Oscillatory dynamics in an attractor neural network with firing rate adaptation

2013 ◽  
Author(s):  
S. Rathore ◽  
D. Bush ◽  
P. Latham ◽  
N. Burgess
Keyword(s):  
Neural Network ◽  
Firing Rate ◽  
Rate Adaptation ◽  
Oscillatory Dynamics ◽  
Firing Rate Adaptation

Measurement and nature of firing rate adaptation in turtle spinal neurons

2005 ◽  
Vol 191 (7) ◽  
pp. 583-603 ◽  
Author(s):  
R. B. Gorman ◽  
J. C. McDonagh ◽  
T. G. Hornby ◽  
R. M. Reinking ◽  
D. G. Stuart
Keyword(s):  
Firing Rate ◽  
Rate Adaptation ◽  
Spinal Neurons ◽  
Firing Rate Adaptation

Firing Rate Adaptation without Losing Sensitivity to Input Fluctuations

2002 ◽  
pp. 180-185 ◽  
Author(s):  
Giancarlo La Camera ◽  
Alexander Rauch ◽  
Walter Senn ◽  
Hans-R. Lüscher ◽  
Stefano Fusi
Keyword(s):  
Firing Rate ◽  
Rate Adaptation ◽  
Firing Rate Adaptation

scholarly journals Dynamic Gain Changes During Attentional Modulation

2006 ◽  
Vol 18 (8) ◽  
pp. 1847-1867 ◽  
Author(s):  
Arun P. Sripati ◽  
Kenneth O. Johnson
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Model Neuron ◽  
Rate Adaptation ◽  
Recognition Task ◽  
Fano Factor ◽  
Attentional Modulation ◽  
Firing Rates ◽  
Spike Threshold ◽  
Firing Rate Adaptation

Attention causes a multiplicative effect on firing rates of cortical neurons without affecting their selectivity (Motter, 1993; McAdams & Maunsell, 1999a) or the relationship between the spike count mean and variance (McAdams & Maunsell, 1999b). We analyzed attentional modulation of the firing rates of 144 neurons in the secondary somatosensory cortex (SII) of two monkeys trained to switch their attention between a tactile pattern recognition task and a visual task. We found that neurons in SII cortex also undergo a predominantly multiplicative modulation in firing rates without affecting the ratio of variance to mean firing rate (i.e., the Fano factor). Furthermore, both additive and multiplicative components of attentional modulation varied dynamically during the stimulus presentation. We then used a standard conductance-based integrate-and-fire model neuron to ascertain which mechanisms might account for a multiplicative increase in firing rate without affecting the Fano factor. Six mechanisms were identified as biophysically plausible ways that attention could modify the firing rate: spike threshold, firing rate adaptation, excitatory input synchrony, synchrony between all inputs, membrane leak resistance, and reset potential. Of these, only a change in spike threshold or in firing rate adaptation affected model firing rates in a manner compatible with the observed neural data. The results indicate that only a limited number of biophysical mechanisms can account for observed attentional modulation.

Adaptation and Inhibition Underlie Responses to Time-Varying Interaural Phase Cues in a Model of Inferior Colliculus Neurons

2002 ◽  
Vol 88 (4) ◽  
pp. 2134-2146 ◽  
Author(s):  
Alla Borisyuk ◽  
Malcolm N. Semple ◽  
John Rinzel
Keyword(s):  
Firing Rate ◽  
Inferior Colliculus ◽  
Rate Adaptation ◽  
Phase Shifts ◽  
Hysteresis Curve ◽  
Medial Superior Olive ◽  
Binaural Beats ◽  
Dynamic Tuning ◽  
Interaural Phase ◽  
Firing Rate Adaptation

A mathematical model was developed for exploring the sensitivity of low-frequency inferior colliculus (IC) neurons to interaural phase disparity (IPD). The formulation involves a firing-rate-type model that does not include spikes per se. The model IC neuron receives IPD-tuned excitatory and inhibitory inputs (viewed as the output of a collection of cells in the medial superior olive). The model cell possesses cellular properties of firing rate adaptation and postinhibitory rebound (PIR). The descriptions of these mechanisms are biophysically reasonable, but only semi-quantitative. We seek to explain within a minimal model the experimentally observed mismatch between responses to IPD stimuli delivered dynamically and those delivered statically ( McAlpine et al. 2000 ; Spitzer and Semple 1993 ). The model reproduces many features of the responses to static IPD presentations, binaural beat, and partial range sweep stimuli. These features include differences in responses to a stimulus presented in static or dynamic context: sharper tuning and phase shifts in response to binaural beats, and hysteresis and “rise-from-nowhere” in response to partial range sweeps. Our results suggest that dynamic response features are due to the structure of inputs and the presence of firing rate adaptation and PIR mechanism in IC cells, but do not depend on a specific biophysical mechanism. We demonstrate how the model's various components contribute to shaping the observed phenomena. For example, adaptation, PIR, and transmission delay shape phase advances and delays in responses to binaural beats, adaptation and PIR shape hysteresis in different ranges of IPD, and tuned inhibition underlies asymmetry in dynamic tuning properties. We also suggest experiments to test our modeling predictions: in vitro simulation of the binaural beat (phase advance at low beat frequencies, its dependence on firing rate), in vivo partial range sweep experiments (dependence of the hysteresis curve on parameters), and inhibition blocking experiments (to study inhibitory tuning properties by observation of phase shifts).

scholarly journals Adaptive coding for dynamic sensory inference

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Wiktor F Młynarski ◽  
Ann M Hermundstad
Keyword(s):  
Firing Rate ◽  
Rate Adaptation ◽  
Sensory Systems ◽  
Neural Representation ◽  
High Fidelity ◽  
Neural Responses ◽  
Adaptive Coding ◽  
Optimal Coding ◽  
Adaptive Encoding ◽  
Firing Rate Adaptation

Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

Grid maps for spaceflight, anyone? They are for free!

2013 ◽  
Vol 36 (5) ◽  
pp. 566-567 ◽  
Author(s):  
Federico Stella ◽  
Bailu Si ◽  
Emilio Kropff ◽  
Alessandro Treves
Keyword(s):  
Firing Rate ◽  
Single Unit ◽  
Three Dimensional ◽  
Rate Adaptation ◽  
Face Centered Cubic ◽  
Cubic Crystal ◽  
Unit Level ◽  
Face Centered ◽  
Approximate Periodicity ◽  
Firing Rate Adaptation

AbstractWe show that, given extensive exploration of a three-dimensional volume, grid units can form with the approximate periodicity of a face-centered cubic crystal, as the spontaneous product of a self-organizing process at the single unit level, driven solely by firing rate adaptation.

scholarly journals Adaptive coding for dynamic sensory inference

2017 ◽  
Author(s):  
Wiktor Młynarski ◽  
Ann M. Hermundstad
Keyword(s):  
Firing Rate ◽  
Rate Adaptation ◽  
Sensory Systems ◽  
Neural Representation ◽  
High Fidelity ◽  
Neural Responses ◽  
Adaptive Coding ◽  
Optimal Coding ◽  
Adaptive Encoding ◽  
Firing Rate Adaptation

AbstractBehavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally-relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

scholarly journals Time course of dynamic range adaptation in the auditory nerve

2012 ◽  
Vol 108 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Bo Wen ◽  
Grace I. Wang ◽  
Isabel Dean ◽  
Bertrand Delgutte
Keyword(s):  
Firing Rate ◽  
Auditory Processing ◽  
Auditory Nerve ◽  
Time Course ◽  
Dynamic Range ◽  
Rate Adaptation ◽  
Sound Level ◽  
Sound Levels ◽  
Level Function ◽  
Firing Rate Adaptation

Auditory adaptation to sound-level statistics occurs as early as in the auditory nerve (AN), the first stage of neural auditory processing. In addition to firing rate adaptation characterized by a rate decrement dependent on previous spike activity, AN fibers show dynamic range adaptation, which is characterized by a shift of the rate-level function or dynamic range toward the most frequently occurring levels in a dynamic stimulus, thereby improving the precision of coding of the most common sound levels (Wen B, Wang GI, Dean I, Delgutte B. J Neurosci 29: 13797–13808, 2009). We investigated the time course of dynamic range adaptation by recording from AN fibers with a stimulus in which the sound levels periodically switch from one nonuniform level distribution to another (Dean I, Robinson BL, Harper NS, McAlpine D. J Neurosci 28: 6430–6438, 2008). Dynamic range adaptation occurred rapidly, but its exact time course was difficult to determine directly from the data because of the concomitant firing rate adaptation. To characterize the time course of dynamic range adaptation without the confound of firing rate adaptation, we developed a phenomenological “dual adaptation” model that accounts for both forms of AN adaptation. When fitted to the data, the model predicts that dynamic range adaptation occurs as rapidly as firing rate adaptation, over 100–400 ms, and the time constants of the two forms of adaptation are correlated. These findings suggest that adaptive processing in the auditory periphery in response to changes in mean sound level occurs rapidly enough to have significant impact on the coding of natural sounds.

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