53 research outputs found
Hippocampal CA1 place cells encode intended destination on a maze with multiple choice points
The hippocampus encodes both spatial and nonspatial aspects of a rat's ongoing behavior at the single-cell level. In this study, we examined the encoding of intended destination by hippocampal (CA1) place cells during performance of a serial reversal task on a double Y-maze. On the maze, rats had to make two choices to access one of four possible goal locations, two of which contained reward. Reward locations were kept constant within blocks of 10 trials but changed between blocks, and the session of each day comprised three or more trial blocks. A disproportionate number of place fields were observed in the start box and beginning stem of the maze, relative to other locations on the maze. Forty-six percent of these place fields had different firing rates on journeys to different goal boxes. Another group of cells had place fields before the second choice point, and, of these, 44% differentiated between journeys to specific goal boxes. In a second experiment, we observed that rats with hippocampal damage made significantly more errors than control rats on the Y-maze when reward locations were reversed. Together, these results suggest that, at the start of the maze, the hippocampus encodes both current location and the intended destination of the rat, and this encoding is necessary for the flexible response to changes in reinforcement contingencies
Generation of Paths in a Maze using a Deep Network without Learning
Trajectory- or path-planning is a fundamental issue in a wide variety of
applications. Here we show that it is possible to solve path planning for
multiple start- and end-points highly efficiently with a network that consists
only of max pooling layers, for which no network training is needed. Different
from competing approaches, very large mazes containing more than half a billion
nodes with dense obstacle configuration and several thousand path end-points
can this way be solved in very short time on parallel hardware
Multi Sentence Description of Complex Manipulation Action Videos
Automatic video description requires the generation of natural language
statements about the actions, events, and objects in the video. An important
human trait, when we describe a video, is that we are able to do this with
variable levels of detail. Different from this, existing approaches for
automatic video descriptions are mostly focused on single sentence generation
at a fixed level of detail. Instead, here we address video description of
manipulation actions where different levels of detail are required for being
able to convey information about the hierarchical structure of these actions
relevant also for modern approaches of robot learning. We propose one hybrid
statistical and one end-to-end framework to address this problem. The hybrid
method needs much less data for training, because it models statistically
uncertainties within the video clips, while in the end-to-end method, which is
more data-heavy, we are directly connecting the visual encoder to the language
decoder without any intermediate (statistical) processing step. Both frameworks
use LSTM stacks to allow for different levels of description granularity and
videos can be described by simple single-sentences or complex multiple-sentence
descriptions. In addition, quantitative results demonstrate that these methods
produce more realistic descriptions than other competing approaches
Action Prediction in Humans and Robots
Efficient action prediction is of central importance for the fluent workflow
between humans and equally so for human-robot interaction. To achieve
prediction, actions can be encoded by a series of events, where every event
corresponds to a change in a (static or dynamic) relation between some of the
objects in a scene. Manipulation actions and others can be uniquely encoded
this way and only, on average, less than 60% of the time series has to pass
until an action can be predicted. Using a virtual reality setup and testing ten
different manipulation actions, here we show that in most cases humans predict
actions at the same event as the algorithm. In addition, we perform an in-depth
analysis about the temporal gain resulting from such predictions when chaining
actions and show in some robotic experiments that the percentage gain for
humans and robots is approximately equal. Thus, if robots use this algorithm
then their prediction-moments will be compatible to those of their human
interaction partners, which should much benefit natural human-robot
collaboration
Differential Hebbian learning with time-continuous signals for active noise reduction
Spike timing-dependent plasticity, related to differential Hebb-rules, has become a leading paradigm in neuronal learning, because weights can grow or shrink depending on the timing of pre- and post-synaptic signals. Here we use this paradigm to reduce unwanted (acoustic) noise. Our system relies on heterosynaptic differential Hebbian learning and we show that it can efficiently eliminate noise by up to -140 dB in multi-microphone setups under various conditions. The system quickly learns, most often within a few seconds, and it is robust with respect to different geometrical microphone configurations, too. Hence, this theoretical study demonstrates that it is possible to successfully transfer differential Hebbian learning, derived from the neurosciences, into a technical domain
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