On the relevance of time in neural computation and learning

Theoretical Computer Science

Volumn 261, Issue 1, 2001, Pages 157-178

  Maass, Wolfgang ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Computational complexity; Neural computation; Spiking neurons; Temporal coding

Indexed keywords

ENCODING (SYMBOLS); LEARNING SYSTEMS; NEURAL NETWORKS; VLSI CIRCUITS;

NEURAL COMPUTATION; TEMPORAL CODING;

COMPUTATIONAL COMPLEXITY;

EID: 0034926306     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3975(00)00137-7     Document Type: Conference Paper

References (66)

1. Decoding neuronal firing and modelling neural networks
2. Synaptic depression and cortical gain control
3. Spatiotemporal firing patterns in frontal cortex of behaving monkeys
4. Signal delay and input synchronization in passive dendritic structures
5. An evaluation of causes for unreliability of synaptic transmission
6. Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey
7. The effect of synchronized inputs at the single neuron level
8. Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex
9. Primary cortical representation of sounds by the coordination of action-potential timing
10. Heterogeneity of release probability, facilitation and depletion at central synapses
11. Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex
12. Latency: Another potential code for feature binding in striate cortex
13. Time structure of the activity in neural network models
14. How to describe neuronal activity: Spikes, rates, or assemblies?
15. A neuronal learning rule for sub-millisecond temporal coding
16. Pattern recognition computation using action potential timing for stimulus representations
17. VC-dimension in circuit complexity
18. Stimulus-dependent synchronization of neuronal responses in the visual cortex of the awake macaque monkey
19. Multielectrode investigation of monkey striate cortex: Spike train correlations in the infragranular layers
20. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
21. On the computational complexity of networks of spiking neurons
22. Lower bounds for the computational power of networks of spiking neurons
23. On the computational power of noisy spiking neurons
24. Fast sigmoidal networks via spiking neurons
25. Networks of spiking neurons: The third generation of neural network models
26. A simple model for neural computation with firing rates and firing correlations
27. Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding
28. A model for fast analog computation based on unreliable synapses
29. On computation with pulses
30. On the complexity of learning for a spiking neuron
31. On the complexity of learning for spiking networks temporal coding
32. Dynamic stochastic synapses as computational units
33. Reliability of spike timing in neocortical neurons
34. A model for visual shape recognition
35. Learning radial basis functions with spiking neurons using action potential timing
36. In search of common foundations for cortical computation
37. Computing functions with spiking neurons in temporal coding
38. Hebbian learning in networks of spiking neurons using temporal coding
39. Self-organizing maps of spiking neurons using temporal coding
40. Reproducibility and variability in neural spike trains
41. Decoding synapses
42. From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony
43. Shattering all sets of k points in 'general position' requires (k-1)/2 parameters
44. Speed of processing in the human visual system
45. Rapid visual processing using spike asynchrony
46. The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability
47. Dynamics of neuronal interactions in monkey cortex in relation to behavioural events
48. Odour encoding by temporal sequences of firing in oscillating neural assemblies
49. VC dimension of an integrate-and-fire neuron model
50. Short-term synaptic plasticity