For questions about modeling processes from cognitive and neurobiological theories via algorithms and computer simulations, and also about confirming experimental results with theoretical/statistical constructs.

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23
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3answers
1k views

What are some of the drawbacks to probabilistic models of cognition?

Probabilistic approaches to modelling cognition are increasing in popularity and being encouraged within the field (Chater, Tanenbaum, & Yuille, 2006). What are some of the arguments against or ...
22
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2answers
1k views

Neural networks with biologically plausible accounts of neurogenesis

One of the reasons artificial neural net algorithms like cascade correlation (pdf) have been generating interest is because they start with a minimal topology (just input and output unit) and recruit ...
16
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2answers
714 views

Applications of computational learning theory in the cognitive sciences

Computational learning theory (CoLT) is a branch of theoretical computer science associated with the mathematical analysis of machine learning. A lot of the early ideas of the field take inspiration ...
15
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3answers
1k views

Why does neuroplasticity decrease in adults?

Although adult brains are malleable and even undergo limited neuorgenesis, the extent of the neuroplasticiy is much lower than in children. This is most obvious in language acquisition, and recovery ...
12
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2answers
486 views

Biological plausibility of bayesian models of cognition

Inspired by this question: What are drawbacks to probabilistic models of cognition? I would like to know more about the biological plausibility of Bayesian models of cognition. Is there any neural ...
12
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1answer
244 views

Computational models of early learning in children

What are currently used biologically plausible computational models/frameworks of early learning in children? Personally, I have used cascade correlation neural nets to model pronoun acquisition ...
12
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1answer
168 views

How does the brain calculate velocity?

How does the human brain calculate velocities? For example, when crossing a road and seeing a car coming towards you, how does the brain actually compute the rough velocity of the vehicle and your own ...
11
votes
4answers
316 views

Why is training better when following an easy-to-difficult schedule?

As suggested in the answer to this question, experimental results show that training is most effective when it follows an easy-to-difficult schedule. What theories and specifically computational ...
10
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3answers
381 views

What are the key examples of the use of computational methods in the study of biological neural networks?

In an upcoming postdoc, I'm going to be looking through biological neural network data in the hopes of finding some interesting "patterns". I'm coming at this field from a mathematics/computer ...
10
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5answers
315 views

Visual search: complexity of positive vs negative search tasks

Thinking about experiments where participants perform visual search tasks, I remember hearing in a Cog Psych lecture that if the instructions of the task were of the form "find the element that has ...
10
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2answers
249 views

References for biologically plausible models of knowledge representation?

I'm looking for references that deal with the issue of how various kinds of semantic knowledge are (or might be) represented neurally. Most of the discussion of this topic seems skewed by social ...
10
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1answer
210 views

How do humans control saccades?

I've gathered the standard rational for a visual system utilizing saccades from perception textbooks: the neural cost of processing an entire scene at a high level of detail would be prohibitive, but ...
10
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1answer
244 views

Are there any modern mechanistic theories of motivation?

I remember hearing about the old 'drive' theory of motivation in Psych 101, and despite continuing my cognitive science education for another 4 years, that's the last theory of motivation I've heard ...
10
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3answers
188 views

Is there any recent work on modeling how we rapidly acquire new knowledge?

I work with neural network models of human cognition a lot, and one thing that bugs me about them is the timescale: they learn over thousands of trials whereas humans seem to learn after a couple ...
10
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1answer
148 views

Computational Model Linking Neural Activity to Behavior

A big question in neuroscience is how neural activity represents knowledge. We can use modelling to explore how different levels of neural activity- subthreshold currents, action potentials, local ...
10
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1answer
188 views

What explains variability in the mean firing rate across biological neurons?

Biological neurons have a trade-off between high information transfer (high firing rate) and energy conservation (low firing rate). One would suspect that the maximization of this function has a ...
9
votes
1answer
589 views

Spurious attractors in Hopfield networks

A classic "Hopfield network" is a type of artificial neural network in which the units are bi-stable and fully interconnected by symmetrically weighted connections. In 1982, Hopfield showed that such ...
9
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1answer
90 views

How distantly related are research in computational neuroscience and neural networks/machine learning?

If one is more interested in understanding how algorithms in the biological brain solve problems (theoretically, particularly the mathematical aspect), and possibly in building brain-inspired ...
8
votes
1answer
39 views

Spiking Neural Network Simulation: Measuring and Classifying Bump Attractor States

I am currently working with Spiking Neural Networks and multi-(meta)-stable attractor states. What I observe in my simulations are 'bump' attractors that appear, disappear, and may wander around. ...
8
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0answers
334 views

Modern treatments of Alan Turing's B-type neural networks

In the cognitive sciences Alan Turing is best known for launching AI with his Computing machinery and intelligence (1950). However, this was not his first contribution to the cognitive sciences, in ...
7
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3answers
382 views

What are the mathematical models of memory?

Are there mathematical models of memory in humans or animals? I want to know how neuroscientists use mathematics to describe memory in living creatures. How do neuroscientists model memory and show ...
7
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1answer
80 views

Importance of Neural Synchrony to Cognition

Is there a consensus on whether computation using Neural Synchrony is reasonable or not? In "How to Build a Brain", Chris Eliasmisth cites Yuko Munakata and R. C. O'Reilly as saying that "the ...
7
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2answers
72 views

What is the difference between spike-triggered averaging and reverse correlation?

I'm interested in the difference between spike-triggered averaging and reverse correlation. In some papers (i.e., Schwartz, Odelia, et al) I see the term 'Spike Triggered Averaging'. In others, (ie ...
7
votes
1answer
100 views

Can processing effort for sub-tasks in neural networks be measured?

I often heard statements like: 80% of your brain processing is computing the effect of gravity or, similarily: You only use 20% of your brain power My question isn't about the truth of ...
7
votes
1answer
51 views

What are biologically plausible ways to model binocular disparity?

I figure there is a vast body of literature on stereovision, both neurophysiological and computational studies. Computer Vision also provides some algorithmic insight on implementing binocular ...
7
votes
1answer
128 views

How can STDP fit with reciprocal connectivity?

I have rather technical question regarding STDP dynamics. I am working on a neural network implementing an STDP learning algorithm, and have noticed that it is extremely anti-reciprocal. When two ...
7
votes
1answer
115 views

Judgments of similarity between samples of writing

I was thinking last night about the possibility of an experiment that investigates the factors contributing to peoples' judgments of 'stylistic similarity' between two samples of writing. For example, ...
6
votes
2answers
147 views

How to computationally model the Wisconsin Card Sorting task? [closed]

The Wisconsin Card Sorting task is rather famous but appears to be quite difficult to model computationally. To respond to @Artem's question, I work in RL and I am interested in how people learn the ...
6
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3answers
126 views

How does the brain compute sound localisation without the equations?

What sort of computations are used for localising sound with the ears, and how does the brain compute the time difference between sounds reaching each ear? I am interested in the specific mechanisms ...
6
votes
1answer
33 views

Criteria for evaluating cognitive systems

In the first chapter of the book "How to Build a Brain", Chris Eliasmith quickly establishes some criteria which he will use to evaluate Spaun, the cognitive system described in the book. He describes ...
6
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1answer
25 views

What are the common components of other cognitive architectures and the Semantic Pointer Architecture

In the papers I've read about it, the Semantic Pointer Architecture (SPA) embodied in Spaun is said to be more biologically plausible than many other proposed architectures such as the Neural ...
5
votes
3answers
127 views

Are there any cognitive models for visual navigation?

I've seen a few neuroscience accounts of visual navigation and many A.I. projects, but no psychologically plausible accounts that actually solve the computational problem (i.e. produce a working ...
5
votes
1answer
93 views

Cosyne vs CNS conferences for Computational Neuroscience?

While Googling, I noticed there are 2 conferences for computational neuroscience: Cosyne and CNS. My questions are: 1) What are these conferences' differences in terms of material & impact/size? ...
5
votes
1answer
74 views

How can one estimate the excitability or mood of general public on a specific day?

I'm interested if there are publicly available tools or resources that can be used to gauge the overall activity/excitability or mood of general public for a specific day. For example, yesterday I ...
5
votes
2answers
128 views

Any models that act using both streams of visual processing?

The Two-Stream Hypothesis, where object properties are processed independently from spatial information, remains the most well established theory of visual processing. However, it concerns me that ...
5
votes
1answer
68 views

How to read a neuron tuning curve graph?

I'm working through the tutorial of section 2.4 of "How to Build a Brain" and I've encountered this graph of a neuron tuning curve. I understand the Y axis is the firing rate of the neuron, that ...
5
votes
1answer
47 views

Are there any agent based cognitive models that are inspired by complex systems studies of ant colonies and economies?

I think that certain aspects of ant colony behavior seem almost like economic decision-making in behavior. There are also links between ant colony optimization and features of the brain like selective ...
5
votes
1answer
113 views

Computational model of biological object recognition

The human brain can achieve a remarkable ability to recognize visual patterns in an Invariant, selective and fast manner. The human visual system is quite powerful. It has an exquisite selectivity ...
4
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3answers
138 views

Is there a complete cortico-cortical connectivity map based on a useful partitioning of the cortex?

I have something like Brodmann Areas in mind, but any complete list of cortex regions would do. I'm primarily interested in human brains here. Ultimately I'd like enough information to be able to ...
4
votes
1answer
88 views

Does the Hodgkin-Huxley Model take into account the action of the ion pumps (e.g., Na-K-ATPase)?

After the firing of a neuron, the sodium and potassium concentration differences vanish. It requires some time for cell to actively transport the ions in and out to re-establish the balance. Does ...
4
votes
1answer
242 views

Computational differences between spiking neural networks and previous ANNs

This is an AI question regarding "3rd generation neural networks" - spiking neural networks (SNN). I hve been studying this concept online from various papers, mainly Maass (1997). I and am not ...
4
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0answers
44 views

For binary (spike train) signals, take FFT of signal or autocorrelation of signal?

I want to characterize a binary time-series signal x (derived from neuron action potential data) in the frequency domain. Should I use the FFT of the original signal x, or the power spectrum (FFT of ...
4
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0answers
151 views

Is “Biophysics of Computation” still a good book?

To begin with, I hope this is the right place to ask - if not, please don't be too mad about it :) Currently I'm studying mathematics (2nd year) and I think I'm pretty into neuroscience. To "test" ...
3
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1answer
41 views

Relation between Nengo, SPA and NEF with respect to other Neural Models

I'm working through How to Build a Brain and I keep getting confused on the relation between Nengo, the Semantic Pointer Architecture (SPA) and the Neurological Engineering Framework (NEF). Are there ...
3
votes
1answer
156 views

What is the difference between computational neuroscience, theoretical neuroscience, and neuroinformatics (if there is one)?

In particular, theoretical and computational neuroscience seem to be synonymous with each other. Neuroinformatics at least seems to deal somewhat more with solving things numerically and the usage and ...
3
votes
1answer
36 views

How does the frequency of a visual stimulus affect the steady-state visually evoked potential?

I want to make a project for EEG signal processing, and in my research I found the concept of SSVEP, which means that if you have a stimulus with low frequency applied to the eye, the electrical ...
3
votes
1answer
21 views

How does hPES compare to the learning rates of ANNs?

The primary learning mechanism of artificial neural networks (ANN) is back-propagation, which is not biologically plausible. Trevor Berkolay created an alternative to this learning with the ...
3
votes
0answers
68 views

Predicting the Duration of Future Events

I am interested in the question of how people use/integrate previous experiences with instances of tasks or events to make predictions about the duration of future instances of tasks/events. To ...
3
votes
0answers
73 views

What are the most well-understood vocal animal languages?

There are many examples of animal language that involve vocal pattens or "grammar". For example, there is the the Bee dance, bird songs, whale songs, dogs. Bird vocalization includes both bird calls ...
2
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2answers
276 views

What skills are required to build simulations of the human brain? [closed]

I want to build a system that has the ability to gather data from the internet in order to build a cognitive model of the human brain. The model should be able to answer the questions required by a ...