scholarly journals Using natural stimuli to estimate receptive fields in neurons that employ sparse coding

2010 ◽  
Vol 4 ◽  
Author(s):  
Sommer Friedrich
Keyword(s):  
Sparse Coding ◽  
Receptive Fields ◽  
Natural Stimuli

scholarly journals Activity Patterns of Interneurons in the Caudal Ganglion of the Crayfish

1960 ◽  
Vol 43 (3) ◽  
pp. 655-670 ◽  
Author(s):  
Donald Kennedy ◽  
James B. Preston
Keyword(s):  
Activity Patterns ◽  
Receptive Fields ◽  
Synaptic Activity ◽  
Constant Frequency ◽  
Natural Stimuli ◽  
Synaptic Potentials ◽  
Tactile Stimuli ◽  
Afferent Inputs ◽  
Degree Of Convergence ◽  
High Degree

Responses of ascending interneurons from the caudal ganglion of crayfish have been recorded from single units isolated by dissection from the ventral nerve cord; in addition, post-synaptic activity within the ganglionic neuropile has been studied with intracellular micropipettes. The following classes of interneurons have been found: (1) Large fibers which responded to tactile stimuli with single spikes or phasic bursts. These units usually showed broad receptive fields; and spontaneous activity, when present, showed transitory depressions following responses to natural stimuli. (2) A group of fibers, including many small ones, which responded to proprioceptive stimuli with tonic discharges of varying adaptation rate. (3) Interneurons which showed responses both to tactile stimuli and to activation of the sixth ganglion photoreceptor; and (4) units with constant frequency discharges which were unmodifiable by any of the above afferent inputs. Intracellular recording of post-synaptic activity has shown (1) that widely graded excitatory post-synaptic potentials occur; (2) that multiple firing from single synaptic potentials is usual; (3) that the post-synaptic responses to phasic natural stimuli and to electrical stimulation of ganglionic roots are similar. The existence of widely graded post-synaptic potentials and of extensive receptive fields suggests a high degree of convergence from primary afferents to interneurons. The activation of such post-synaptic units involves integrative synaptic transfer, without 1:1 correspondence between pre- and post-fiber activity.

scholarly journals Scene statistics and noise determine the relative arrangement of receptive field mosaics

2021 ◽  
Vol 118 (39) ◽  
pp. e2105115118
Author(s):  
Na Young Jun ◽  
Greg D. Field ◽  
John Pearson
Keyword(s):  
Neural Coding ◽  
Coding Theory ◽  
Receptive Fields ◽  
Optimal Arrangement ◽  
Efficient Coding ◽  
Natural Stimuli ◽  
Signal Stimulus ◽  
Theoretical Predictions ◽  
On And Off Pathways ◽  
Contrast Response

Many sensory systems utilize parallel ON and OFF pathways that signal stimulus increments and decrements, respectively. These pathways consist of ensembles or grids of ON and OFF detectors spanning sensory space. Yet, encoding by opponent pathways raises a question: How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? We investigated this question using a model of the retina guided by efficient coding theory. Specifically, we optimized spatial receptive fields and contrast response functions to encode natural images given noise and constrained firing rates. We find that the optimal arrangement of ON and OFF receptive fields exhibits a transition between aligned and antialigned grids. The preferred phase depends on detector noise and the statistical structure of the natural stimuli. These results reveal that noise and stimulus statistics produce qualitative shifts in neural coding strategies and provide theoretical predictions for the configuration of opponent pathways in the nervous system.

scholarly journals Learning sparse codes from compressed representations with biologically plausible local wiring constraints

2020 ◽  
Author(s):  
Kion Fallah ◽  
Adam A. Willats ◽  
Ninghao Liu ◽  
Christopher J. Rozell
Keyword(s):  
Unsupervised Learning ◽  
Sparse Coding ◽  
Visual Information ◽  
Neural Coding ◽  
Receptive Fields ◽  
Sparse Representations ◽  
Linear Operators ◽  
Compression Process ◽  
Cortical Areas ◽  
Theoretical Neuroscience

AbstractSparse coding is an important method for unsupervised learning of task-independent features in theoretical neuroscience models of neural coding. While a number of algorithms exist to learn these representations from the statistics of a dataset, they largely ignore the information bottlenecks present in fiber pathways connecting cortical areas. For example, the visual pathway has many fewer neurons transmitting visual information to cortex than the number of photoreceptors. Both empirical and analytic results have recently shown that sparse representations can be learned effectively after performing dimensionality reduction with randomized linear operators, producing latent coefficients that preserve information. Unfortunately, current proposals for sparse coding in the compressed space require a centralized compression process (i.e., dense random matrix) that is biologically unrealistic due to local wiring constraints observed in neural circuits. The main contribution of this paper is to leverage recent results on structured random matrices to propose a theoretical neuroscience model of randomized projections for communication between cortical areas that is consistent with the local wiring constraints observed in neuroanatomy. We show analytically and empirically that unsupervised learning of sparse representations can be performed in the compressed space despite significant local wiring constraints in compression matrices of varying forms (corresponding to different local wiring patterns). Our analysis verifies that even with significant local wiring constraints, the learned representations remain qualitatively similar, have similar quantitative performance in both training and generalization error, and are consistent across many measures with measured macaque V1 receptive fields.

Afferent input to movement-related precentral neurones in conscious monkeys

1976 ◽  
Vol 194 (1116) ◽  
pp. 313-339 ◽  
Keyword(s):  
Pyramidal Tract ◽  
Significant Proportion ◽  
Receptive Fields ◽  
Total Sample ◽  
Afferent Input ◽  
Peripheral Region ◽  
Joint Movement ◽  
Natural Stimulus ◽  
Natural Stimuli ◽  
Stimulation Of

Monkeys were trained to perform a stereotyped movement task, and to accept passive manipulation and natural stimulation of the limbs while remaining relaxed and quiet. All training, both of movement and for relaxation, was with food rewards. The effects of natural stimuli on 257 precentral neurones showing consistent modulations in discharge frequency during the performance of the movement task were investigated. Most precentral neurones had small, stable input zones located on the contralateral arm: 197 facilitatory and 17 inhibitory responses were obtained, while the remaining 43 cells were unaffected by the natural stimuli used. The most common natural stimulus capable of influencing precentral neurones was joint movement: 152 cells responded to joint movement, including 98 which only responded to movement at a single joint. Joint movement rather than joint position was the effective stimulus and none of these cells was influenced by palpation of muscles acting at the joint. The next most common natural stimulus capable of influencing precentral neurones was muscle palpation: 35 cells responded to a tap applied to a localized portion of the muscle belly, including 26 cells which also responded to movement of the joint at which the muscle acted. The direction of joint movement which influenced the cell was usually such as to stretch the muscle containing the receptors for the effective afferent input set up by tapping. The natural stimulus which influenced the smallest number of precentral neurones was tactile stimulation of the skin: 27 cells had cutaneous receptive fields, most of which were small ( < 5 cm 2 ) and confined to the hand. Included in the total sample were 51 pyramidal tract neurones. The behaviour of these was found to be similar to the unidentified neurones examined in the same animals with respect to their afferent input. However, there was a tendency for pyramidal tract neurones to be in receipt of a more convergent input than unidentified neurones in their vicinity. The majority of neurones recorded in close proximity to one another (within 500 μm or less) usually received their afferent input from the same peripheral region, but a significant proportion of such cells received inputs from different and remote peripheral zones. Hence the afferent input to the precentral motor cortex is not organized to provide independent and spatially segregated projections from particular peripheral sites only to limited and localized radial aggregations of neurones.

Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli

2001 ◽  
Vol 12 (3) ◽  
pp. 289-316 ◽  
Author(s):  
F.E. Theunissen ◽  
S.V. David ◽  
N.C. Singh ◽  
A. Hsu ◽  
W.E. Vinje ◽  
...  
Keyword(s):  
Receptive Fields ◽  
Visual Neurons ◽  
Natural Stimuli ◽  
Spatio Temporal

scholarly journals Cortical Spikes use Analog Sparse Coding

2020 ◽  
Author(s):  
Dana H. Ballard ◽  
Ruohan Zhang
Keyword(s):  
Sparse Coding ◽  
Action Potentials ◽  
Receptive Fields ◽  
Network Models ◽  
Pyramidal Cells ◽  
Task Structure ◽  
Sinusoidal Grating ◽  
Functional Interpretation ◽  
Gamma Frequency ◽  
Visual Inputs

Quantifying the message communicated by neurons in the cortex by averaging action potentials over repeated trials of a given stimulus can reveal neuronal tuning features. For example, simple cells in the visual cortex have been characterized by reverse correlation based on the detailed structure of their oriented receptive fields. This structure, in turn, has been modeled using large libraries of such receptive fields to allow the simultaneous coding of visual stimuli with small numbers of appropriate combinations of cells selected from the library. This strategy, known as sparse coding, has been shown to produce excellent approximations for natural visual inputs. In concert with this mathematical development has been the discovery of cells’ use of oscillations in the gamma frequency range for general coding tasks, such as a mechanism for synchronizing distal networks of neurons. More recently, spikes timed with oscillations have been shown to exhibit local phase delays within a single gamma cycle, but such delays have resisted a behavioral functional interpretation. We show here that a specific coordinate system for the gamma cycle allows resultant phase delays to be interpreted quantitatively in classical terms. Specifically, extracted phase delays from mice viewing oriented sinusoidal grating images are shown to have the same distributions as those from a computer sparse coding model using natural images, suggesting for the first time a direct link between experimentally measured phase delays and model receptive fields.Significance StatementNetworks of pyramidal cells in the cortex exhibit action potentials (spikes) that are characterized by randomness and low firing rates. Spike averaging methods have been ordinarily useful in dealing with these features to reveal behavioral task structure, but the randomness and slowness so far prevented the specification of a satisfactory generative spike model. We show that a spike can be analyzed using the context of a specific phase of the gamma component of its membrane potential. The result is each spike can be can be assigned a scalar, which makes it immediately useful for network models.

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