A Novel Learning Rule for Long-Term Plasticity of Short-Term Synaptic Plasticity Enhances Temporal Processing

It is well established that short-term synaptic plasticity (STP) of neocortical synapses is itself plastic – e.g., the induction of LTP and LTD tend to shift STP towards short-term depression and facilitation, respectively. What has not been addressed theoretically or experimentally is whether STP is “learned”; that is, is STP regulated by specific learning rules that are in place to optimize the computations performed at synapses, or, are changes in STP essentially an epiphenomenon of long-term plasticity? Here we propose that STP is governed by specific learning rules that operate independently and in parallel of the associative learning rules governing baseline synaptic strength. We describe a learning rule for STP and, using simulations, demonstrate that it significantly enhances the discrimination of spatiotemporal stimuli. Additionally we generate a set of experimental predictions aimed at testing our hypothesis

Learning of Temporal Motor Patterns: An Analysis of Continuous Versus Reset Timing

Our ability to generate well-timed sequences of movements is critical to an array of behaviors, including the ability to play a musical instrument or a video game. Here we address two questions relating to timing with the goal of better understanding the neural mechanisms underlying temporal processing. First, how does accuracy and variance change over the course of learning of complex spatiotemporal patterns? Second, is the timing of sequential responses most consistent with starting and stopping an internal timer at each interval or with continuous timing? To address these questions we used a psychophysical task in which subjects learned to reproduce a sequence of finger taps in the correct order and at the correct times – much like playing a melody at the piano. This task allowed us to calculate the variance of the responses at different time points using data from the same trials. Our results show that while “standard” Weber’s law is clearly violated, variance does increase as a function of time squared, as expected according to the generalized form of Weber’s law – which separates the source of variance into time-dependent and time-independent components. Over the course of learning, both the time-independent variance and the coefficient of the time-dependent term decrease. Our analyses also suggest that timing of sequential events does not rely on the resetting of an internal timer at each event. We describe and interpret our results in the context of computer simulations that capture some of our psychophysical findings. Specifically, we show that continuous timing, as opposed to “reset” timing, is consistent with “population clock” models in which timing emerges from the internal dynamics of recurrent neural networks

Musical expertise generalizes to superior temporal scaling in a Morse code tapping task

A key feature of the brain’s ability to tell time and generate complex temporal patterns is its capacity to produce similar temporal patterns at different speeds. For example, humans can tie a shoe, type, or play an instrument at different speeds or tempi—a phenomenon referred to as temporal scaling. While it is well established that training improves timing precision and accuracy, it is not known whether expertise improves temporal scaling, and if so, whether it generalizes across skill domains. We quantified temporal scaling and timing precision in musicians and non-musicians as they learned to tap a Morse code sequence. We found that non-musicians improved significantly over the course of days of training at the standard speed. In contrast, musicians exhibited a high level of temporal precision on the first day, which did not improve significantly with training. Although there was no significant difference in performance at the end of training at the standard speed, musicians were significantly better at temporal scaling—i.e., at reproducing the learned Morse code pattern at faster and slower speeds. Interestingly, both musicians and non-musicians exhibited a Weber-speed effect, where temporal precision at the same absolute time was higher when producing patterns at the faster speed. These results are the first to establish that the ability to generate the same motor patterns at different speeds improves with extensive training and generalizes to non-musical domains

Multifocal Fluorescence Microscope for Fast Optical Recordings of Neuronal Action Potentials

AbstractIn recent years, optical sensors for tracking neural activity have been developed and offer great utility. However, developing microscopy techniques that have several kHz bandwidth necessary to reliably capture optically reported action potentials (APs) at multiple locations in parallel remains a significant challenge. To our knowledge, we describe a novel microscope optimized to measure spatially distributed optical signals with submillisecond and near diffraction-limit resolution. Our design uses a spatial light modulator to generate patterned illumination to simultaneously excite multiple user-defined targets. A galvanometer driven mirror in the emission path streaks the fluorescence emanating from each excitation point during the camera exposure, using unused camera pixels to capture time varying fluorescence at rates that are ∼1000 times faster than the camera’s native frame rate. We demonstrate that this approach is capable of recording Ca2+ transients resulting from APs in neurons labeled with the Ca2+ sensor Oregon Green Bapta-1 (OGB-1), and can localize the timing of these events with millisecond resolution. Furthermore, optically reported APs can be detected with the voltage sensitive dye DiO-DPA in multiple locations within a neuron with a signal/noise ratio up to ∼40, resolving delays in arrival time along dendrites. Thus, the microscope provides a powerful tool for photometric measurements of dynamics requiring submillisecond sampling at multiple locations

Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset

Distortions of Subjective Time Perception Within and Across Senses

Background: The ability to estimate the passage of time is of fundamental importance for perceptual and cognitive processes. One experience of time is the perception of duration, which is not isomorphic to physical duration and can be distorted by a number of factors. Yet, the critical features generating these perceptual shifts in subjective duration are not understood. Methodology/Findings: We used prospective duration judgments within and across sensory modalities to examine the effect of stimulus predictability and feature change on the perception of duration. First, we found robust distortions of perceived duration in auditory, visual and auditory-visual presentations despite the predictability of the feature changes in the stimuli. For example, a looming disc embedded in a series of steady discs led to time dilation, whereas a steady disc embedded in a series of looming discs led to time compression. Second, we addressed whether visual (auditory) inputs could alter the perception of duration of auditory (visual) inputs. When participants were presented with incongruent audio-visual stimuli, the perceived duration of auditory events could be shortened or lengthened by the presence of conflicting visual information; however, the perceived duration of visual events was seldom distorted by the presence of auditory information and was never perceived shorter than their actual durations. Conclusions/Significance: These results support the existence of multisensory interactions in the perception of duration and, importantly, suggest that vision can modify auditory temporal perception in a pure timing task. Insofar as distortions in subjective duration can neither be accounted for by the unpredictability of an auditory, visual or auditory-visual event, we propose that it is the intrinsic features of the stimulus that critically affect subjective time distortions

A Computational Mechanism for Unified Gain and Timing Control in the Cerebellum

Precise gain and timing control is the goal of cerebellar motor learning. Because the basic neural circuitry of the cerebellum is homogeneous throughout the cerebellar cortex, a single computational mechanism may be used for simultaneous gain and timing control. Although many computational models of the cerebellum have been proposed for either gain or timing control, few models have aimed to unify them. In this paper, we hypothesize that gain and timing control can be unified by learning of the complete waveform of the desired movement profile instructed by climbing fiber signals. To justify our hypothesis, we adopted a large-scale spiking network model of the cerebellum, which was originally developed for cerebellar timing mechanisms to explain the experimental data of Pavlovian delay eyeblink conditioning, to the gain adaptation of optokinetic response (OKR) eye movements. By conducting large-scale computer simulations, we could reproduce some features of OKR adaptation, such as the learning-related change of simple spike firing of model Purkinje cells and vestibular nuclear neurons, simulated gain increase, and frequency-dependent gain increase. These results suggest that the cerebellum may use a single computational mechanism to control gain and timing simultaneously

Decoding Temporal Information: A Model Based on Short-Term Synaptic Plasticity

words: interval; short-term plasticity; paired-pulse facilitation; paired-pulse depression; timing; temporal processing Our perception of the world is based on the spatiotemporal patterns of neuronal activity produced at sensory layers. By decoding these patterns the brain determines what we see and hear. It is useful to distinguish between the spatial and temporal content of stimuli because f undamentally different mechanisms may underlie each form of processing. Spatial information refers to stimuli defined by the location of active sensory afferents. For instance, vertical and horizontal bars of light activate different retinal ganglion cells arranged in specific spatial patterns. Similarly, 1 and 4 kHz tones activate spatially distinct populations of cochlear hair cells. In both cases there is a place code at the earliest sensory stages. In its simplest form, the generation of neurons that respond selectively to spatial stimuli is a wiring problem: a neuron that responds

Harnessing Chaos in Recurrent Neural Networks

In this issue of Neuron, Sussillo and Abbott describe a new learning rule that helps harness the computational power of recurrent neural networks