Neural Computation
Overview
Neural Computation is a peer-reviewed journal that focuses on the interdisciplinary field of computational neuroscience. It publishes cutting-edge research on the computational principles underlying neural systems, including artificial intelligence, machine learning, and neural network models. The journal provides a platform for researchers to explore the complex interactions between neurons and their computational implications, advancing our understanding of brain function and inspiring innovative applications in various domains.
Details
Details
Abbr.
Neural Comput
Publisher
MIT Press
Start
1989
End
Continuing
Frequency
Monthly, 2000-
p-ISSN
0899-7667
e-ISSN
1530-888X
Country
United States
Language
English
Specialty
Medical Informatics
Metrics
Metrics
h-index / Ranks: 933
180
SJR / Ranks: 4168
948
CiteScore / Ranks: 1684
9.30
JIF / Ranks: 3624
2.9
Recent Articles
1.
Lorenzo J, Rico-Gallego J, Binczak S, Jacquir S
Neural Comput
. 2025 Mar;
:1-31.
PMID: 40030144
From biological and artificial network perspectives, researchers have started acknowledging astrocytes as computational units mediating neural processes. Here, we propose a novel biologically inspired neuron-astrocyte network model for image recognition,...
2.
Pankka H, Lehtinen J, Ilmoniemi R, Roine T
Neural Comput
. 2025 Mar;
:1-22.
PMID: 40030141
Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and...
3.
Graham B, Kay J, Phillips W
Neural Comput
. 2025 Mar;
:1-47.
PMID: 40030139
Neocortical layer 5 thick-tufted pyramidal cells are prone to exhibiting burst firing on receipt of coincident basal and apical dendritic inputs. These inputs carry different information, with basal inputs coming...
4.
Huang K, Liu M, Ma S
Neural Comput
. 2025 Mar;
:1-56.
PMID: 40030138
We propose a sparse deep ReLU network (SDRN) estimator of the regression function obtained from regularized empirical risk minimization with a Lipschitz loss function. Our framework can be applied to...
5.
Marino R, Buffoni L, Chicchi L, Patti F, Febbe D, Giambagli L, et al.
Neural Comput
. 2025 Mar;
:1-41.
PMID: 40030137
The Wilson-Cowan model for metapopulation, a neural mass network model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective...
6.
Zhang X, Aravamudan A, Anagnostopoulos G
Neural Comput
. 2025 Mar;
:1-15.
PMID: 40030136
Temporal point processes are essential for modeling event dynamics in fields such as neuroscience and social media. The time rescaling theorem is commonly used to assess model fit by transforming...
7.
Friston K, Salvatori T, Isomura T, Tschantz A, Kiefer A, Verbelen T, et al.
Neural Comput
. 2025 Mar;
:1-35.
PMID: 40030135
Recent advances in theoretical biology suggest that key definitions of basal cognition and sentient behavior may arise as emergent properties of in vitro cell cultures and neuronal networks. Such neuronal...
8.
He Y, Lubchenko V
Neural Comput
. 2025 Mar;
:1-51.
PMID: 40030134
We construct a thermodynamic potential that can guide training of a generative model defined on a set of binary degrees of freedom. We argue that upon reduction in description, so...
9.
Zhu Z, Qi Y, Lu W, Wang Z, Cao L, Feng J
Neural Comput
. 2025 Jan;
37(3):481-521.
PMID: 39787430
Spiking neural networks (SNNs) have attracted significant interest in the development of brain-inspired computing systems due to their energy efficiency and similarities to biological information processing. In contrast to continuous-valued...
10.
Warr K, Hare J, Thomas D
Neural Comput
. 2025 Jan;
37(3):437-480.
PMID: 39787425
The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphic settings. One such algorithmic challenge is the ability to recall learned...