The cerebellum could solve the motor error problem through error increase prediction

We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error. We analyze our architecture formally and computationally for the case of reaching in a 3D environment. In the case of motor control, we show that there are synergies of this architecture with the Equilibrium-Point hypothesis, leading to novel ways to solve the motor error problem. In particular, the presence of desired equilibrium lengths for muscles provides a way to know when the error is increasing, and which corrections to apply. In the context of Threshold Control Theory and Perceptual Control Theory we show how to extend our model so it implements anticipative corrections in cascade control systems that span from muscle contractions to cognitive operations.Comment: 34 pages (without bibliography), 13 figure

Midazolam, hippocampal function, and transitive inference: Reply to Greene

The transitive inference (TI) task assesses the ability to generalize learned knowledge to new contexts, and is thought to depend on the hippocampus (Dusek & Eichenbaum, 1997). Animals or humans learn in separate trials to choose stimulus A over B, B over C, C over D and D over E, via reinforcement feedback. Transitive responding based on the hierarchical structure A > B > C > D > E is then tested with the novel BD pair. We and others have argued that successful BD performance by animals – and even humans in some implicit studies – can be explained by simple reinforcement learning processes which do not depend critically on the hippocampus, but rather on the striatal dopamine system. We recently showed that the benzodiazepene midazolam, which is thought to disrupt hippocampal function, profoundly impaired human memory recall performance but actually enhanced implicit TI performance (Frank, O'Reilly & Curran, 2006). We posited that midazolam biased participants to recruit striatum during learning due to dysfunctional hippocampal processing, and that this change actually supported generalization of reinforcement values. Greene (2007) questions the validity of our pharmacological assumptions and argues that our conclusions are unfounded. Here we stand by our original hypothesis, which remains the most parsimonious account of the data, and is grounded by multiple lines of evidence

Receptive Field Characteristics That Allow Parietal Lobe Neurons to Encode Spatial Properties of Visual Input: A Computational Analysis

A subset of visually sensitive neurons in the parietal lobe apparently can encode the locations of stimuli, whereas visually sensitive neurons in the inferotemporal cortex (area IT) cannot. This finding is puzzling because both sorts of neurons have large receptive fields, and yet location can be encoded in one case, but not in the other. The experiments reported here investigated the hypothesis that a crucial difference between the IT and parietal neurons is the spatial distribution of their response profiles. In particular, IT neurons typically respond maximally when stimuli are presented at the fovea, whereas parietal neurons do not. We found that a parallel-distributed-processing network could map a point in an array to a coordinate representation more easily when a greater proportion of its input units had response peaks off the center of the input array. Furthermore, this result did not depend on potentially implausible assumptions about the regularity of the overlap in receptive fields or the homogeneity of the response profiles of different units. Finally, the internal representations formed within the network had receptive fields resembling those found in area 7a of the parietal lobe.Psycholog

A Neural Network Model of Continual Learning with Cognitive Control

Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans learn effectively in these settings, in some cases even showing an advantage of blocking, suggesting the brain contains mechanisms to overcome this problem. Here, we build on previous work and show that neural networks equipped with a mechanism for cognitive control do not exhibit catastrophic forgetting when trials are blocked. We further show an advantage of blocking over interleaving when there is a bias for active maintenance in the control signal, implying a tradeoff between maintenance and the strength of control. Analyses of map-like representations learned by the networks provided additional insights into these mechanisms. Our work highlights the potential of cognitive control to aid continual learning in neural networks, and offers an explanation for the advantage of blocking that has been observed in humans.Comment: 7 pages, 5 figures, paper accepted as a talk to CogSci 2022 (https://escholarship.org/uc/item/3gn3w58z

Visual Representation in the Wild: How Rhesus Monkeys Parse Objects

Visual object representation was studied in free-ranging rhesus monkeys. To facilitate comparison with humans, and to provide a new tool for neurophysiologists, we used a looking time procedure originally developed for studies of human infants. Monkeys' looking times were measured to displays with one or two distinct objects, separated or together, stationary or moving. Results indicate that rhesus monkeys used featural information to parse the displays into distinct objects, and they found events in which distinct objects moved together more novel or unnatural than events in which distinct objects moved separately. These findings show both common-alities and contrasts with those obtained from human infants. We discuss their implications for the development and neural mechanisms of higher-level vision.Psycholog