Recurrent cerebellar architecture solves the motor-error problem

Current views of cerebellar function have been heavily influenced by the models of Marr and Albus, who suggested that the climbing fibre input to the cerebellum acts as a teaching signal for motor learning. It is commonly assumed that this teaching signal must be motor error (the difference between actual and correct motor command), but this approach requires complex neural structures to estimate unobservable motor error from its observed sensory consequences. We have proposed elsewhere a recurrent decorrelation control architecture in which Marr-Albus models learn without requiring motor error. Here, we prove convergence for this architecture and demonstrate important advantages for the modular control of systems with multiple degrees of freedom. These results are illustrated by modelling adaptive plant compensation for the three-dimensional vestibular ocular reflex. This provides a functional role for recurrent cerebellar connectivity, which may be a generic anatomical feature of projections between regions of cerebral and cerebellar cortex

Learning Spiking Neural Systems with the Event-Driven Forward-Forward Process

We develop a novel credit assignment algorithm for information processing with spiking neurons without requiring feedback synapses. Specifically, we propose an event-driven generalization of the forward-forward and the predictive forward-forward learning processes for a spiking neural system that iteratively processes sensory input over a stimulus window. As a result, the recurrent circuit computes the membrane potential of each neuron in each layer as a function of local bottom-up, top-down, and lateral signals, facilitating a dynamic, layer-wise parallel form of neural computation. Unlike spiking neural coding, which relies on feedback synapses to adjust neural electrical activity, our model operates purely online and forward in time, offering a promising way to learn distributed representations of sensory data patterns with temporal spike signals. Notably, our experimental results on several pattern datasets demonstrate that the even-driven forward-forward (ED-FF) framework works well for training a dynamic recurrent spiking system capable of both classification and reconstruction

Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependant Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn ‘early spike patterns’, that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense ‘distractor’ spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it

A neural circuit model of decision uncertainty and change-of-mind

Decision-making is often accompanied by a degree of confidence on whether a choice is correct. Decision uncertainty, or lack in confidence, may lead to change-of-mind. Studies have identified the behavioural characteristics associated with decision confidence or change-of-mind, and their neural correlates. Although several theoretical accounts have been proposed, there is no neural model that can compute decision uncertainty and explain its effects on change-of-mind. We propose a neuronal circuit model that computes decision uncertainty while accounting for a variety of behavioural and neural data of decision confidence and change-of-mind, including testable model predictions. Our theoretical analysis suggests that change-of-mind occurs due to the presence of a transient uncertainty-induced choice-neutral stable steady state and noisy fluctuation within the neuronal network. Our distributed network model indicates that the neural basis of change-of-mind is more distinctively identified in motor-based neurons. Overall, our model provides a framework that unifies decision confidence and change-of-mind

Perception of the intensity and duration of a stimulus within a unified framework: psychophysics and underlying neuronal processing

Every sensory experience is embedded in time, and is accompanied by the perception of the passage of time. The fact that perception of the content of a sensory event and the perception of the time occupied by that event are generated in parallel raises a number of questions: Do these percepts interact with each other? Do they emerge within separate neural populations? Which neuronal mechanism underlies this divergence? In the work of my thesis I explored how the perception of the intensity of a vibrotactile stimulus, interacts with the perception of its duration, in both humans and rats. I have carried out three main studies. Chapter I works out the details of the interaction between vibration amplitude and duration, revealing a symmetric confound: perceived duration depends on stimulus speed, and perceived intensity depends on stimulus duration. Quantification of this interaction allowed us formulate a testable computational model for the generation of both percepts, which posits that a single sensory drive provides input to two distinct downstream centers, which generate the two percepts in parallel. Chapter II addresses the effect of stimulus history. Systems neuroscience has given considerable attention in recent years to the effects of preceding stimuli on the perception of the current stimulus. We now ask whether the interaction found in Study I extends to an interaction in the memory trace of recent stimuli: are the perceptual priors mixed or separate? Through psychophysical testing, we were able to show that perception of the duration and the intensity of stimuli, are biased toward the perceived features of previously presented stimuli, and not their low-level physical properties, and that separate representations of prior perceived duration and prior perceived intensity exist in the brain. Chapter III begins to look for neuronal correlates of perceived duration, through extracellular recordings in behaving rats in Dorso-Lateral Striatum (DLS), a region which receives direct input from primary somatosensory cortex and has previously shown to be involved in time perception. The delayed comparison task, differently from many common behavioral paradigms, has the advantage of dissociating the first stimulus presented to the animal from any decisional and motor processes. This makes it particularly relevant for the search for the neural basis of stimulus duration perception. Moreover, the bias of stimulus intensity on perceived time found on Study I, posits the principle that the interaction between these two features should be present in the neural population that encodes the perception of stimulus duration in a behaviourally-relevant way. Ongoing recordings are showing that the unfolding of trial time can be decoded from the striatal neural activity, but the confound of stimulus speed is not encoded by the population. This findings points toward a role of striatum in representing temporal sequences of events, while questioning its involvement in encoding the perception of stimulus duration