letter - Hanks Lab
... Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions1–8. Here we develop a generalizable method to measure tuning curves that specify the relationship between neural responses and mentally accumulated ev ...
... Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions1–8. Here we develop a generalizable method to measure tuning curves that specify the relationship between neural responses and mentally accumulated ev ...
Reinforcement learning, conditioning, and the brain
... response (S–R) associations. Given a situation or stimulus S, the animal tries a response R. If the outcome is positive, the connection between S and R is strengthened; if the outcome is negative, the connection is weakened. In this way, the advantageous response or responses for each situation beco ...
... response (S–R) associations. Given a situation or stimulus S, the animal tries a response R. If the outcome is positive, the connection between S and R is strengthened; if the outcome is negative, the connection is weakened. In this way, the advantageous response or responses for each situation beco ...
Human frequency-following response: representation of pitch
... human FFR preserves pitch-relevant information about complex sounds that produce time-invariant pitch (Greenberg et al., 1987), leading them to conclude that pitch-relevant neural activity is based on the temporal pattern of neural activity in the brainstem. In light of these earlier ¢ndings, it is ...
... human FFR preserves pitch-relevant information about complex sounds that produce time-invariant pitch (Greenberg et al., 1987), leading them to conclude that pitch-relevant neural activity is based on the temporal pattern of neural activity in the brainstem. In light of these earlier ¢ndings, it is ...
Folie 1
... outputs. Each neuron computes a weighted sum of the incoming signals, to yield a net input, and passes this value through its sigmoidal activation function to yield the neuron's activation value. Unlike the perceptron, an MLP can solve linearly ...
... outputs. Each neuron computes a weighted sum of the incoming signals, to yield a net input, and passes this value through its sigmoidal activation function to yield the neuron's activation value. Unlike the perceptron, an MLP can solve linearly ...
SENSE AND THE SINGLE NEURON: Probing the Physiology of
... that should be fulfilled if we are to claim that some neuron or set of neurons plays a critical role in the generation of a perceptual event. All these points are, in principle, open to experimental test, but for some criteria, no experimental test may be immediately available, owing to current tech ...
... that should be fulfilled if we are to claim that some neuron or set of neurons plays a critical role in the generation of a perceptual event. All these points are, in principle, open to experimental test, but for some criteria, no experimental test may be immediately available, owing to current tech ...
Brain-Computer - University of South Australia
... are compromised. Thus, the pursuit of freedom through autonomy requires that privacy be available. Given the research advances outlined above and the commercial availability of BCIs, an ethical and legal obligation to support the privacy of BCI users exists. Should an fBCI be commercially viable, th ...
... are compromised. Thus, the pursuit of freedom through autonomy requires that privacy be available. Given the research advances outlined above and the commercial availability of BCIs, an ethical and legal obligation to support the privacy of BCI users exists. Should an fBCI be commercially viable, th ...
Hold your horses: A dynamic computational role
... The details of the BG model are described in Frank (2005a, 2005b). In brief, the premotor cortex represents and “considers” two possible responses (R1 and R2) for each input stimulus. The BG system modulates which one of these responses is facilitated and which is suppressed by signaling Go or NoGo ...
... The details of the BG model are described in Frank (2005a, 2005b). In brief, the premotor cortex represents and “considers” two possible responses (R1 and R2) for each input stimulus. The BG system modulates which one of these responses is facilitated and which is suppressed by signaling Go or NoGo ...
A Theory of Cerebral Cortex - Temporal Dynamics of Learning Center
... This report sketches the author’s theory of cerebral cortex and thalamus; incorporating relevant discoveries, improvements, changes and updates as of the issue date. The main focus is to provide a coherent, integrated picture of that portion of the theory which provides answers to the following ques ...
... This report sketches the author’s theory of cerebral cortex and thalamus; incorporating relevant discoveries, improvements, changes and updates as of the issue date. The main focus is to provide a coherent, integrated picture of that portion of the theory which provides answers to the following ques ...
Learning in the oculomotor system: from molecules to behavior
... afferent and efferent projections of the flocculus and ventral paraflocculus, which raise the possibility that the two structures may make somewhat different contributions to the VOR [27,28•,29•]. To understand VOR adaptation, it will be important to specify more precisely which synapses in the vest ...
... afferent and efferent projections of the flocculus and ventral paraflocculus, which raise the possibility that the two structures may make somewhat different contributions to the VOR [27,28•,29•]. To understand VOR adaptation, it will be important to specify more precisely which synapses in the vest ...
Rewardguided learning beyond dopamine in the nucleus
... Pavlovian CRs are autonomic or consummatory, other CRs, such as approach behavior towards a reward, are not so conveniently characterized (Rescorla & Solomon, 1967); indeed, they can easily be mistaken for instrumental actions (Brown & Jenkins, 1968; Williams & Williams, 1969; Schwartz & Gamzu, 1977 ...
... Pavlovian CRs are autonomic or consummatory, other CRs, such as approach behavior towards a reward, are not so conveniently characterized (Rescorla & Solomon, 1967); indeed, they can easily be mistaken for instrumental actions (Brown & Jenkins, 1968; Williams & Williams, 1969; Schwartz & Gamzu, 1977 ...
A Neural Network of Adaptively Timed Reinforcement
... processing stages are also compared with anatomical, neurophysiological, and biochemical data about several brain regions, notably the hippocampal formation. 1.2 Timing the Balance between Exploration for Novel Rewards and Consummation of Expected Rewards The spectral timing model clarifies the foll ...
... processing stages are also compared with anatomical, neurophysiological, and biochemical data about several brain regions, notably the hippocampal formation. 1.2 Timing the Balance between Exploration for Novel Rewards and Consummation of Expected Rewards The spectral timing model clarifies the foll ...
From Thought to Action
... segments. This effectively creates an axon that is composed of passive wires (myelinated ...
... segments. This effectively creates an axon that is composed of passive wires (myelinated ...
Neurally Plausible Model of Robot Reaching Inspired by Infant
... First of all, I would like to thank my Ph.D. advisor and mentor, Dr. Bruce MacLennan for his support, guidance, and patience throughout the course of my study. This dissertation would not have been completed without his invaluable teachings and inputs during our weekly meetings. I appreciate that he ...
... First of all, I would like to thank my Ph.D. advisor and mentor, Dr. Bruce MacLennan for his support, guidance, and patience throughout the course of my study. This dissertation would not have been completed without his invaluable teachings and inputs during our weekly meetings. I appreciate that he ...
Neural representation of object orientation: A dissociation between
... For each ROI (V1, LO, and pFs) potential hemispheric differences were assessed with a 2 (Hemisphere: Right vs. left) × 4 (Condition: Identical, OPA, EVA, Different) repeated-measures ANOVA. No main effects or interactions involving hemisphere were observed, so all subsequent analyses collapsed acros ...
... For each ROI (V1, LO, and pFs) potential hemispheric differences were assessed with a 2 (Hemisphere: Right vs. left) × 4 (Condition: Identical, OPA, EVA, Different) repeated-measures ANOVA. No main effects or interactions involving hemisphere were observed, so all subsequent analyses collapsed acros ...
Artificial neural network
In machine learning and cognitive science, artificial neural networks (ANNs) are a family of statistical learning models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial neural networks are generally presented as systems of interconnected ""neurons"" which exchange messages between each other. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.