17,604 research outputs found
Stimulus-dependent maximum entropy models of neural population codes
Neural populations encode information about their stimulus in a collective
fashion, by joint activity patterns of spiking and silence. A full account of
this mapping from stimulus to neural activity is given by the conditional
probability distribution over neural codewords given the sensory input. To be
able to infer a model for this distribution from large-scale neural recordings,
we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal
extension of the canonical linear-nonlinear model of a single neuron, to a
pairwise-coupled neural population. The model is able to capture the
single-cell response properties as well as the correlations in neural spiking
due to shared stimulus and due to effective neuron-to-neuron connections. Here
we show that in a population of 100 retinal ganglion cells in the salamander
retina responding to temporal white-noise stimuli, dependencies between cells
play an important encoding role. As a result, the SDME model gives a more
accurate account of single cell responses and in particular outperforms
uncoupled models in reproducing the distributions of codewords emitted in
response to a stimulus. We show how the SDME model, in conjunction with static
maximum entropy models of population vocabulary, can be used to estimate
information-theoretic quantities like surprise and information transmission in
a neural population.Comment: 11 pages, 7 figure
Functional Clustering Drives Encoding Improvement in a Developing Brain Network during Awake Visual Learning
Sensory experience drives dramatic structural and functional plasticity in developing neurons. However, for single-neuron plasticity to optimally improve whole-network encoding of sensory information, changes must be coordinated between neurons to ensure a full range of stimuli is efficiently represented. Using two-photon calcium imaging to monitor evoked activity in over 100 neurons simultaneously, we investigate network-level changes in the developing Xenopus laevis tectum during visual training with motion stimuli. Training causes stimulus-specific changes in neuronal responses and interactions, resulting in improved population encoding. This plasticity is spatially structured, increasing tuning curve similarity and interactions among nearby neurons, and decreasing interactions among distant neurons. Training does not improve encoding by single clusters of similarly responding neurons, but improves encoding across clusters, indicating coordinated plasticity across the network. NMDA receptor blockade prevents coordinated plasticity, reduces clustering, and abolishes whole-network encoding improvement. We conclude that NMDA receptors support experience-dependent network self-organization, allowing efficient population coding of a diverse range of stimuli.Canadian Institutes of Health Researc
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Towards Informed Exploration for Deep Reinforcement Learning
In this thesis, we discuss various techniques for improving exploration for deep reinforcement learning. We begin with a brief review of reinforcement learning (RL) and the fundamental v.s. exploitation trade-off. Then we review how deep RL has improved upon classical and summarize six categories of the latest exploration methods for deep RL, in the order increasing usage of prior information. We then explore representative works in three categories discuss their strengths and weaknesses. The first category, represented by Soft Q-learning, uses regularization to encourage exploration. The second category, represented by count-based via hashing, maps states to hash codes for counting and assigns higher exploration to less-encountered states. The third category utilizes hierarchy and is represented by modular architecture for RL agents to play StarCraft II. Finally, we conclude that exploration by prior knowledge is a promising research direction and suggest topics of potentially impact
Optimal Neural Codes for Natural Stimuli
The efficient coding hypothesis assumes that biological sensory systems use neural codes that are optimized to best possibly represent the stimuli that occur in their environment. When formulating such optimization problem of neural codes, two key components must be considered. The first is what types of constraints the neural codes must satisfy? The second is the objective function itself -- what is the goal of the neural codes? We seek to provide a systematic framework to address these types of problem.
Previous work often assume one specific set of constraint and analytically or numerically solve the optimization problem. Here we want to put everything in a unified framework and show that these results can be understood from a much more generalized perspective. In particular, we provide analytical solutions for a variety of neural noise models and two types of constraint: a range constraint which specifies the max/min neural activity and a metabolic constraint which upper bounds the mean neural activity.
In terms of objective functions, most common models rely on information theoretic measures, whereas alternative formulations propose incorporating downstream decoding performance. We systematically evaluate different optimality criteria based upon the reconstruction error of the maximum likelihood decoder. This parametric family of optimal criteria includes special cases such as the information maximization criterion and the mean squared loss minimization of decoding error. We analytically derive the optimal tuning curve of a single neuron in terms of the reconstruction error norm to encode natural stimuli with an arbitrary input distribution.
Under our framework, we can try to answer questions such as what is the objective function the neural code is actually using? Under what constraints can the predicted results provide a better fit for the actual data? Using different combination of objective function and constraints, we tested our analytical predictions against previously measured characteristics of some early visual systems found in biology. We find solutions under the metabolic constraint and low values of provides a better fit for physiology data on early visual perception systems
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Arousal regulates frequency tuning in primary auditory cortex.
Changes in arousal influence cortical sensory representations, but the synaptic mechanisms underlying arousal-dependent modulation of cortical processing are unclear. Here, we use 2-photon Ca2+ imaging in the auditory cortex of awake mice to show that heightened arousal, as indexed by pupil diameter, broadens frequency-tuned activity of layer 2/3 (L2/3) pyramidal cells. Sensory representations are less sparse, and the tuning of nearby cells more similar when arousal increases. Despite the reduction in selectivity, frequency discrimination by cell ensembles improves due to a decrease in shared trial-to-trial variability. In vivo whole-cell recordings reveal that mechanisms contributing to the effects of arousal on sensory representations include state-dependent modulation of membrane potential dynamics, spontaneous firing, and tone-evoked synaptic potentials. Surprisingly, changes in short-latency tone-evoked excitatory input cannot explain the effects of arousal on the broadness of frequency-tuned output. However, we show that arousal strongly modulates a slow tone-evoked suppression of recurrent excitation underlying lateral inhibition [H. K. Kato, S. K. Asinof, J. S. Isaacson, Neuron, 95, 412-423, (2017)]. This arousal-dependent "network suppression" gates the duration of tone-evoked responses and regulates the broadness of frequency tuning. Thus, arousal can shape tuning via modulation of indirect changes in recurrent network activity
From Holistic to Discrete Speech Sounds: The Blind Snow-Flake Maker Hypothesis
Sound is a medium used by humans to carry information.
The existence of this kind of
medium is a pre-requisite for language. It is organized
into a code, called speech, which
provides a repertoire of forms that is shared in each
language community. This code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is characterized by several
properties: speech is digital and compositional (vocalizations
are made of units re-used systematically in other syllables);
phoneme inventories have precise regularities as well as
great diversity in human languages; all the speakers of a
language community categorize sounds in the same manner,
but each language has its own system of categorization,
possibly very different from every other.
How can a speech code with these properties form?
These are the questions we will approach in the paper. We will
study them using the method of the artificial. We will
build a society of artificial agents, and study what mechanisms
may provide answers. This will not prove directly what mechanisms
were used for humans, but rather give ideas about what kind
of mechanism may have been used. This allows us to shape the
search space of possible answers, in particular by showing
what is sufficient and what is not necessary.
The mechanism we present is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
allows a population of agents to build a speech code that
has the properties mentioned above. The originality is
that it pre-supposes neither a functional pressure for
communication, nor the ability to have coordinated
social interactions (they do not play language or imitation
games). It relies on the self-organizing properties of a generic
coupling between perception and production both
within agents, and on the interactions between agents
Stochasticity from function -- why the Bayesian brain may need no noise
An increasing body of evidence suggests that the trial-to-trial variability
of spiking activity in the brain is not mere noise, but rather the reflection
of a sampling-based encoding scheme for probabilistic computing. Since the
precise statistical properties of neural activity are important in this
context, many models assume an ad-hoc source of well-behaved, explicit noise,
either on the input or on the output side of single neuron dynamics, most often
assuming an independent Poisson process in either case. However, these
assumptions are somewhat problematic: neighboring neurons tend to share
receptive fields, rendering both their input and their output correlated; at
the same time, neurons are known to behave largely deterministically, as a
function of their membrane potential and conductance. We suggest that spiking
neural networks may, in fact, have no need for noise to perform sampling-based
Bayesian inference. We study analytically the effect of auto- and
cross-correlations in functionally Bayesian spiking networks and demonstrate
how their effect translates to synaptic interaction strengths, rendering them
controllable through synaptic plasticity. This allows even small ensembles of
interconnected deterministic spiking networks to simultaneously and
co-dependently shape their output activity through learning, enabling them to
perform complex Bayesian computation without any need for noise, which we
demonstrate in silico, both in classical simulation and in neuromorphic
emulation. These results close a gap between the abstract models and the
biology of functionally Bayesian spiking networks, effectively reducing the
architectural constraints imposed on physical neural substrates required to
perform probabilistic computing, be they biological or artificial
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