. Shamma
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Receptive Fields ◽  
Primary Auditory Cortex ◽  
Neural Population ◽  
Stimulus Context ◽  
Sensory Stimuli ◽  
Complex Sounds ◽  
Population Responses ◽  
Stimulus Reconstruction

Population responses of cortical neurons encode considerable details about sensory stimuli, and the encoded information is likely to change with stimulus context and behavioral conditions. The details of encoding are difficult to discern across large sets of single neuron data because of the complexity of naturally occurring stimulus features and cortical receptive fields. To overcome this problem, we used the method of stimulus reconstruction to study how complex sounds are encoded in primary auditory cortex (AI). This method uses a linear spectro-temporal model to map neural population responses to an estimate of the stimulus spectrogram, thereby enabling a direct comparison between the original stimulus and its reconstruction. By assessing the fidelity of such reconstructions from responses to modulated noise stimuli, we estimated the range over which AI neurons can faithfully encode spectro-temporal features. For stimuli containing statistical regularities (typical of those found in complex natural sounds), we found that knowledge of these regularities substantially improves reconstruction accuracy over reconstructions that do not take advantage of this prior knowledge. Finally, contrasting stimulus reconstructions under different behavioral states showed a novel view of the rapid changes in spectro-temporal response properties induced by attentional and motivational state.

scholarly journals Dynamics of Neural Population Responses in Prefrontal Cortex Indicate Changes of Mind on Single Trials

2014 ◽  
Vol 24 (13) ◽  
pp. 1542-1547 ◽  
Author(s):  
Roozbeh Kiani ◽  
Christopher J. Cueva ◽  
John B. Reppas ◽  
William T. Newsome
Keyword(s):  
Prefrontal Cortex ◽  
Neural Population ◽  
Population Responses
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